diff --git a/.github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml b/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
similarity index 78%
rename from .github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml
rename to .github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
index 6e77ae1ec..62bfbc96d 100644
--- a/.github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml
+++ b/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
@@ -16,15 +16,15 @@ jobs:
           submodules: true
       - name: Build And Deploy
         id: builddeploy
-        uses: Azure/static-web-apps-deploy@v0.0.1-preview
+        uses: Azure/static-web-apps-deploy@v1
         with:
-          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_JOLLY_SEA_0A877260F }}
+          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_WHITE_WATER_09EC41F0F }}
           repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments)
           action: "upload"
-          ###### Repository/Build Configurations - These values can be configured to match you app requirements. ######
+          ###### Repository/Build Configurations - These values can be configured to match your app requirements. ######
           # For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig
           app_location: "/quiz-app" # App source code path
-          api_location: "api" # Api source code path - optional
+          api_location: "" # Api source code path - optional
           output_location: "dist" # Built app content directory - optional
           ###### End of Repository/Build Configurations ######
 
@@ -35,7 +35,7 @@ jobs:
     steps:
       - name: Close Pull Request
         id: closepullrequest
-        uses: Azure/static-web-apps-deploy@v0.0.1-preview
+        uses: Azure/static-web-apps-deploy@v1
         with:
-          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_JOLLY_SEA_0A877260F }}
+          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_WHITE_WATER_09EC41F0F }}
           action: "close"
diff --git a/.gitignore b/.gitignore
index a80a15e32..51f47a5aa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,6 +33,8 @@ bld/
 
 # Visual Studio 2015/2017 cache/options directory
 .vs/
+# Visual Studio Code cache/options directory
+.vscode/
 # Uncomment if you have tasks that create the project's static files in wwwroot
 #wwwroot/
 
diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index e18a00361..7a645b6e9 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -4,7 +4,7 @@
 
 > 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -17,7 +17,7 @@ Welcome to this course on classical machine learning for beginners! Whether you'
 
 Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally.
 
-- **Configure your machine with these videos**. Learn more about how to set up your machine in this [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
+- **Configure your machine with these videos**. Use the following links to learn [how to install Python](https://youtu.be/CXZYvNRIAKM) in your system and [setup a text editor](https://youtu.be/EU8eayHWoZg) for development.
 - **Learn Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), a programming language useful for data scientists that we use in this course.
 - **Learn Node.js and JavaScript**. We also use JavaScript a few times in this course when building web apps, so you will need to have [node](https://nodejs.org) and [npm](https://www.npmjs.com/) installed, as well as [Visual Studio Code](https://code.visualstudio.com/) available for both Python and JavaScript development.
 - **Create a GitHub account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too 😊)
@@ -35,7 +35,7 @@ We live in a universe full of fascinating mysteries. Great scientists such as St
 
 A child's brain and senses perceive the facts of their surroundings and gradually learn the hidden patterns of life which help the child to craft logical rules to identify learned patterns. The learning process of the human brain makes humans the most sophisticated living creature of this world. Learning continuously by discovering hidden patterns and then innovating on those patterns enables us to make ourselves better and better throughout our lifetime. This learning capacity and evolving capability is related to a concept called [brain plasticity](https://www.simplypsychology.org/brain-plasticity.html). Superficially, we can draw some motivational similarities between the learning process of the human brain and the concepts of machine learning.
 
-The [human brain](https://www.livescience.com/29365-human-brain.html) perceives things from the real world, processes the perceived information, makes rational decisions, and performs certain actions based on circumstances. This is what we called behaving intelligently. When we program a facsimile of the intelligent behavioral process to a machine, it is called artificial intelligence (AI). 
+The [human brain](https://www.livescience.com/29365-human-brain.html) perceives things from the real world, processes the perceived information, makes rational decisions, and performs certain actions based on circumstances. This is what we called behaving intelligently. When we program a facsimile of the intelligent behavioral process to a machine, it is called artificial intelligence (AI).
 
 Although the terms can be confused, machine learning (ML) is an important subset of artificial intelligence. **ML is concerned with using specialized algorithms to uncover meaningful information and find hidden patterns from perceived data to corroborate the rational decision-making process**.
 
@@ -45,7 +45,7 @@ Although the terms can be confused, machine learning (ML) is an important subset
 
 ## What you will learn in this course
 
-In this curriculum, we are going to cover only the core concepts of machine learning that a beginner must know. We cover what we call 'classical machine learning' primarily using Scikit-learn, an excellent library many students use to learn the basics.  To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable, and so we would like to offer it here. 
+In this curriculum, we are going to cover only the core concepts of machine learning that a beginner must know. We cover what we call 'classical machine learning' primarily using Scikit-learn, an excellent library many students use to learn the basics.  To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable, and so we would like to offer it here.
 
 In this course you will learn:
 
@@ -64,7 +64,7 @@ In this course you will learn:
 - deep learning
 - neural networks
 - AI
-  
+
 To make for a better learning experience, we will avoid the complexities of neural networks, 'deep learning' - many-layered model-building using neural networks - and AI, which we will discuss in a different curriculum. We also will offer a forthcoming data science curriculum to focus on that aspect of this larger field.
 ## Why study machine learning?
 
@@ -96,12 +96,14 @@ In the near future, understanding the basics of machine learning is going to be
 
 Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
 ## Review & Self Study
 
 To learn more about how you can work with ML algorithms in the cloud, follow this [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
 
+Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) about the basics of ML.
+
 ## Assignment
 
 [Get up and running](assignment.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/README.es.md b/1-Introduction/1-intro-to-ML/translations/README.es.md
index e69de29bb..15288de91 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.es.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.es.md
@@ -0,0 +1,113 @@
+# Introducción al machine learning
+
+[![ML, IA, deep learning - ¿Cuál es la diferencia?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, IA, deep learning - ¿Cuál es la diferencia?")
+
+> 🎥 Haz clic en la imagen de arriba para ver un video donde se discuten las diferencias entre el machine learning, la inteligencia artificial, y el deep learning.
+
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+
+### Introducción
+
+Te damos la bienvenida a este curso acerca del machine learning (ML) clásico para principiantes! Así se trate de tu primera incursión en este tema, o cuentes con amplia experiencia en el ML y busques refrescar tus conocimientos en un área específica, ¡nos alegramos de que te nos unas! Queremos crear un punto de lanzamiento amigable para tus estudios de ML y nos encantaría evaluar, responder, e incorporar tu [retroalimentación](https://github.com/microsoft/ML-For-Beginners/discussions).
+
+[![Introducción al ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introducción al ML")
+
+> Haz clic en la imagen de arriba para ver el video: John Guttag del MIT presenta el machine learning
+
+### Empezando con el machine learning
+
+Antes de comenzar con este currículum, debes tener tu computadora configurada y lista para ejecutar los notebooks localmente.
+
+- **Configura tu equipo con estos videos**. Aprende más acerca de como configurar tu equipo con [estos videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
+- **Aprende Python**. También se recomienda que tengas un entendimiento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un lenguaje de programación útil para practicantes de la ciencia de datos, y que se utiliza en este curso.
+- **Aprende Node.js y JavaScript**. También usamos JavaScript unas cuantas veces en este curso cuando creamos aplicaciones web, así que necesitarás tener [node](https://nodejs.org) y [npm](https://www.npmjs.com/) instalados, así como [Visual Studio Code](https://code.visualstudio.com/) listo para el desarrollo con Python y JavaScript.
+- **Crea una cuenta de GitHub**. Como nos encontraste aquí en [GitHub](https://github.com), puede que ya tengas una cuenta, pero si no, créate una y después haz un fork de este curriculum para usarlo en tu computadora personal. (Siéntete en libertad de regalarnos una estrella, también 😊)
+- **Explora Scikit-learn**. Familiarízate con [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un juego de bibliotecas de ML que referenciamos en estas lecciones.
+
+### ¿Qué es el machine learning?
+
+El término "machine learning" es uno de los términos más frecuentemente usados y populares hoy en día. Es muy probable que hayas escuchado este término al menos una vez si tienes algún tipo de familiaridad con la tecnología, no importa el sector en que trabajes. Aún así, las mecánicas del machine learning son un misterio para la mayoría de la gente. Para un principiante en machine learning, el tema puede sentirse intimidante. Es por esto que es importante entender lo que realmente es el machine learning, y aprender sobre el tema poco a poco, a través de ejemplos prácticos.
+
+![curva de interés en ml](../images/hype.png)
+
+> Google Trends nos muestra la más reciente "curva de interés" para el término "machine learning"
+
+Vivimos en un universo lleno de misterios fascinantes. Grandes científicos como Stephen Hawking, Albert Einstein, y muchos más han dedicado sus vidas a la búsqueda de información significativa que revela los misterios del mundo a nuestro alrededor. Esta es la condición humana del aprendizaje: un niño humano aprende cosas nuevas y descubre la estructura de su mundo año con año conforme se convierten en adultos.
+
+El cerebro de un niño y sus sentidos perciben sus alrededores y van aprendiendo gradualmente los patrones escondidos de la vida, lo que le ayuda al niño a crear reglas lógicas para identificar los patrones aprendidos. El proceso de aprendizaje del cerebro humano nos vuelve las criaturas más sofisticadas del planeta. Aprender de forma continua al descubrir patrones ocultos e innovar sobre esos patrones nos permite seguir mejorando a lo largo de nuestras vidas. Esta capacidad de aprendizaje y la capacidad de evolución están relacionadas a un concepto llamado [plasticidad cerebral o neuroplasticidad](https://www.simplypsychology.org/brain-plasticity.html). Podemos trazar algunas similitudes superficiales en cuanto a la motivación entre el proceso de aprendizaje del cerebro humano y los conceptos de machine learning.
+
+El [cerebro humano](https://www.livescience.com/29365-human-brain.html) percibe cosas del mundo real, procesa la información percibida, toma decisiones racionales, y realiza ciertas acciones basadas en las circunstancias. Esto es a lo que se le conoce como el comportamiento inteligente. Cuando programamos un facsímil (copia) del proceso del comportamiento inteligente, se le llama inteligencia artificial (IA).
+
+Aunque los términos se suelen confundir, machine learning (ML) es un subconjunto importante de la inteligencia artificial. **El objetivo del ML es utilizar algoritmos especializados para descubrir información significativa y encontrar patrones ocultos de los datos percibidos para corroborar el proceso relacional de la toma de decisiones**.
+
+![IA, ML, deep learning, ciencia de los datos](../images/ai-ml-ds.png)
+
+> El diagrama muestra la relación entre IA, ML, deep learning y la ciencia de los datos. Infografía hecha por [Jen Looper](https://twitter.com/jenlooper) inspirada en [esta gráfica](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining).
+
+## Lo que aprenderás en el curso
+
+En este currículum, vamos a cubrir solo los conceptos clave de machine learning que un principiante debería conocer. Cubrimos algo a lo que le llamamos "machine learning clásico" usando principalmente Scikit-learn, una biblioteca excelente que muchos estudiantes utilizan para aprender las bases. Para entender conteptos más amplios de la inteligencia artificial o deep learning, es indispensable tener un fuerte conocimiento de los fundamentos, y eso es lo que nos gustaría ofrecerte aquí.
+
+En este curso aprenderás:
+
+- conceptos clave del machine learning
+- la historia de ML
+- la justicia y el ML
+- técnicas de regresión en ML
+- técnicas de clasificación en ML
+- técnicas de agrupamiento en ML
+- técnicas de procesamiento del lenguaje natural en ML
+- técnicas de previsión de series temporales en ML
+- reforzamiento del aprendizaje
+- ML aplicada al mundo real
+
+## Lo que no cubriremos
+
+- deep learning
+- redes neuronales
+- inteligencia artificial (IA)
+
+Para tener una mejor experiencia de aprendizaje, vamos a evitar las complejidades de las redes neuronales, "deep learning" (construcción de modelos de muchas capas utilizando las redes neuronales) e inteligencia artificial, que se discutirá en un currículum diferente. En un futuro también ofreceremos un currículum acerca de la ciencia de datos para enfocarnos en ese aspecto de este campo.
+
+## ¿Por qué estudiar machine learning?
+
+Machine learning, desde una perspectiva de los sistemas, se define como la creación de sistemas automáticos que pueden aprender patrones ocultos a partir de datos para ayudar en tomar decisiones inteligentes.
+
+Esta motivación está algo inspirada por como el cerebro humano aprende ciertas cosas basadas en los datos que percibe en el mundo real.
+
+✅ Piensa por un minuto en porqué querría un negocio intentar implementar estrategias de machine learning vs. programar un motor basado en reglas.
+
+### Aplicaciones del machine learning
+
+Las aplicaciones del machine learning hoy en día están casi en todas partes, y son tan ubicuas como los datos que fluyen alrededor de nuestras sociedades, generados por nuestros teléfonos inteligentes, dispositivos conectados a internet, y otros sistemas. Considerando el inmenso potencial de los algoritmos estado del arte de machine learning, investigadores han estado explorando su capacidad de resolver problemas multidimensionales y multidisciplinarios de la vida real con resultados muy positivos.
+
+**Tú puedes utilizar machine learning de muchas formas**:
+
+- Para predecir la probabilidad de enfermedad a partir del historial médico o reportes de un paciente.
+- Para aprovechar datos del clima y predecir eventos climatológicos.
+- Para entender la intención de un texto.
+- Para detectar noticias falsas y evitar la propagación de propaganda.
+
+Finanzas, economía, ciencias de la Tierra, exploración espacial, ingeniería biomédica, ciencia cognitiva, e incluso campos en las humanidades han adaptado machine learning para solucionar los problemas más arduos y pesados en cuanto al procesamiento de datos.
+
+Machine learning automatiza el proceso del descubrimiento de patrones al encontrar perspectivas significativas desde el mundo real o datos generados. Machine learning ha demostrado ser muy valioso en las aplicaciones del sector salud, negocios y finanzas, entre otros.
+
+En el futuro próximo, entender las bases de machine learning va a ser una necesidad para la gente en cualquier sector debido a su alta adopción.
+
+---
+
+## 🚀 Desafío
+
+Dibuja, en papel o usando una aplicación como [Excalidraw](https://excalidraw.com/), como tú entiendes las diferencias entre inteligencia artificial, ML, deep learning, y la ciencia de datos. Agrega algunas ideas o problemas que cada una de estas técnicas son buenas en resolver.
+
+## [Cuestionario después de la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+
+## Revisión y autoestudio
+
+Para aprender más sobre como puedes trabajar con algoritmos de ML en la nube, sigue esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
+
+Toma esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) sobre las bases de ML.
+
+## Asignación
+
+[Ponte en marcha](assignment.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
new file mode 100644
index 000000000..907981606
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -0,0 +1,109 @@
+# Introduction au machine learning
+
+[![ML, AI, deep learning - Quelle est la différence ?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
+
+> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
+
+## [Quiz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=fr)
+
+### Introduction
+
+Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vous soyez complètement nouveau sur ce sujet ou que vous soyez un professionnel du ML expérimenté cherchant à peaufiner vos connaissances, nous sommes heureux de vous avoir avec nous ! Nous voulons créer un tremplin chaleureux pour vos études en ML et serions ravis d'évaluer, de répondre et d'apprendre de vos retours d'[expériences](https://github.com/microsoft/ML-For-Beginners/discussions).
+
+[![Introduction au ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
+
+> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo: John Guttag du MIT introduit le machine learning
+### Débuter avec le machine learning
+
+Avant de commencer avec ce cours, vous aurez besoin d'un ordinateur configuré et prêt à faire tourner des notebooks (jupyter) localement.
+
+- **Configurer votre ordinateur avec ces vidéos**. Apprendre comment configurer votre ordinateur avec cette [série de vidéos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
+- **Apprendre Python**. Il est aussi recommandé d'avoir une connaissance basique de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un langage de programmaton utile pour les data scientist que nous utilisons tout au long de ce cours.
+- **Apprendre Node.js et Javascript**. Nous utilisons aussi Javascript par moment dans ce cours afin de construire des applications WEB, vous aurez donc besoin de [node](https://nodejs.org) et [npm](https://www.npmjs.com/) installé, ainsi que de [Visual Studio Code](https://code.visualstudio.com/) pour développer en Python et Javascript.
+- **Créer un compte GitHub**. Comme vous nous avez trouvé sur [GitHub](https://github.com), vous y avez sûrement un compte, mais si non, créez en un et répliquez ce cours afin de l'utiliser à votre grés. (N'oublier pas de nous donner une étoile aussi 😊)
+- **Explorer Scikit-learn**. Familiariser vous avec [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un ensemble de librairies ML que nous mentionnons dans nos leçons.
+
+### Qu'est-ce que le machine learning
+
+Le terme `machine learning` est un des mots les plus populaire et le plus utilisé ces derniers temps. Il y a une probabilité accrue que vous l'ayez entendu au moins une fois si vous avez une appétence pour la technologie indépendamment du domaine dans lequel vous travaillez. Le fonctionnement du machine learning, cependant, reste un mystère pour la plupart des personnes. Pour un débutant en machine learning, le sujet peut nous submerger. Ainsi, il est important de comprendre ce qu'est le machine learning et de l'apprendre petit à petit au travers d'exemples pratiques.   
+
+![ml hype curve](../images/hype.png)
+
+> Google Trends montre la récente 'courbe de popularité' pour le mot 'machine learning'
+
+Nous vivons dans un univers rempli de mystères fascinants. De grands scientifiques comme Stephen Hawking, Albert Einstein et pleins d'autres ont dévoués leur vie à la recherche d'informations utiles afin de dévoiler les mystères qui nous entourent. C'est la condition humaine pour apprendre : un enfant apprend de nouvelles choses et découvre la structure du monde année après année jusqu'à qu'ils deviennent adultes.
+
+Le cerveau d'un enfant et ses sens perçoivent l'environnement qui les entourent et apprennent graduellement des schémas non observés de la vie qui vont l'aider à fabriquer des règles logiques afin d'identifier les schémas appris. Le processus d'apprentissage du cerveau humain est ce que rend les hommes comme la créature la plus sophistiquée du monde vivant. Apprendre continuellement par la découverte de schémas non observés et ensuite innover sur ces schémas nous permet de nous améliorer tout au long de notre vie. Cette capacité d'apprendre et d'évoluer est liée au concept de [plasticité neuronale](https://www.simplypsychology.org/brain-plasticity.html), nous pouvons tirer quelques motivations similaires entre le processus d'apprentissage du cerveau humain et le concept de machine learning.
+
+Le [cerveau humain](https://www.livescience.com/29365-human-brain.html) perçoit des choses du monde réel, assimile les informations perçues, fait des décisions rationnelles et entreprend certaines actions selon le contexte. C'est ce que l'on appelle se comporter intelligemment. Lorsque nous programmons une reproduction du processus de ce comportement à une machine, c'est ce que l'on appelle intelligence artificielle (IA).
+
+Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas non observés depuis des données perçues pour corroborer un processus de décision rationnel**.
+
+![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
+
+> Un diagramme montrant les relations entre AI, ML, deep learning et data science. Infographie par [Jen Looper](https://twitter.com/jenlooper) et inspiré par [ce graphique](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
+
+## Ce que vous allez apprendre dans ce cours
+
+Dans ce cours, nous allons nous concentrer sur les concepts clés du machine learning qu'un débutant se doit de connaître. Nous parlerons de ce que l'on appelle le 'machine learning classique' en utilisant principalement Scikit-learn, une excellente librairie que beaucoup d'étudiants utilisent afin d'apprendre les bases. Afin de comprendre les concepts plus larges de l'intelligence artificielle ou du deep learning, une profonde connaissance en machine learning est indispensable, et c'est ce que nous aimerions fournir ici.
+
+Dans ce cours, vous allez apprendre :
+
+- Les concepts clés du machine learning
+- L'histoire du ML
+- ML et équité (fairness)
+- Les techniques de régression ML
+- Les techniques de classification ML
+- Les techniques de regroupement (clustering) ML
+- Les techniques du traitement automatique des langues (NLP) ML
+- Les techniques de prédictions à partir de séries chronologiques ML
+- Apprentissage renforcé
+- D'applications réels du ML
+
+## Ce que nous ne couvrirons pas
+
+- Deep learning
+- Neural networks
+- IA
+
+Afin d'avoir la meilleur expérience d'apprentissage, nous éviterons les complexités des réseaux neuronaux, du 'deep learning' (construire un modèle utilisant plusieurs couches de réseaux neuronaux) et IA, dont nous parlerons dans un cours différent. Nous offirons aussi un cours à venir sur la data science pour concentrer sur cet aspect de champs très large.
+
+## Pourquoi etudier le machine learning ?
+
+Le machine learning, depuis une perspective systémique, est défini comme la création de systèmes automatiques pouvant apprendre des schémas non observés depuis des données afin d'aider à prendre des décisions intelligentes.
+
+Ce but est faiblement inspiré de la manière dont le cerveau humain apprend certaines choses depuis les données qu'il perçoit du monde extérieur.
+
+✅ Penser une minute aux raisons qu'une entreprise aurait d'essayer d'utiliser des stratégies de machine learning au lieu de créer des règles codés en dur.
+
+### Les applications du machine learning
+
+Les applications du machine learning sont maintenant pratiquement partout, et sont aussi omniprésentes que les données qui circulent autour de notre société (générés par nos smartphones, appareils connectés ou autres systèmes). En prenant en considération l'immense potentiel des algorithmes dernier cri de machine learning, les chercheurs ont pu exploités leurs capacités afin de résoudre des problèmes multidimensionnels et interdisciplinaires de la vie avec d'important retours positifs 
+
+**Vous pouvez utiliser le machine learning de plusieurs manières** :
+
+- Afin de prédire la possibilité d'avoir une maladie à partir des données médicales d'un patient.
+- Pour tirer parti des données météorologiques afin de prédire les événements météorologiques.
+- Afin de comprendre le sentiment d'un texte.
+- Afin de détecter les fake news pour stopper la propagation de la propagande.
+
+La finance, l'économie, les sciences de la terre, l'exploration spatiale, le génie biomédical, les sciences cognitives et même les domaines des sciences humaines ont adapté le machine learning pour résoudre les problèmes ardus et lourds de traitement des données dans leur domaine respectif.
+
+Le machine learning automatise le processus de découverte de modèles en trouvant des informations significatives à partir de données réelles ou générées. Il s'est avéré très utile dans les applications commerciales, de santé et financières, entre autres.
+
+Dans un avenir proche, comprendre les bases du machine learning sera indispensable pour les personnes de tous les domaines en raison de son adoption généralisée.
+
+---
+## 🚀 Challenge
+
+Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
+
+## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=fr)
+
+## Révision et auto-apprentissage
+
+Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
+
+## Devoir
+
+[Être opérationnel](assignment.fr.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
new file mode 100644
index 000000000..69a9157b5
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -0,0 +1,107 @@
+# Pengantar Machine Learning
+
+[![ML, AI, deep learning - Apa perbedaannya?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - Apa perbedaannya?")
+
+> 🎥 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
+
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+
+### Pengantar
+
+Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang masih benar-benar baru, atau seorang praktisi ML berpengalaman yang ingin meningkatkan kemampuan kamu, kami senang kamu ikut bersama kami! Kami ingin membuat sebuah titik mulai yang ramah untuk pembelajaran ML kamu dan akan sangat senang untuk mengevaluasi, merespon, dan memasukkan [umpan balik](https://github.com/microsoft/ML-For-Beginners/discussions) kamu.
+
+[![Pengantar Machine Learning](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Pengantar Machine Learning")
+
+> 🎥 Klik gambar diatas untuk menonton video: John Guttag dari MIT yang memberikan pengantar Machine Learning.
+### Memulai Machine Learning
+
+Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersiapkan untuk menjalankan *notebook* secara lokal.
+
+- **Konfigurasi komputer kamu dengan video ini**. Pelajari bagaimana menyiapkan komputer kamu dalam [video-video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) ini.
+- **Belajar Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), sebuah bahasa pemrograman yang digunakan oleh data scientist yang juga akan kita gunakan dalam pelajaran ini. 
+- **Belajar Node.js dan JavaScript**. Kita juga menggunakan JavaScript beberapa kali dalam pelajaran ini ketika membangun aplikasi web, jadi kamu perlu menginstal [node](https://nodejs.org) dan [npm](https://www.npmjs.com/), serta [Visual Studio Code](https://code.visualstudio.com/) yang tersedia untuk pengembangan Python dan JavaScript.
+- **Buat akun GitHub**. Karena kamu menemukan kami di [GitHub](https://github.com), kamu mungkin sudah punya akun, tapi jika belum, silakan buat akun baru kemudian *fork* kurikulum ini untuk kamu pergunakan sendiri. (Jangan ragu untuk memberikan kami bintang juga 😊)
+- **Jelajahi Scikit-learn**. Buat diri kamu familiar dengan [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html), seperangkat *library* ML yang kita acu dalam pelajaran-pelajaran ini.
+
+### Apa itu Machine Learning?
+
+Istilah 'Machine Learning' merupakan salah satu istilah yang paling populer dan paling sering digunakan saat ini. Ada kemungkinan kamu pernah mendengar istilah ini paling tidak sekali jika kamu familiar dengan teknologi. Tetapi untuk mekanisme Machine Learning sendiri, merupakan sebuah misteri bagi sebagian besar orang. Karena itu, penting untuk memahami sebenarnya apa itu Machine Learning, dan mempelajarinya langkah demi langkah melalui contoh praktis.
+
+![kurva tren ml](../images/hype.png)
+
+> Google Trends memperlihatkan 'kurva tren' dari istilah 'Machine Learning' belakangan ini.
+
+Kita hidup di sebuah alam semesta yang penuh dengan misteri yang menarik. Ilmuwan-ilmuwan besar seperti Stephen Hawking, Albert Einstein, dan banyak lagi telah mengabdikan hidup mereka untuk mencari informasi yang berarti yang mengungkap misteri dari dunia disekitar kita. Ini adalah kondisi belajar manusia: seorang anak manusia belajar hal-hal baru dan mengungkap struktur dari dunianya tahun demi tahun saat mereka tumbuh dewasa. 
+
+Otak dan indera seorang anak memahami fakta-fakta di sekitarnya dan secara bertahap mempelajari pola-pola kehidupan yang tersembunyi yang membantu anak untuk menyusun aturan-aturan logis untuk mengidentifikasi pola-pola yang dipelajari. Proses pembelajaran otak manusia ini menjadikan manusia sebagai makhluk hidup paling canggih di dunia ini. Belajar terus menerus dengan menemukan pola-pola tersembunyi dan kemudian berinovasi pada pola-pola itu memungkinkan kita untuk terus menjadikan diri kita lebih baik sepanjang hidup. Kapasitas belajar dan kemampuan berkembang ini terkait dengan konsep yang disebut dengan *[brain plasticity](https://www.simplypsychology.org/brain-plasticity.html)*. Secara sempit, kita dapat menarik beberapa kesamaan motivasi antara proses pembelajaran otak manusia dan konsep Machine Learning.
+
+[Otak manusia](https://www.livescience.com/29365-human-brain.html) menerima banyak hal dari dunia nyata, memproses informasi yang diterima, membuat keputusan rasional, dan melakukan aksi-aksi tertentu berdasarkan keadaan. Inilah yang kita sebut dengan berperilaku cerdas. Ketika kita memprogram sebuah salinan dari proses perilaku cerdas ke sebuah mesin, ini dinamakan kecerdasan buatan atau Artificial Intelligence (AI).
+
+Meskipun istilah-stilahnya bisa membingungkan, Machine Learning (ML) adalah bagian penting dari Artificial Intelligence. **ML berkaitan dengan menggunakan algoritma-algoritma terspesialisasi untuk mengungkap informasi yang berarti dan mencari pola-pola tersembunyi dari data yang diterima untuk mendukung proses pembuatan keputusan rasional**.
+
+![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
+
+> Sebuah diagram yang memperlihatkan hubungan antara AI, ML, Deep Learning, dan Data Science. Infografis oleh [Jen Looper](https://twitter.com/jenlooper) terinspirasi dari [infografis ini](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
+
+## Apa yang akan kamu pelajari
+
+Dalam kurikulum ini, kita hanya akan membahas konsep inti dari Machine Learning yang harus diketahui oleh seorang pemula. Kita membahas apa yang kami sebut sebagai 'Machine Learning klasik' utamanya menggunakan Scikit-learn, sebuah *library* luar biasa yang banyak digunakan para siswa untuk belajar dasarnya. Untuk memahami konsep Artificial Intelligence atau Deep Learning yang lebih luas, pengetahuan dasar yang kuat tentang Machine Learning sangat diperlukan, itulah yang ingin kami tawarkan di sini. 
+
+Kamu akan belajar:
+
+- Konsep inti ML
+- Sejarah dari ML
+- Keadilan dan ML
+- Teknik regresi ML
+- Teknik klasifikasi ML
+- Teknik *clustering* ML
+- Teknik *natural language processing* ML
+- Teknik *time series forecasting* ML
+- *Reinforcement learning*
+- Penerapan nyata dari ML
+## Yang tidak akan kita bahas
+
+- *deep learning*
+- *neural networks*
+- AI
+
+Untuk membuat pengalaman belajar yang lebih baik, kita akan menghindari kerumitan dari *neural network*, *deep learning* - membangun *many-layered model* menggunakan *neural network* - dan AI, yang mana akan kita bahas dalam kurikulum yang berbeda. Kami juga akan menawarkan kurikulum *data science* yang berfokus pada aspek bidang tersebut. 
+## Kenapa belajar Machine Learning?
+
+Machine Learning, dari perspektif sistem, didefinisikan sebagai pembuatan sistem otomatis yang dapat mempelajari pola-pola tersembunyi dari data untuk membantu membuat keputusan cerdas. 
+
+Motivasi ini secara bebas terinspirasi dari bagaimana otak manusia mempelajari hal-hal tertentu berdasarkan data yang diterimanya dari dunia luar. 
+
+✅ Pikirkan sejenak mengapa sebuah bisnis ingin mencoba menggunakan strategi Machine Learning dibandingkan membuat sebuah mesin berbasis aturan yang tertanam (*hard-coded*). 
+
+### Penerapan Machine Learning
+
+Penerapan Machine Learning saat ini hampir ada di mana-mana, seperti data yang mengalir di sekitar kita, yang dihasilkan oleh ponsel pintar, perangkat yang terhubung, dan sistem lainnya. Mempertimbangkan potensi besar dari algoritma Machine Learning terkini, para peneliti telah mengeksplorasi kemampuan Machine Learning untuk memecahkan masalah kehidupan nyata multi-dimensi dan multi-disiplin dengan hasil positif yang luar biasa. 
+
+**Kamu bisa menggunakan Machine Learning dalam banyak hal**:
+
+- Untuk memprediksi kemungkinan penyakit berdasarkan riwayat atau laporan medis pasien.
+- Untuk memanfaatkan data cuaca untuk memprediksi peristiwa cuaca.
+- Untuk memahami sentimen sebuah teks.
+- Untuk mendeteksi berita palsu untuk menghentikan penyebaran propaganda.
+
+Keuangan, ekonomi, geosains, eksplorasi ruang angkasa, teknik biomedis, ilmu kognitif, dan bahkan bidang humaniora telah mengadaptasi Machine Learning untuk memecahkan masalah sulit pemrosesan data di bidang mereka. 
+
+Machine Learning mengotomatiskan proses penemuan pola dengan menemukan wawasan yang berarti dari dunia nyata atau dari data yang dihasilkan. Machine Learning terbukti sangat berharga dalam penerapannya di berbagai bidang, diantaranya adalah bidang bisnis, kesehatan, dan keuangan.
+
+Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keharusan bagi orang-orang dari bidang apa pun karena adopsinya yang luas. 
+
+---
+## 🚀 Tantangan
+
+Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik.
+
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+
+## Ulasan & Belajar Mandiri
+
+Untuk mempelajari lebih lanjut tentang bagaimana kamu dapat menggunakan algoritma ML di cloud, ikuti [Jalur Belajar](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa) ini. 
+
+## Tugas
+
+[Persiapan](assignment.id.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md
index 7d9fe43ae..eab0f4907 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.it.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.it.md
@@ -4,7 +4,7 @@
 
 > 🎥 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/?loc=it)
 
 ### Introduzione
 
@@ -97,7 +97,7 @@ Nel prossimo futuro, comprendere le basi di machine learning sarà un must per l
 
 Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche è in grado di risolvere.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/?loc=it)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index aded0f7e6..b88738d03 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -4,7 +4,7 @@
 
 > 🎥 上の画像をクリックすると、機械学習、AI、深層学習の違いについて説明した動画が表示されます。
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ja)
 
 ### イントロダクション
 
@@ -94,12 +94,12 @@
 ## 🚀 Challenge
 AI、ML、深層学習、データサイエンスの違いについて理解していることを、紙や[Excalidraw](https://excalidraw.com/)などのオンラインアプリを使ってスケッチしてください。また、それぞれの技術が得意とする問題のアイデアを加えてみてください。
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ja)
 
 ## 振り返りと自習
 
-クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)に従ってください。.
+クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)に従ってください。
 
 ## 課題
 
-[起動し、実行してください。](assignment.md)
+[稼働させる](assignment.ja.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ko.md b/1-Introduction/1-intro-to-ML/translations/README.ko.md
new file mode 100644
index 000000000..478192bd1
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/README.ko.md
@@ -0,0 +1,110 @@
+# 머신러닝 소개
+
+[![ML, AI, deep learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
+
+> 🎥 머신러닝, AI 그리고 딥러닝의 차이를 설명하는 영상을 보려면 위 이미지를 클릭합니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+
+### 소개
+
+입문자를 위한 classical 머신러닝 코스에 오신 것을 환영합니다! 이 토픽에 완벽하게 새로 접해보거나, 한 분야에 완벽해지고 싶어하는 ML 실무자도 저희와 함께하게 되면 좋습니다! ML 연구를 위한 친숙한 시작점을 만들고 싶고, 당신의 [feedback](https://github.com/microsoft/ML-For-Beginners/discussions)을 평가, 응답하고 반영하겠습니다.
+
+[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
+
+> 🎥 동영상을 보려면 위 이미지 클릭: MIT의 John Guttag가 머신러닝을 소개합니다.
+### 머신러닝 시작하기
+
+이 커리큘럼을 시작하기 전, 컴퓨터를 세팅하고 노트북을 로컬에서 실행할 수 있게 준비해야 합니다.
+
+- **이 영상으로 컴퓨터 세팅하기**. [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)에서 컴퓨터를 세팅하는 방법에 대하여 자세히 알아봅니다.
+- **Python 배우기**. 이 코스에서 사용할 데이터 사이언티스트에게 유용한 프로그래밍 언어인 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)에 대한 기본적인 이해를 해야 좋습니다.
+- **Node.js 와 JavaScript 배우기**. 이 코스에서 웹앱을 빌드할 때 몇 번 JavaScript를 사용하므로, [node](https://nodejs.org) 와 [npm](https://www.npmjs.com/)을 설치해야 합니다, Python 과 JavaScript를 개발하며 모두 쓸 수 있는 [Visual Studio Code](https://code.visualstudio.com/)도 있습니다.
+- **GitHub 계정 만들기**. [GitHub](https://github.com)에서 찾았으므로, 이미 계정이 있을 수 있습니다, 혹시 없다면, 계정을 만든 뒤에 이 커리큘럼을 포크해서 직접 쓸 수 있습니다. (star 주셔도 됩니다 😊)
+- **Scikit-learn 찾아보기**. 이 강의에서 참조하고 있는 ML 라이브러리 셋인 [Scikit-learn](https://scikit-learn.org/stable/user_guide.html)을 숙지합니다.
+
+### 머신러닝은 무엇인가요?
+
+'머신러닝'은 최근 가장 인기있고 자주 언급되는 용어입니다. 어떤 분야든 기술에 어느 정도 익숙해지면 이러한 용어를 한 번즈음 들어본 적이 있었을 것입니다, 그러나, 머신러닝의 구조는 대부분의 사람들에겐 미스테리입니다. 머신러닝 입문자에겐 주제가 때때로 숨막힐 수 있습니다, 그래서 머신러닝이 실제로 어떤 지 이해하고, 실제 적용된 예시로, 단계별 학습을 진행하는 것이 중요합니다.
+
+![ml hype curve](../images/hype.png)
+
+> Google Trends는 '머신러닝' 용어의 최근 'hype curve'로 보여줍니다
+
+우리는 매우 신비한 우주에 살고 있습니다. Stephen Hawking, Albert Einstein과 같은 위대한 과학자들은 주변 세계의 신비를 밝혀낼 의미있는 정보를 찾는 데 일생을 바쳤습니다. 이건 사람의 학습 조건입니다: 아이는 자라면서 해마다 새로운 것을 배우고 세계 구조를 발견합니다.
+
+어린이의 뇌와 센스는 주변의 사실을 인식하고 점차 숨겨진 생활 패턴을 학습하여 학습된 패턴을 식별할 논리 규칙을 만드는 데 도움을 줍니다. 뇌의 논리 프로세스는 사람을 가장 정교한 생명체로 만듭니다. 숨겨진 패턴을 발견하고 개선하여 지속해서 학습하면 평생 발전할 수 있습니다. 이런 학습 능력과 진화력은 [brain plasticity](https://www.simplypsychology.org/brain-plasticity.html)로 불리는 컨셉과 관련있습니다. 표면적으로, 뇌의 학습 과정과 머신러닝의 개념 사이에 motivational similarities를 그릴 수 있습니다.
+
+[human brain](https://www.livescience.com/29365-human-brain.html)은 실제 세계에서 사물을 인식하고, 인식된 정보를 처리하며, 합리적인 결정과, 상황에 따른 행동을 합니다. 이걸 지능적으로 행동한다고 합니다. 기계에 지능적인 행동 복사본를 프로그래밍할 때, 인공 지능 (AI)라고 부릅니다.
+
+용어가 햇갈릴 수 있지만, 머신러닝(ML)은 중요한 인공 지능의 서브넷입니다. **ML은 특수한 알고리즘을 써서 의미있는 정보를 찾고 인식한 데이터에서 숨겨진 패턴을 찾아 합리적으로 판단할 프로세스를 확실하게 수행하는 것에 관심있습니다**.
+
+![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
+
+> AI, ML, 딥러닝, 그리고 데이터 사이언티스트 간의 관계를 보여주는 다이어그램. [this graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)에서 영감을 받은 [Jen Looper](https://twitter.com/jenlooper)의 인포그래픽
+
+## 이 코스에서 배우는 것
+
+이 커리큘럼에서, 입문자가 반드시 알아야 할 머신러닝의 핵심적인 개념만 다룰 것입니다. 많은 학생들이 기초를 배우기 위해 사용하는 훌륭한 라이브러리인, Scikit-learn으로 'classical machine learning'이라고 부르는 것을 다룹니다. 인공 지능 또는 딥러닝의 대략적인 개념을 이해하려면, 머신러닝에 대한 강력한 기초 지식이 꼭 필요하므로, 여기에서 제공하고자 합니다.
+
+이 코스에서 다음 사항을 배웁니다:
+
+- 머신러닝의 핵심 컨셉
+- ML 의 역사
+- ML 과 공정성
+- regression ML 기술
+- classification ML 기술
+- clustering ML 기술
+- natural language processing ML 기술
+- time series forecasting ML 기술
+- 강화 학습
+- real-world 애플리케이션 for ML
+
+## 다루지 않는 것
+
+- 딥러닝
+- 신경망
+- AI
+  
+더 좋은 학습 환경을 만들기 위해서, 신경망, '딥러닝' - many-layered model-building using neural networks - 과 AI의 복잡도를 피할 것이며, 다른 커리큘럼에서 논의할 것입니다. 또한 더 큰 필드에 초점을 맞추기 위하여 향후 데이터 사이언스 커리큘럼을 제공할 예정입니다. 
+## 왜 머신러닝을 배우나요?
+
+시스템 관점에서 보는 머신러닝은, 지능적인 결정하도록 데이터에서 숨겨진 패턴을 학습할 수 있는 자동화 시스템 생성으로 정의합니다.
+
+동기 부여는 뇌가 다른 세계에서 보는 데이터를 기반으로 특정한 무언가들을 학습하는 방식에서 살짝 영감을 받았습니다.
+
+✅  비지니스에서 머신러닝 전략 대신 하드-코딩된 룰-베이스 엔진을 만드려는 이유를 잠시 생각해봅시다.
+
+### 머신러닝의 애플리케이션
+
+머신러닝의 애플리케이션은 이제 거의 모든 곳에서, 스마트 폰, 연결된 기기, 그리고 다른 시스템에 의하여 생성된 주변의 흐르는 데이터만큼 어디에나 존재합니다. 첨단 머신러닝 알고리즘의 큰 잠재력을 고려한, 연구원들은 긍정적인 결과로 multi-dimensional과 multi-disciplinary적인 실-생활 문제를 해결하는 능력을 찾고 있습니다.
+
+**다양한 방식으로 머신러닝을 사용할 수 있습니다**:
+
+- 환자의 병력이나 보고서를 기반으로 질병 가능성을 예측합니다.
+- 날씨 데이터로 계절 이벤트를 예측합니다.
+- 문장의 감정을 이해합니다.
+- 가짜 뉴스를 감지하고 선동을 막습니다.
+
+금융, 경제학, 지구 과학, 우주 탐험, 생물 공학, 인지 과학, 그리고 인문학까지 머신러닝을 적용하여 힘들고, 데이터 처리가 버거운 이슈를 해결했습니다.
+
+머신러닝은 실제-환경이거나 생성된 데이터에서 의미를 찾아 패턴-발견하는 프로세스를 자동화합니다. 비즈니스, 건강과 금용 애플리케이션에서 높은 가치가 있다고 증명되었습니다.
+
+가까운 미래에, 머신러닝의 기본을 이해하는 건 광범위한 선택으로 인하여 모든 분야의 사람들에게 필수적으로 다가올 것 입니다.
+
+---
+## 🚀 도전
+
+종이에 그리거나, [Excalidraw](https://excalidraw.com/)처럼 온라인 앱을 이용하여 AI, ML, 딥러닝, 그리고 데이터 사이언스의 차이를 이해합시다. 각 기술들이 잘 해결할 수 있는 문제에 대해 아이디어를 합쳐보세요.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+
+## 리뷰 & 자기주도 학습
+
+클라우드에서 ML 알고리즘을 어떻게 사용하는 지 자세히 알아보려면, [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)를 따릅니다.
+
+ML의 기초에 대한 [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa)를 봅니다.
+
+## 과제
+
+[Get up and running](../assignment.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md
index cfa7e1274..669e649de 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.tr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.tr.md
@@ -4,7 +4,7 @@
 
 > 🎥  Makine öğrenimi, yapay zeka ve derin öğrenme arasındaki farkı tartışan bir video için yukarıdaki resme tıklayın.
 
-## [Ders öncesi sınav](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=tr)
+## [Ders öncesi sınav](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=tr)
 
 ### Introduction
 
@@ -103,7 +103,7 @@ Yakın gelecekte, yaygın olarak benimsenmesi nedeniyle makine öğreniminin tem
 
 Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bir uygulama kullanarak AI, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki farkları anladığınızdan emin olun. Bu tekniklerin her birinin çözmede iyi olduğu bazı problem fikirleri ekleyin.
 
-## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=tr)
+## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=tr)
 
 ## İnceleme ve Bireysel Çalışma
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index 8693ff20c..a29596036 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -4,24 +4,24 @@
 
 > 🎥 点击上面的图片观看讨论机器学习、人工智能和深度学习之间区别的视频。
 
-## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
 ### 介绍
 
-欢迎来到这个经典机器学习的初学者课程！无论你是这个主题的新手，还是一个有经验的ML从业者，我们都很高兴你能加入我们！我们希望为你的ML研究创建一个好的开始，并很乐意评估、回应和接受你的[反馈](https://github.com/microsoft/ML-For-Beginners/discussions)。
+欢迎来到这个经典机器学习的初学者课程！无论你是这个主题的新手，还是一个有经验的 ML 从业者，我们都很高兴你能加入我们！我们希望为你的 ML 研究创建一个好的开始，并很乐意评估、回应和接受你的[反馈](https://github.com/microsoft/ML-For-Beginners/discussions)。
 
 [![机器学习简介](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
 
 > 🎥 单击上图观看视频：麻省理工学院的 John Guttag 介绍机器学习
 ### 机器学习入门
 
-在开始本课程之前，你需要设置计算机能在本地运行Jupyter Notebooks。
+在开始本课程之前，你需要设置计算机能在本地运行 Jupyter Notebooks。
 
 - **按照这些视频里的讲解配置你的计算机**。了解有关如何在此[视频集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中设置计算机的更多信息。
-- **学习Python**. 还建议你对[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，我们在本课程中使用的一种对数据科学家有用的编程语言，有一个基本的了解。
-- **学习Node.js和JavaScript**。在本课程中，我们在构建web应用程序时也使用过几次JavaScript，因此你需要有[node](https://nodejs.org)和[npm](https://www.npmjs.com/) 以及[Visual Studio Code](https://code.visualstudio.com/)用于Python和JavaScript开发。
-- **创建GitHub帐户**。既然你在[GitHub](https://github.com)上找到我们，你可能已经有了一个帐户，但如果没有，请创建一个帐户，然后fork此课程自己使用(也给我们一颗星星吧😊) 
-- **探索Scikit-learn**. 熟悉[Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我们在这些课程中引用的一组ML库。
+- **学习 Python**。 还建议你对 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) 有一个基本的了解。这是我们在本课程中使用的一种对数据科学家有用的编程语言。
+- **学习 Node.js 和 JavaScript**。在本课程中，我们在构建 web 应用程序时也使用过几次 JavaScript，因此你需要有 [Node.js](https://nodejs.org) 和 [npm](https://www.npmjs.com/) 以及 [Visual Studio Code](https://code.visualstudio.com/) 用于 Python 和 JavaScript 开发。
+- **创建 GitHub 帐户**。既然你在 [GitHub](https://github.com) 上找到我们，你可能已经有了一个帐户，但如果没有，请创建一个帐户，然后 fork 此课程自己使用(也给我们一颗星星吧😊) 
+- **探索 Scikit-learn**. 熟悉 [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我们在这些课程中引用的一组 ML 库。
 
 ### 什么是机器学习？
 
@@ -41,11 +41,11 @@
 
 ![人工智能、机器学习、深度学习、数据科学](../images/ai-ml-ds.png)
 
-> 显示AI、ML、深度学习和数据科学之间关系的图表。图片作者[Jen Looper](https://twitter.com/jenlooper)，灵感来自[这张图](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
+> 显示 AI、ML、深度学习和数据科学之间关系的图表。图片作者 [Jen Looper](https://twitter.com/jenlooper)，灵感来自[这张图](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
 ## 你将在本课程中学到什么
 
-在本课程中，我们将仅涵盖初学者必须了解的机器学习的核心概念。 我们主要使用Scikit-learn来介绍我们所谓的“经典机器学习”，这是一个许多学生用来学习基础知识的优秀库。要理解更广泛的人工智能或深度学习的概念，机器学习的基础知识是必不可少的，所以我们想在这里提供它。
+在本课程中，我们将仅涵盖初学者必须了解的机器学习的核心概念。 我们主要使用 Scikit-learn 来介绍我们所谓的“经典机器学习”，这是一个许多学生用来学习基础知识的优秀库。要理解更广泛的人工智能或深度学习的概念，机器学习的基础知识是必不可少的，所以我们想在这里提供它。
 
 在本课程中，你将学习：
 
@@ -94,14 +94,14 @@
 ---
 ## 🚀 挑战
 
-在纸上或使用[Excalidraw](https://excalidraw.com/)等在线应用程序绘制草图，了解你对AI、ML、深度学习和数据科学之间差异的理解。添加一些关于这些技术擅长解决的问题的想法。
+在纸上或使用 [Excalidraw](https://excalidraw.com/) 等在线应用程序绘制草图，了解你对 AI、ML、深度学习和数据科学之间差异的理解。添加一些关于这些技术擅长解决的问题的想法。
 
-## [阅读后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
 ## 复习与自学
 
-要了解有关如何在云中使用ML算法的更多信息，请遵循以下[学习路径](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)。
+要了解有关如何在云中使用 ML 算法的更多信息，请遵循以下[学习路径](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)。
 
 ## 任务
 
-[启动并运行](../assignment.md)
+[启动并运行](assignment.zh-cn.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.es.md b/1-Introduction/1-intro-to-ML/translations/assignment.es.md
new file mode 100644
index 000000000..5241ca962
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.es.md
@@ -0,0 +1,9 @@
+# Lévantate y corre
+
+## Instrucciones
+
+En esta tarea no calificada, debe repasar Python y hacer que su entorno esté en funcionamiento y sea capaz de ejecutar cuadernos.
+
+Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), y luego configure sus sistemas con estos videos introductorios:
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.fr.md b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md
new file mode 100644
index 000000000..0d703d26c
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md
@@ -0,0 +1,10 @@
+# Être opérationnel
+
+
+## Instructions
+
+Dans ce devoir non noté, vous devez vous familiariser avec Python et rendre votre environnement opérationnel et capable d'exécuter des notebook.
+
+Suivez ce [parcours d'apprentissage Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), puis configurez votre système en parcourant ces vidéos introductives :
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.id.md b/1-Introduction/1-intro-to-ML/translations/assignment.id.md
new file mode 100644
index 000000000..c6ba6e4a8
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.id.md
@@ -0,0 +1,9 @@
+# Persiapan
+
+## Instruksi
+
+Dalam tugas yang tidak dinilai ini, kamu akan mempelajari Python dan mempersiapkan *environment* kamu sehingga dapat digunakan untuk menjalankan *notebook*.
+
+Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) ini, kemudian persiapkan sistem kamu dengan menonton video-video pengantar ini:
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ja.md b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md
new file mode 100644
index 000000000..9c86969cd
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md
@@ -0,0 +1,9 @@
+# 稼働させる
+
+## 指示
+
+この評価のない課題では、Pythonについて復習し、環境を稼働させてノートブックを実行できるようにする必要があります。
+
+この[Pythonラーニングパス](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)を受講し、次の入門用ビデオに従ってシステムをセットアップしてください。
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..fd59f6919
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md
@@ -0,0 +1,9 @@
+# 启动和运行
+
+## 说明
+
+在这个不评分的作业中，你应该温习一下 Python，将 Python 环境能够运行起来，并且可以运行 notebooks。
+
+学习这个 [Python 学习路径](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，然后通过这些介绍性的视频将你的系统环境设置好：
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md
index 67c93dbf5..a44703d68 100644
--- a/1-Introduction/2-history-of-ML/README.md
+++ b/1-Introduction/2-history-of-ML/README.md
@@ -3,7 +3,7 @@
 ![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
 
@@ -101,7 +101,7 @@ It remains to be seen what the future holds, but it is important to understand t
 
 Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## Review & Self Study
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
old mode 100644
new mode 100755
index e69de29bb..28402267a
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -0,0 +1,117 @@
+# Historia del machine learning
+
+![Resumen de la historoia del machine learning en un boceto](../../sketchnotes/ml-history.png)
+> Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+
+En esta lección, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial.
+
+La historia de la inteligencia artificial, AI, como campo está entrelazada con la historia del machine learning, ya que los algoritmos y avances computacionales que sustentan el ML se incorporaron al desarrollo de la inteligencia artificial. Es útil recordar que, si bien, estos campos como áreas distintas de investigación comenzaron a cristalizar en la década de 1950, importantes [desubrimientos algorítmicos, estadísticos, matemáticos, computacionales y técnicos](https://wikipedia.org/wiki/Timeline_of_machine_learning) predecieronn y superpusieron a esta era. De hecho, las personas han estado pensando en estas preguntas durante [cientos de años](https://wikipedia.org/wiki/History_of_artificial_intelligence): este artículo analiza los fundamentos intelectuales históricos de la idea de una 'máquina pensante.'
+
+## Descubrimientos notables
+
+- 1763, 1812 [Teorema de Bayes](https://wikipedia.org/wiki/Bayes%27_theorem) y sus predecesores. Este teorema y sus aplicaciones son la base de la inferencia, describiendo la probabilidad de que ocurra un evento basado en el concimiento previo.
+- 1805 [Teoría de mínimos cuadrados](https://wikipedia.org/wiki/Least_squares) por el matemático francés Adrien-Marie Legendre. Esta teoría, que aprenderá en nuestra unidad de Regresión, ayuda en el data fitting.
+- 1913 [Cadenas de Markov](https://wikipedia.org/wiki/Markov_chain) el nombre del matemático ruso Andrey Markov es utilizado para describir una secuencia de eventos basados en su estado anterior.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) es un tipo de clasificador lineal inventado por el psicólogo Frank Rosenblatt que subyace a los avances en el deep learning.
+- 1967 [Nearest Neighbor (Vecino más cercano)](https://wikipedia.org/wiki/Nearest_neighbor) es un algoritmo diseñado originalmente para trazar rutas. En un contexto de ML, se utiliza para detectar patrones.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) es usado para entrenar [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network).
+- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) son redes neuronales artificiales derivadas de redes neuronales feedforward que crean grafos temporales.
+
+✅ Investigue un poco. ¿Qué otras fechas se destacan como fundamentales en la historia del machine learning (ML) y la inteligencia artificial (AI)?
+## 1950: Máquinas que piensan
+
+Alan Turing, una persona verdaderamente notable que fue votada [por el público en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) como el científico más grande del siglo XX, se le atribuye haber ayudado a sentar las bases del concepto de una 'máquina que puede pensar.' Lidió con los detractores y su propia necesidad de evidencia empírica de este concepto en parte mediante la creación de la [prueba de Turing](https://www.bbc.com/news/technology-18475646, que explorarás en nuestras lecciones de NLP.
+
+## 1956: Dartmouth Summer Research Project
+
+"The Dartmouth Summer Research Project sobre inteligencia artificial fuer un evento fundamental para la inteligencia artificial como campo," y fue aquí donde el se acuñó el término 'inteligencia artificial' ([fuente](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+
+> Todos los aspectos del aprendizaje y cualquier otra característica de la inteligencia pueden, en principio, describirse con tanta precisión que se puede hacer una máquina para simularlos.
+
+El investigador principal, el profesor de matemáticas John McCarthy, esperaba "proceder sobre las bases de la conjetura que cada aspecto del aprendizaje o cualquier otra característica de la inteligencia pueden, en principio, describirse con tanta precición que se se puede hacer una máquina para simularlos." Los participantes, incluyeron otra luminaria en el campo, Marvin Minsky.
+
+El taller tiene el mérito de haber iniciado y alentado varias discusiones que incluyen "el surgimiento de métodos simbólicos, systemas en dominios limitados (primeros sistemas expertos), y sistemas deductivos versus sistemas inductivos."  ([fuente](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Los años dorados"
+
+Desde la década de 1950, hasta mediados de la de 1970, el optimismo se elevó con la esperanza de que la AI pudiera resolver muchos problemas. En 1967, Marvin Minsky declaró con seguridad que "dentro de una generación ... el problema de crear 'inteligencia artificial' se resolverá sustancialemte."  (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La investigación del procesamiento del lenguaje natural floreció, la búsqueda se refinó y se hizo más poderosa, y el concepto de 'micro-worlds' fue creado, donde se completaban tareas simples utilizando instrucciones en lenguaje sencillo.
+
+La investigación estuvo bien financiado por agencias gubernamentales, se realizaron avances en computación y algoritmos, y se construyeron prototipos de máquinas inteligentes.Algunas de esta máquinas incluyen:
+
+* [Shakey la robot](https://wikipedia.org/wiki/Shakey_the_robot), que podría maniobrar y decidir cómo realizar las tares de forma 'inteligente'.
+
+    ![Shakey, un robot inteligente](images/shakey.jpg)
+    > Shakey en 1972
+
+* Eliza, unas de las primeras 'chatterbot', podía conversar con las personas y actuar como un 'terapeuta' primitivo. Aprenderá más sobre ELiza en las lecciones de NLP.
+
+    ![Eliza, un bot](images/eliza.png)
+    > Una versión de Eliza, un chatbot
+
+* "Blocks world" era un ejemplo de micro-world donde los bloques se podían apilar y ordenar, y se podían probar experimentos en máquinas de enseñanza para tomar decisiones. Los avances creados con librerías como [SHRDLU](https://wikipedia.org/wiki/SHRDLU) ayudaron a inpulsar el procesamiento del lenguaje natural.
+
+    [![blocks world con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world con SHRDLU")
+    
+    > 🎥 Haga click en la imagen de arriba para ver un video: Blocks world con SHRDLU
+
+## 1974 - 1980: "Invierno de la AI"
+
+A mediados de la década de 1970, se hizo evidente que la complejidad de la fabricación de 'máquinas inteligentes' se había subestimado y que su promesa, dado la potencia computacional disponible, había sido exagerada. La financiación se agotó y la confianza en el campo se ralentizó. Algunos problemas que impactaron la confianza incluyeron:
+
+- **Limitaciones**. La potencia computacional era demasiado limitada.
+- **Explosión combinatoria**. La cantidad de parámetros necesitados para entrenar creció exponencialmente a medida que se pedía más a las computadoras sin una evolución paralela de la potencia y la capacidad de cómputo.
+- **Escasez de datos**. Hubo una escasez de datos que obstaculizó el proceso de pruebas, desarrollo y refinamiento de algoritmos.
+- **¿Estamos haciendo las preguntas correctas?**. Las mismas preguntas que se estaban formulando comenzaron a cuestionarse. Los investigadores comenzaron a criticar sus aproches:
+  - Las pruebas de Turing se cuestionaron por medio, entre otras ideas, de la 'teoría de la habitación china' que postulaba que "progrmar una computadora digital puede hacerse que parezca que entiende el lenguaje, pero no puede producir una comprensión real" ([fuente](https://plato.stanford.edu/entries/chinese-room/))
+  - Se cuestionó la ética de introducir inteligencias artificiales como la "terapeuta" Eliza en la sociedad.
+
+Al mismo tiempo, comenzaron a formarse varia escuelas de pensamiento de AI. Se estableció una dicotomía entre las prácticas ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). _Scruffy_ labs modificó los programas durante horas hasta que obtuvieron los objetivos deseados. _Neat_ labs "centrados en la lógica y la resolución de problemas formales". ELIZA y SHRDLU eran systemas _scruffy_ bien conocidos. En la década de 1980, cuando surgió la demanda para hacer que los sistemas de aprendizaje fueran reproducibles, el enfoque _neat_ gradualmente tomó la vanguardia a medidad que sus resultados eran más explicables.
+
+## Systemas expertos de la década de 1980
+
+A medida que el campo creció, su beneficio para las empresas se hizo más claro, y en la década de 1980 también lo hizo la proliferación de 'sistemas expertos'. "Los sistemas expertos estuvieron entre las primeras formas verdaderamente exitosas de software de inteligencia artificial (IA)." ([fuente](https://wikipedia.org/wiki/Expert_system)).
+
+Este tipo de sistemas es en realidad _híbrido_, que consta parcialmente de un motor de reglas que define los requisitos comerciales, y un motor de inferencia que aprovechó el sistema de reglas para deducir nuevos hechos.
+
+En esta era también se prestó mayor atención a las redes neuronales.
+
+## 1987 - 1993: AI 'Chill'
+
+La prolifercaión de hardware de sistemas expertos especializados tuvo el desafortunado efecto de volverse demasiado especializado. El auge de las computadoras personales también compitió con estos grandes sistemas centralizados especializados. La democratización de la informática había comenzado, y finalmente, allanó el camino para la explosión moderna del big data.
+
+## 1993 - 2011
+
+Esta época vió una nueva era para el ML y la IA para poder resolver problemas que habían sido causados anteriormente for la falta de datos y poder de cómputo. La cantidad de datos comenzó a aumentar rápidamente y a estar más disponible, para bien o para mal, especialmente con la llegada del smartphone alrededor del 2007. El poder computacional se expandió exponencialmente y los algoritmos evolucionaron al mismo tiempo. El campo comenzó a ganar madurez a medida que los días libres del pasado comenzaron a cristalizar en un verdadera disciplina.
+
+## Ahora
+
+Hoy en día, machine learning y la inteligencia artificial tocan casi todos los aspectos de nuestras vidas. Esta era requiere una comprensión cuidadosa de los riesgos y los efectos potenciales de estos algoritmos en las vidas humanas. Como ha dicho Brad Smith de Microsoft, "La tecnología de la información plantea problemas que van al corazón de las protecciones fundamentales de los derechos humanos, como la privacidad y la libertad de expresión. Esos problemas aumentan las responsabilidades de las empresas de tecnología que crean estos productos. En nuestra opinión, también exige regulación gubernamental reflexiva y para el desarrollo de normas sobre usos aceptables" ([fuente](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Queda por ver qué depara el futuro, pero es importante entender estos sistemas informáticos y el software y algortimos que ejecutan. Esperamos que este plan de estudios le ayude a comprender mejor para que pueda decidir por si mismo.
+
+[![La historia del deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
+> 🎥 Haga Click en la imagen de arriba para ver un video: Yann LeCun analiza la historia del deep learning en esta conferencia
+
+---
+## 🚀Desafío
+
+Sumérjase dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca se creó ningún descubrimiento científico en un vacío cultural. ¿Qué descubres?
+
+## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+
+## Revisión y autoestudio
+
+Aquí hay elementos para ver y escuchar:
+
+[Este podcast donde Amy Boyd habla sobre la evolución de la IA](http://runasradio.com/Shows/Show/739)
+
+[![La historia de la IA por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La historia de la IA por Amy Boyd")
+
+## Asignación
+
+[Crea un timeline](assignment.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
new file mode 100644
index 000000000..efe268777
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -0,0 +1,117 @@
+# Histoire du Machine Learning (apprentissage automatique)
+
+![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quizz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=fr)
+
+Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle.
+
+L'histoire de l'intelligence artificielle, l'IA, en tant que domaine est étroitement liée à l'histoire du machine learning, car les algorithmes et les avancées informatiques qui sous-tendent le ML alimentent le développement de l'IA. Bien que ces domaines en tant que domaines de recherches distincts ont commencé à se cristalliser dans les années 1950, il est important de rappeler que les [découvertes algorithmiques, statistiques, mathématiques, informatiques et techniques](https://wikipedia.org/wiki/Timeline_of_machine_learning) ont précédé et chevauchait cette époque. En fait, le monde réfléchit à ces questions depuis [des centaines d'années](https://fr.wikipedia.org/wiki/Histoire_de_l%27intelligence_artificielle) : cet article traite des fondements intellectuels historiques de l'idée d'une « machine qui pense ».
+
+## Découvertes notables
+
+- 1763, 1812 [théorème de Bayes](https://wikipedia.org/wiki/Bayes%27_theorem) et ses prédécesseurs. Ce théorème et ses applications sous-tendent l'inférence, décrivant la probabilité qu'un événement se produise sur la base de connaissances antérieures.
+- 1805 [Théorie des moindres carrés](https://wikipedia.org/wiki/Least_squares) par le mathématicien français Adrien-Marie Legendre. Cette théorie, que vous découvrirez dans notre unité Régression, aide à l'ajustement des données.
+- 1913 [Chaînes de Markov](https://wikipedia.org/wiki/Markov_chain) du nom du mathématicien russe Andrey Markov sont utilisées pour décrire une séquence d'événements possibles basée sur un état antérieur.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) est un type de classificateur linéaire inventé par le psychologue américain Frank Rosenblatt qui sous-tend les progrès de l'apprentissage en profondeur.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) est un algorithme conçu à l'origine pour cartographier les itinéraires. Dans un contexte ML, il est utilisé pour détecter des modèles.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) est utilisé pour former des [réseaux de neurones feedforward (propagation avant)](https://fr.wikipedia.org/wiki/R%C3%A9seau_de_neurones_%C3%A0_propagation_avant).
+- 1982 [Réseaux de neurones récurrents](https://wikipedia.org/wiki/Recurrent_neural_network) sont des réseaux de neurones artificiels dérivés de réseaux de neurones à réaction qui créent des graphes temporels.
+
+✅ Faites une petite recherche. Quelles autres dates sont marquantes dans l'histoire du ML et de l'IA ?
+
+## 1950 : Des machines qui pensent
+
+Alan Turing, une personne vraiment remarquable qui a été élue [par le public en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) comme le plus grand scientifique du 20e siècle, est reconnu pour avoir aidé à jeter les bases du concept d'une "machine qui peut penser". Il a lutté avec ses opposants et son propre besoin de preuves empiriques de sa théorie en créant le [Test de Turing] (https://www.bbc.com/news/technology-18475646), que vous explorerez dans nos leçons de NLP (TALN en français).
+
+## 1956 : Projet de recherche d'été à Dartmouth
+
+« Le projet de recherche d'été de Dartmouth sur l'intelligence artificielle a été un événement fondateur pour l'intelligence artificielle en tant que domaine », et c'est ici que le terme « intelligence artificielle » a été inventé ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> Chaque aspect de l'apprentissage ou toute autre caractéristique de l'intelligence peut en principe être décrit si précisément qu'une machine peut être conçue pour les simuler.
+
+Le chercheur en tête, le professeur de mathématiques John McCarthy, espérait « procéder sur la base de la conjecture selon laquelle chaque aspect de l'apprentissage ou toute autre caractéristique de l'intelligence peut en principe être décrit avec une telle précision qu'une machine peut être conçue pour les simuler ». Les participants comprenaient une autre sommité dans le domaine, Marvin Minsky.
+
+L'atelier est crédité d'avoir initié et encouragé plusieurs discussions, notamment « l'essor des méthodes symboliques, des systèmes spécialisés sur des domaines limités (premiers systèmes experts) et des systèmes déductifs par rapport aux systèmes inductifs ». ([source](https://fr.wikipedia.org/wiki/Conf%C3%A9rence_de_Dartmouth)).
+
+## 1956 - 1974 : "Les années d'or"
+
+Des années 50 au milieu des années 70, l'optimisme était au rendez-vous en espérant que l'IA puisse résoudre de nombreux problèmes. En 1967, Marvin Minsky a déclaré avec assurance que « Dans une génération... le problème de la création d'"intelligence artificielle" sera substantiellement résolu. » (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La recherche sur le Natural Language Processing (traitement du langage naturel en français) a prospéré, la recherche a été affinée et rendue plus puissante, et le concept de « micro-mondes » a été créé, où des tâches simples ont été effectuées en utilisant des instructions en langue naturelle.
+
+La recherche a été bien financée par les agences gouvernementales, des progrès ont été réalisés dans le calcul et les algorithmes, et des prototypes de machines intelligentes ont été construits. Certaines de ces machines incluent :
+
+* [Shakey le robot](https://fr.wikipedia.org/wiki/Shakey_le_robot), qui pouvait manœuvrer et décider comment effectuer des tâches « intelligemment ».
+
+    ![Shakey, un robot intelligent](../images/shakey.jpg)
+    > Shaky en 1972
+
+* Eliza, une des premières « chatbot », pouvait converser avec les gens et agir comme une « thérapeute » primitive. Vous en apprendrez plus sur Eliza dans les leçons de NLP.
+
+    ![Eliza, un bot](../images/eliza.png)
+    > Une version d'Eliza, un chatbot
+
+* Le « monde des blocs » était un exemple de micro-monde où les blocs pouvaient être empilés et triés, et où des expériences d'apprentissages sur des machines, dans le but qu'elles prennent des décisions, pouvaient être testées. Les avancées réalisées avec des bibliothèques telles que [SHRDLU](https://fr.wikipedia.org/wiki/SHRDLU) ont contribué à faire avancer le natural language processing.
+
+    [![Monde de blocs avec SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Monde de blocs avec SHRDLU" )
+    
+    > 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Blocks world with SHRDLU
+
+## 1974 - 1980 : « l'hiver de l'IA »
+
+Au milieu des années 1970, il était devenu évident que la complexité de la fabrication de « machines intelligentes » avait été sous-estimée et que sa promesse, compte tenu de la puissance de calcul disponible, avait été exagérée. Les financements se sont taris et la confiance dans le domaine s'est ralentie. Parmi les problèmes qui ont eu un impact sur la confiance, citons :
+
+- **Restrictions**. La puissance de calcul était trop limitée.
+- **Explosion combinatoire**. Le nombre de paramètres à former augmentait de façon exponentielle à mesure que l'on en demandait davantage aux ordinateurs, sans évolution parallèle de la puissance et de la capacité de calcul.
+- **Pénurie de données**. Il y avait un manque de données qui a entravé le processus de test, de développement et de raffinement des algorithmes.
+- **Posions-nous les bonnes questions ?**. Les questions mêmes, qui étaient posées, ont commencé à être remises en question. Les chercheurs ont commencé à émettre des critiques sur leurs approches :
+  - Les tests de Turing ont été remis en question au moyen, entre autres, de la « théorie de la chambre chinoise » qui postulait que « la programmation d'un ordinateur numérique peut faire croire qu'il comprend le langage mais ne peut pas produire une compréhension réelle ». ([source](https://plato.stanford.edu/entries/chinese-room/))
+  - L'éthique de l'introduction d'intelligences artificielles telles que la "thérapeute" ELIZA dans la société a été remise en cause.
+
+Dans le même temps, diverses écoles de pensée sur l'IA ont commencé à se former. Une dichotomie a été établie entre les pratiques IA ["scruffy" et "neat"](https://wikipedia.org/wiki/Neats_and_scruffies). Les laboratoires _Scruffy_ peaufinaient leurs programmes pendant des heures jusqu'à ce qu'ils obtiennent les résultats souhaités. Les laboratoires _Neat_ "se concentraient sur la logique et la résolution formelle de problèmes". ELIZA et SHRDLU étaient des systèmes _scruffy_ bien connus. Dans les années 1980, alors qu'émergeait la demande de rendre les systèmes ML reproductibles, l'approche _neat_ a progressivement pris le devant de la scène car ses résultats sont plus explicables.
+
+## 1980 : Systèmes experts
+
+Au fur et à mesure que le domaine s'est développé, ses avantages pour les entreprises sont devenus plus clairs, particulièrement via les « systèmes experts » dans les années 1980. "Les systèmes experts ont été parmi les premières formes vraiment réussies de logiciels d'intelligence artificielle (IA)." ([source](https://fr.wikipedia.org/wiki/Syst%C3%A8me_expert)).
+
+Ce type de système est en fait _hybride_, composé en partie d'un moteur de règles définissant les exigences métier et d'un moteur d'inférence qui exploite le système de règles pour déduire de nouveaux faits.
+
+Cette époque a également vu une attention croissante accordée aux réseaux de neurones.
+
+## 1987 - 1993 : IA « Chill »
+
+La prolifération du matériel spécialisé des systèmes experts a eu pour effet malheureux de devenir trop spécialisée. L'essor des ordinateurs personnels a également concurrencé ces grands systèmes spécialisés et centralisés. La démocratisation de l'informatique a commencé et a finalement ouvert la voie à l'explosion des mégadonnées.
+
+## 1993 - 2011
+
+Cette époque a vu naître une nouvelle ère pour le ML et l'IA afin de résoudre certains des problèmes qui n'avaient pu l'être plus tôt par le manque de données et de puissance de calcul. La quantité de données a commencé à augmenter rapidement et à devenir plus largement disponibles, pour le meilleur et pour le pire, en particulier avec l'avènement du smartphone vers 2007. La puissance de calcul a augmenté de façon exponentielle et les algorithmes ont évolué parallèlement. Le domaine a commencé à gagner en maturité alors que l'ingéniosité a commencé à se cristalliser en une véritable discipline.
+
+## À présent
+
+Aujourd'hui, le machine learning et l'IA touchent presque tous les aspects de notre vie. Cette ère nécessite une compréhension approfondie des risques et des effets potentiels de ces algorithmes sur les vies humaines. Comme l'a déclaré Brad Smith de Microsoft, « les technologies de l'information soulèvent des problèmes qui vont au cœur des protections fondamentales des droits de l'homme comme la vie privée et la liberté d'expression. Ces problèmes accroissent la responsabilité des entreprises technologiques qui créent ces produits. À notre avis, ils appellent également à une réglementation gouvernementale réfléchie et au développement de normes autour des utilisations acceptables" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Reste à savoir ce que l'avenir nous réserve, mais il est important de comprendre ces systèmes informatiques ainsi que les logiciels et algorithmes qu'ils exécutent. Nous espérons que ce programme vous aidera à mieux les comprendre afin que vous puissiez décider par vous-même.
+
+[![L'histoire du Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "L'histoire du Deep Learning")
+> 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Yann LeCun discute de l'histoire du deep learning dans cette conférence
+
+---
+## 🚀Challenge
+
+Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
+
+## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=fr)
+
+## Révision et auto-apprentissage
+
+Voici quelques articles à regarder et à écouter :
+
+[Ce podcast où Amy Boyd discute de l'évolution de l'IA](http://runasradio.com/Shows/Show/739)
+
+[![L'histoire de l'IA par Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "L'histoire de l'IA par Amy Boyd")
+
+## Devoir
+
+[Créer une frise chronologique](assignment.fr.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
new file mode 100644
index 000000000..9e695a8a9
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -0,0 +1,116 @@
+# Sejarah Machine Learning
+
+![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-history.png)
+> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+
+Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence. 
+
+Sejarah Artifical Intelligence, AI, sebagai bidang terkait dengan sejarah Machine Learning, karena algoritma dan kemajuan komputasi yang mendukung ML dimasukkan ke dalam pengembangan AI. Penting untuk diingat bahwa, meski bidang-bidang ini sebagai bidang-bidang penelitian yang berbeda mulai terbentuk pada 1950-an, [algoritmik, statistik, matematik, komputasi dan penemuan teknis](https://wikipedia.org/wiki/Timeline_of_machine_learning) penting sudah ada sebelumnya, dan saling tumpang tindih di era ini. Faktanya, orang-orang telah memikirkan pertanyaan-pertanyaan ini selama [ratusan tahun](https://wikipedia.org/wiki/History_of_artificial_intelligence): artikel ini membahas dasar-dasar intelektual historis dari gagasan 'mesin yang berpikir'. 
+
+## Penemuan penting
+
+- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) dan para pendahulu. Teorema ini dan penerapannya mendasari inferensi, mendeskripsikan kemungkinan suatu peristiwa terjadi berdasarkan pengetahuan sebelumnya. 
+- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) oleh matematikawan Perancis Adrien-Marie Legendre. Teori ini yang akan kamu pelajari di unit Regresi, ini membantu dalam *data fitting*. 
+- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) dinamai dengan nama matematikawan Rusia, Andrey Markov, digunakan untuk mendeskripsikan sebuah urutan dari kejadian-kejadian yang mungkin terjadi berdasarkan kondisi sebelumnya.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) adalah sebuah tipe dari *linear classifier* yang ditemukan oleh psikolog Amerika, Frank Rosenblatt, yang mendasari kemajuan dalam *Deep Learning*.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) adalah sebuah algoritma yang pada awalnya didesain untuk memetakan rute. Dalam konteks ML, ini digunakan untuk mendeteksi berbagai pola.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) digunakan untuk melatih [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network). 
+- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) adalah *artificial neural networks* yang berasal dari *feedforward neural networks* yang membuat grafik sementara. 
+
+✅ Lakukan sebuah riset kecil. Tanggal berapa lagi yang merupakan tanggal penting dalam sejarah ML dan AI?
+## 1950: Mesin yang berpikir
+
+Alan Turing, merupakan orang luar biasa yang terpilih oleh [publik di tahun 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) sebagai ilmuwan terhebat di abad 20, diberikan penghargaan karena membantu membuat fondasi dari sebuah konsep 'mesin yang bisa berpikir', Dia berjuang menghadapi orang-orang yang menentangnya dan keperluannya sendiri untuk bukti empiris dari konsep ini dengan membuat [Turing Test](https://www.bbc.com/news/technology-18475646), yang mana akan kamu jelajahi di pelajaran NLP kami.
+
+## 1956: Proyek Riset Musim Panas Dartmouth
+
+"Proyek Riset Musim Panas Dartmouth pada *artificial intelligence* merupakan sebuah acara penemuan untuk *artificial intelligence* sebagai sebuah bidang," dan dari sinilah istilah '*artificial intelligence*' diciptakan ([sumber](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> Setiap aspek pembelajaran atau fitur kecerdasan lainnya pada prinsipnya dapat dideskripsikan dengan sangat tepat sehingga sebuah mesin dapat dibuat untuk mensimulasikannya. 
+
+Ketua peneliti, profesor matematika John McCarthy, berharap "untuk meneruskan dasar dari dugaan bahwa setiap aspek pembelajaran atau fitur kecerdasan lainnya pada prinsipnya dapat dideskripsikan dengan sangat tepat sehingga mesin dapat dibuat untuk mensimulasikannya." Marvin Minsky, seorang tokoh terkenal di bidang ini juga termasuk sebagai peserta penelitian.
+
+Workshop ini dipuji karena telah memprakarsai dan mendorong beberapa diskusi termasuk "munculnya metode simbolik, sistem yang berfokus pada domain terbatas (sistem pakar awal), dan sistem deduktif versus sistem induktif." ([sumber](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Tahun-tahun Emas"
+
+Dari tahun 1950-an hingga pertengahan 70-an, optimisme memuncak dengan harapan bahwa AI dapat memecahkan banyak masalah. Pada tahun 1967, Marvin Minsky dengan yakin menyatakan bahwa "Dalam satu generasi ... masalah menciptakan '*artificial intelligence*' akan terpecahkan secara substansial." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+Penelitian *natural language processing* berkembang, pencarian disempurnakan dan dibuat lebih *powerful*, dan konsep '*micro-worlds*' diciptakan, di mana tugas-tugas sederhana diselesaikan menggunakan instruksi bahasa sederhana. 
+
+Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat dalam komputasi dan algoritma, dan prototipe mesin cerdas dibangun. Beberapa mesin tersebut antara lain: 
+
+* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), yang bisa bermanuver dan memutuskan bagaimana melakukan tugas-tugas secara 'cerdas'. 
+
+    ![Shakey, an intelligent robot](../images/shakey.jpg)
+    > Shakey pada 1972
+
+* Eliza, sebuah 'chatterbot' awal, dapat mengobrol dengan orang-orang dan bertindak sebagai 'terapis' primitif. Kamu akan belajar lebih banyak tentang Eliza dalam pelajaran NLP. 
+
+    ![Eliza, a bot](../images/eliza.png)
+    > Sebuah versi dari Eliza, sebuah *chatbot*
+
+* "Blocks world" adalah contoh sebuah *micro-world* dimana balok dapat ditumpuk dan diurutkan, dan pengujian eksperimen mesin pengajaran untuk membuat keputusan dapat dilakukan. Kemajuan yang dibuat dengan *library-library* seperti [SHRDLU](https://wikipedia.org/wiki/SHRDLU) membantu mendorong kemajuan pemrosesan bahasa.
+
+    [![blocks world dengan SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world dengan SHRDLU")
+    
+    > 🎥 Klik gambar diatas untuk menonton video: Blocks world with SHRDLU
+
+## 1974 - 1980: "Musim Dingin AI"
+
+Pada pertengahan 1970-an, semakin jelas bahwa kompleksitas pembuatan 'mesin cerdas' telah diremehkan dan janjinya, mengingat kekuatan komputasi yang tersedia, telah dilebih-lebihkan. Pendanaan telah habis dan kepercayaan dalam bidang ini menurun. Beberapa masalah yang memengaruhi kepercayaan diri termasuk: 
+
+- **Keterbatasan**. Kekuatan komputasi terlalu terbatas.
+- **Ledakan kombinatorial**. Jumlah parameter yang perlu dilatih bertambah secara eksponensial karena lebih banyak hal yang diminta dari komputer, tanpa evolusi paralel dari kekuatan dan kemampuan komputasi. 
+- **Kekurangan data**. Adanya kekurangan data yang menghalangi proses pengujian, pengembangan, dan penyempurnaan algoritma.
+- **Apakah kita menanyakan pertanyaan yang tepat?**. Pertanyaan-pertanyaan yang diajukan pun mulai dipertanyakan kembali. Para peneliti mulai melontarkan kritik tentang pendekatan mereka 
+  - Tes Turing mulai dipertanyakan, di antara ide-ide lain, dari 'teori ruang Cina' yang mengemukakan bahwa, "memprogram komputer digital mungkin membuatnya tampak memahami bahasa tetapi tidak dapat menghasilkan pemahaman yang sebenarnya." ([sumber](https://plato.stanford.edu/entries/chinese-room/))
+  - Tantangan etika ketika memperkenalkan kecerdasan buatan seperti si "terapis" ELIZA ke dalam masyarakat. 
+
+Pada saat yang sama, berbagai aliran pemikiran AI mulai terbentuk. Sebuah dikotomi didirikan antara praktik ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). Lab _Scruffy_ mengubah program selama berjam-jam sampai mendapat hasil yang diinginkan. Lab _Neat_ "berfokus pada logika dan penyelesaian masalah formal". ELIZA dan SHRDLU adalah sistem _scruffy_ yang terkenal. Pada tahun 1980-an, karena perkembangan permintaan untuk membuat sistem ML yang dapat direproduksi, pendekatan _neat_ secara bertahap menjadi yang terdepan karena hasilnya lebih dapat dijelaskan.
+
+## 1980s Sistem Pakar
+
+Seiring berkembangnya bidang ini, manfaatnya bagi bisnis menjadi lebih jelas, dan begitu pula dengan menjamurnya 'sistem pakar' pada tahun 1980-an. "Sistem pakar adalah salah satu bentuk perangkat lunak artificial intelligence (AI) pertama yang benar-benar sukses."  ([sumber](https://wikipedia.org/wiki/Expert_system)).
+
+Tipe sistem ini sebenarnya adalah _hybrid_, sebagian terdiri dari mesin aturan yang mendefinisikan kebutuhan bisnis, dan mesin inferensi yang memanfaatkan sistem aturan untuk menyimpulkan fakta baru. 
+
+Pada era ini juga terlihat adanya peningkatan perhatian pada jaringan saraf. 
+
+## 1987 - 1993: AI 'Chill'
+
+Perkembangan perangkat keras sistem pakar terspesialisasi memiliki efek yang tidak menguntungkan karena menjadi terlalu terspesialiasasi. Munculnya komputer pribadi juga bersaing dengan sistem yang besar, terspesialisasi, dan terpusat ini. Demokratisasi komputasi telah dimulai, dan pada akhirnya membuka jalan untuk ledakan modern dari *big data*. 
+
+## 1993 - 2011
+
+Pada zaman ini memperlihatkan era baru bagi ML dan AI untuk dapat menyelesaikan beberapa masalah yang sebelumnya disebabkan oleh kurangnya data dan daya komputasi. Jumlah data mulai meningkat dengan cepat dan tersedia secara luas, terlepas dari baik dan buruknya, terutama dengan munculnya *smartphone* sekitar tahun 2007. Daya komputasi berkembang secara eksponensial, dan algoritma juga berkembang saat itu. Bidang ini mulai mengalami kedewasaan karena hari-hari yang tidak beraturan di masa lalu mulai terbentuk menjadi disiplin yang sebenarnya. 
+
+## Sekarang
+
+Saat ini, *machine learning* dan AI hampir ada di setiap bagian dari kehidupan kita. Era ini menuntut pemahaman yang cermat tentang risiko dan efek potensi dari berbagai algoritma yang ada pada kehidupan manusia. Seperti yang telah dinyatakan oleh Brad Smith dari Microsoft, "Teknologi informasi mengangkat isu-isu yang menjadi inti dari perlindungan hak asasi manusia yang mendasar seperti privasi dan kebebasan berekspresi. Masalah-masalah ini meningkatkan tanggung jawab bagi perusahaan teknologi yang menciptakan produk-produk ini. Dalam pandangan kami, mereka juga menyerukan peraturan pemerintah yang bijaksana dan untuk pengembangan norma-norma seputar penggunaan yang wajar" ([sumber](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk memahami sistem komputer dan perangkat lunak serta algoritma yang dijalankannya. Kami berharap kurikulum ini akan membantu kamu untuk mendapatkan pemahaman yang lebih baik sehingga kamu dapat memutuskan sendiri. 
+
+[![Sejarah Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "Sejarah Deep Learning")
+> 🎥 Klik gambar diatas untuk menonton video: Yann LeCun mendiskusikan sejarah dari Deep Learning dalam pelajaran ini
+
+---
+## 🚀Tantangan
+
+Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan? 
+
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+
+## Ulasan & Belajar Mandiri
+
+Berikut adalah item untuk ditonton dan didengarkan: 
+
+[Podcast dimana Amy Boyd mendiskusikan evolusi dari AI](http://runasradio.com/Shows/Show/739)
+
+[![Sejarah AI oleh Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Sejarah AI oleh Amy Boyd")
+
+## Tugas
+
+[Membuat sebuah *timeline*](assignment.id.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md
index c7e59fd77..e6fc2d900 100644
--- a/1-Introduction/2-history-of-ML/translations/README.it.md
+++ b/1-Introduction/2-history-of-ML/translations/README.it.md
@@ -1,118 +1,118 @@
-# Storia di machine learning
-
-![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png)
-> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
-
-In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
-
-La storia dell'intelligenza artificiale, AI, come campo è intrecciata con la storia di machine learning, poiché gli algoritmi e i progressi computazionali alla base di machine learning hanno contribuito allo sviluppo dell'intelligenza artificiale. È utile ricordare che, mentre questi campi come distinte aree di indagine hanno cominciato a cristallizzarsi negli anni '50, importanti [scoperte algoritmiche, statistiche, matematiche, computazionali e tecniche](https://wikipedia.org/wiki/Timeline_of_machine_learning) hanno preceduto e si sono sovrapposte a questa era. In effetti, le persone hanno riflettuto su queste domande per [centinaia di anni](https://wikipedia.org/wiki/History_of_artificial_intelligence); questo articolo discute le basi intellettuali storiche dell'idea di una "macchina pensante".
-
-## Scoperte rilevanti
-
-- 1763, 1812 [Teorema di Bayes](https://it.wikipedia.org/wiki/Teorema_di_Bayes) e suoi predecessori. Questo teorema e le sue applicazioni sono alla base dell'inferenza, descrivendo la probabilità che un evento si verifichi in base alla conoscenza precedente.
-- 1805 [Metodo dei Minimi Quadrati](https://it.wikipedia.org/wiki/Metodo_dei_minimi_quadrati) del matematico francese Adrien-Marie Legendre. Questa teoria, che verrà trattata nell'unità Regressione, aiuta nell'adattamento dei dati.
-- 1913 [Processo Markoviano](https://it.wikipedia.org/wiki/Processo_markoviano) dal nome del matematico russo Andrey Markov è usato per descrivere una sequenza di possibili eventi basati su uno stato precedente.
-- 1957 [Percettrone](https://it.wikipedia.org/wiki/Percettrone) è un tipo di classificatore lineare inventato dallo psicologo americano Frank Rosenblatt che sta alla base dei progressi nel deep learning.
-- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) è un algoritmo originariamente progettato per mappare i percorsi. In un contesto ML viene utilizzato per rilevare i modelli.
-- 1970 [La Retropropagazione dell'Errore](https://it.wikipedia.org/wiki/Retropropagazione_dell'errore) viene utilizzata per addestrare [le reti neurali feed-forward](https://it.wikipedia.org/wiki/Rete_neurale_feed-forward).
-- Le [Reti Neurali Ricorrenti](https://it.wikipedia.org/wiki/Rete_neurale_ricorrente) del 1982 sono reti neurali artificiali derivate da reti neurali feedforward che creano grafici temporali.
-
-✅ Fare una piccola ricerca. Quali altre date si distinguono come fondamentali nella storia del machine learning e dell'intelligenza artificiale?
-## 1950: Macchine che pensano
-
-Alan Turing, una persona davvero notevole che è stata votata [dal pubblico nel 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) come il più grande scienziato del XX secolo, è accreditato per aver contribuito a gettare le basi per il concetto di "macchina in grado di pensare". Ha affrontato gli oppositori e il suo stesso bisogno di prove empiriche di questo concetto in parte creando il [Test di Turing](https://www.bbc.com/news/technology-18475646), che verrà esplorato nelle lezioni di NLP (elaborazione del linguaggio naturale).
-
-## 1956: Progetto di Ricerca Estivo Dartmouth
-
-"Il Dartmouth Summer Research Project sull'intelligenza artificiale è stato un evento seminale per l'intelligenza artificiale come campo", qui è stato coniato il termine "intelligenza artificiale" ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
-
-> In linea di principio, ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza può essere descritto in modo così preciso che si può costruire una macchina per simularlo.
-
-Il ricercatore capo, il professore di matematica John McCarthy, sperava "di procedere sulla base della congettura che ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza possa in linea di principio essere descritta in modo così preciso che si possa costruire una macchina per simularlo". I partecipanti includevano un altro luminare nel campo, Marvin Minsky.
-
-Il workshop è accreditato di aver avviato e incoraggiato diverse discussioni tra cui "l'ascesa di metodi simbolici, sistemi focalizzati su domini limitati (primi sistemi esperti) e sistemi deduttivi contro sistemi induttivi". ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)).
-
-## 1956 - 1974: "Gli anni d'oro"
-
-Dagli anni '50 fino alla metà degli anni '70, l'ottimismo era alto nella speranza che l'AI potesse risolvere molti problemi. Nel 1967, Marvin Minsky dichiarò con sicurezza che "Entro una generazione... il problema della creazione di 'intelligenza artificiale' sarà sostanzialmente risolto". (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
-
-La ricerca sull'elaborazione del linguaggio naturale è fiorita, la ricerca è stata perfezionata e resa più potente ed è stato creato il concetto di "micro-mondi", in cui compiti semplici sono stati completati utilizzando istruzioni in linguaggio semplice.
-
-La ricerca è stata ben finanziata dalle agenzie governative, sono stati fatti progressi nel calcolo e negli algoritmi e sono stati costruiti prototipi di macchine intelligenti. Alcune di queste macchine includono:
-
-* [Shakey il robot](https://wikipedia.org/wiki/Shakey_the_robot), che poteva manovrare e decidere come eseguire i compiti "intelligentemente".
-
-   ![Shakey, un robot intelligente](../images/shakey.jpg)
-   > Shakey nel 1972
-
-* Eliza, una delle prime "chatterbot", poteva conversare con le persone e agire come una "terapeuta" primitiva. Si Imparerà di più su Eliza nelle lezioni di NLP.
-
-   ![Eliza, un bot](../images/eliza.png)
-   > Una versione di Eliza, un chatbot
-
-* Il "mondo dei blocchi" era un esempio di un micromondo in cui i blocchi potevano essere impilati e ordinati e si potevano testare esperimenti su macchine per insegnare a prendere decisioni. I progressi realizzati con librerie come [SHRDLU](https://it.wikipedia.org/wiki/SHRDLU) hanno contribuito a far progredire l'elaborazione del linguaggio.
-
-    [![Il mondo dei blocchi con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Il mondo dei blocchi con SHRDLU")
-
-   > 🎥 Fare clic sull'immagine sopra per un video: Blocks world con SHRDLU
-
-## 1974 - 1980: "L'inverno dell'AI"
-
-Verso la metà degli anni '70, era diventato evidente che la complessità della creazione di "macchine intelligenti" era stata sottovalutata e che la sua promessa, data la potenza di calcolo disponibile, era stata esagerata. I finanziamenti si sono prosciugati e la fiducia nel settore è rallentata. Alcuni problemi che hanno influito sulla fiducia includono:
-
-- **Limitazioni**. La potenza di calcolo era troppo limitata.
-- **Esplosione combinatoria**. La quantità di parametri necessari per essere addestrati è cresciuta in modo esponenziale man mano che veniva chiesto di più ai computer, senza un'evoluzione parallela della potenza e delle capacità di calcolo.
-- **Scarsità di dati**. C'era una scarsità di dati che ostacolava il processo di test, sviluppo e perfezionamento degli algoritmi.
-- **Stiamo facendo le domande giuste?**. Le stesse domande che venivano poste cominciarono ad essere messe in discussione. I ricercatori hanno iniziato a criticare i loro approcci:
-   - I test di Turing furono messi in discussione attraverso, tra le altre idee, la "teoria della stanza cinese" che postulava che "la programmazione di un computer digitale può far sembrare che capisca il linguaggio ma non potrebbe produrre una vera comprensione". ([fonte](https://plato.stanford.edu/entries/chinese-room/))
-   - L'etica dell'introduzione di intelligenze artificiali come la "terapeuta" ELIZA nella società è stata messa in discussione.
-
-Allo stesso tempo, iniziarono a formarsi varie scuole di pensiero sull'AI. È stata stabilita una dicotomia tra pratiche ["scruffy" contro "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). I laboratori _scruffy_ ottimizzavano i programmi per ore fino a quando non ottenevano i risultati desiderati. I laboratori _Neat_ "si focalizzavano sulla logica e sulla risoluzione formale dei problemi". ELIZA e SHRDLU erano ben noti _sistemi scruffy_. Negli anni '80, quando è emersa la richiesta di rendere riproducibili i sistemi ML, l'_approccio neat_ ha gradualmente preso il sopravvento in quanto i suoi risultati sono più spiegabili.
-
-## Sistemi esperti degli anni '80
-
-Man mano che il settore cresceva, i suoi vantaggi per le imprese diventavano più chiari e negli anni '80 lo stesso accadeva con la proliferazione di "sistemi esperti". "I sistemi esperti sono stati tra le prime forme di software di intelligenza artificiale (AI) di vero successo". ([fonte](https://wikipedia.org/wiki/Expert_system)).
-
-Questo tipo di sistema è in realtà _ibrido_, costituito in parte da un motore di regole che definisce i requisiti aziendali e un motore di inferenza che sfrutta il sistema di regole per dedurre nuovi fatti.
-
-Questa era ha visto anche una crescente attenzione rivolta alle reti neurali.
-
-## 1987 - 1993: AI 'Chill'
-
-La proliferazione di hardware specializzato per sistemi esperti ha avuto lo sfortunato effetto di diventare troppo specializzato. L'ascesa dei personal computer ha anche gareggiato con questi grandi sistemi centralizzati specializzati. La democratizzazione dell'informatica era iniziata e alla fine ha spianato la strada alla moderna esplosione dei big data.
-
-## 1993 - 2011
-
-Questa epoca ha visto una nuova era per ML e AI per essere in grado di risolvere alcuni dei problemi che erano stati causati in precedenza dalla mancanza di dati e potenza di calcolo. La quantità di dati ha iniziato ad aumentare rapidamente e a diventare più ampiamente disponibile, nel bene e nel male, soprattutto con l'avvento degli smartphone intorno al 2007. La potenza di calcolo si è ampliata in modo esponenziale e gli algoritmi si sono evoluti di pari passo. Il campo ha iniziato a maturare quando i giorni a ruota libera del passato hanno iniziato a cristallizzarsi in una vera disciplina.
-
-## Adesso
-
-Oggi, machine learning e intelligenza artificiale toccano quasi ogni parte della nostra vita. Questa era richiede un'attenta comprensione dei rischi e dei potenziali effetti di questi algoritmi sulle vite umane. Come ha affermato Brad Smith di Microsoft, "La tecnologia dell'informazione solleva questioni che vanno al cuore delle protezioni fondamentali dei diritti umani come la privacy e la libertà di espressione. Questi problemi aumentano la responsabilità delle aziende tecnologiche che creano questi prodotti. A nostro avviso, richiedono anche un'attenta regolamentazione del governo e lo sviluppo di norme sugli usi accettabili" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
-
-Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi informatici e il software e gli algoritmi che eseguono. Ci si augura che questo programma di studi aiuti ad acquisire una migliore comprensione in modo che si possa decidere in autonomia.
-
-[![La storia del deeplearningLa](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 " storia del deep learning")
-> 🎥 Fare clic sull'immagine sopra per un video: Yann LeCun discute la storia del deep learning in questa lezione
-
----
-
-## 🚀 Sfida
-
-Approfondire uno di questi momenti storici e scoprire
- di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
-
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
-
-## Revisione e Auto Apprendimento
-
-Ecco gli elementi da guardare e ascoltare:
-
-[Questo podcast in cui Amy Boyd discute l'evoluzione dell'AI](http://runasradio.com/Shows/Show/739)
-
-[![La storia dell'AI di Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La storia dell'AI di Amy Boyd")
-
-## Compito
-
-[Creare una sequenza temporale](assignment.it.md)
+# Storia di machine learning
+
+![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/?loc=it)
+
+In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
+
+La storia dell'intelligenza artificiale, AI, come campo è intrecciata con la storia di machine learning, poiché gli algoritmi e i progressi computazionali alla base di machine learning hanno contribuito allo sviluppo dell'intelligenza artificiale. È utile ricordare che, mentre questi campi come distinte aree di indagine hanno cominciato a cristallizzarsi negli anni '50, importanti [scoperte algoritmiche, statistiche, matematiche, computazionali e tecniche](https://wikipedia.org/wiki/Timeline_of_machine_learning) hanno preceduto e si sono sovrapposte a questa era. In effetti, le persone hanno riflettuto su queste domande per [centinaia di anni](https://wikipedia.org/wiki/History_of_artificial_intelligence); questo articolo discute le basi intellettuali storiche dell'idea di una "macchina pensante".
+
+## Scoperte rilevanti
+
+- 1763, 1812 [Teorema di Bayes](https://it.wikipedia.org/wiki/Teorema_di_Bayes) e suoi predecessori. Questo teorema e le sue applicazioni sono alla base dell'inferenza, descrivendo la probabilità che un evento si verifichi in base alla conoscenza precedente.
+- 1805 [Metodo dei Minimi Quadrati](https://it.wikipedia.org/wiki/Metodo_dei_minimi_quadrati) del matematico francese Adrien-Marie Legendre. Questa teoria, che verrà trattata nell'unità Regressione, aiuta nell'adattamento dei dati.
+- 1913 [Processo Markoviano](https://it.wikipedia.org/wiki/Processo_markoviano) dal nome del matematico russo Andrey Markov è usato per descrivere una sequenza di possibili eventi basati su uno stato precedente.
+- 1957 [Percettrone](https://it.wikipedia.org/wiki/Percettrone) è un tipo di classificatore lineare inventato dallo psicologo americano Frank Rosenblatt che sta alla base dei progressi nel deep learning.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) è un algoritmo originariamente progettato per mappare i percorsi. In un contesto ML viene utilizzato per rilevare i modelli.
+- 1970 [La Retropropagazione dell'Errore](https://it.wikipedia.org/wiki/Retropropagazione_dell'errore) viene utilizzata per addestrare [le reti neurali feed-forward](https://it.wikipedia.org/wiki/Rete_neurale_feed-forward).
+- Le [Reti Neurali Ricorrenti](https://it.wikipedia.org/wiki/Rete_neurale_ricorrente) del 1982 sono reti neurali artificiali derivate da reti neurali feedforward che creano grafici temporali.
+
+✅ Fare una piccola ricerca. Quali altre date si distinguono come fondamentali nella storia del machine learning e dell'intelligenza artificiale?
+## 1950: Macchine che pensano
+
+Alan Turing, una persona davvero notevole che è stata votata [dal pubblico nel 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) come il più grande scienziato del XX secolo, è accreditato per aver contribuito a gettare le basi per il concetto di "macchina in grado di pensare". Ha affrontato gli oppositori e il suo stesso bisogno di prove empiriche di questo concetto in parte creando il [Test di Turing](https://www.bbc.com/news/technology-18475646), che verrà esplorato nelle lezioni di NLP (elaborazione del linguaggio naturale).
+
+## 1956: Progetto di Ricerca Estivo Dartmouth
+
+"Il Dartmouth Summer Research Project sull'intelligenza artificiale è stato un evento seminale per l'intelligenza artificiale come campo", qui è stato coniato il termine "intelligenza artificiale" ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> In linea di principio, ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza può essere descritto in modo così preciso che si può costruire una macchina per simularlo.
+
+Il ricercatore capo, il professore di matematica John McCarthy, sperava "di procedere sulla base della congettura che ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza possa in linea di principio essere descritta in modo così preciso che si possa costruire una macchina per simularlo". I partecipanti includevano un altro luminare nel campo, Marvin Minsky.
+
+Il workshop è accreditato di aver avviato e incoraggiato diverse discussioni tra cui "l'ascesa di metodi simbolici, sistemi focalizzati su domini limitati (primi sistemi esperti) e sistemi deduttivi contro sistemi induttivi". ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Gli anni d'oro"
+
+Dagli anni '50 fino alla metà degli anni '70, l'ottimismo era alto nella speranza che l'AI potesse risolvere molti problemi. Nel 1967, Marvin Minsky dichiarò con sicurezza che "Entro una generazione... il problema della creazione di 'intelligenza artificiale' sarà sostanzialmente risolto". (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La ricerca sull'elaborazione del linguaggio naturale è fiorita, la ricerca è stata perfezionata e resa più potente ed è stato creato il concetto di "micro-mondi", in cui compiti semplici sono stati completati utilizzando istruzioni in linguaggio semplice.
+
+La ricerca è stata ben finanziata dalle agenzie governative, sono stati fatti progressi nel calcolo e negli algoritmi e sono stati costruiti prototipi di macchine intelligenti. Alcune di queste macchine includono:
+
+* [Shakey il robot](https://wikipedia.org/wiki/Shakey_the_robot), che poteva manovrare e decidere come eseguire i compiti "intelligentemente".
+
+   ![Shakey, un robot intelligente](../images/shakey.jpg)
+   > Shakey nel 1972
+
+* Eliza, una delle prime "chatterbot", poteva conversare con le persone e agire come una "terapeuta" primitiva. Si Imparerà di più su Eliza nelle lezioni di NLP.
+
+   ![Eliza, un bot](../images/eliza.png)
+   > Una versione di Eliza, un chatbot
+
+* Il "mondo dei blocchi" era un esempio di un micromondo in cui i blocchi potevano essere impilati e ordinati e si potevano testare esperimenti su macchine per insegnare a prendere decisioni. I progressi realizzati con librerie come [SHRDLU](https://it.wikipedia.org/wiki/SHRDLU) hanno contribuito a far progredire l'elaborazione del linguaggio.
+
+    [![Il mondo dei blocchi con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Il mondo dei blocchi con SHRDLU")
+
+   > 🎥 Fare clic sull'immagine sopra per un video: Blocks world con SHRDLU
+
+## 1974 - 1980: "L'inverno dell'AI"
+
+Verso la metà degli anni '70, era diventato evidente che la complessità della creazione di "macchine intelligenti" era stata sottovalutata e che la sua promessa, data la potenza di calcolo disponibile, era stata esagerata. I finanziamenti si sono prosciugati e la fiducia nel settore è rallentata. Alcuni problemi che hanno influito sulla fiducia includono:
+
+- **Limitazioni**. La potenza di calcolo era troppo limitata.
+- **Esplosione combinatoria**. La quantità di parametri necessari per essere addestrati è cresciuta in modo esponenziale man mano che veniva chiesto di più ai computer, senza un'evoluzione parallela della potenza e delle capacità di calcolo.
+- **Scarsità di dati**. C'era una scarsità di dati che ostacolava il processo di test, sviluppo e perfezionamento degli algoritmi.
+- **Stiamo facendo le domande giuste?**. Le stesse domande che venivano poste cominciarono ad essere messe in discussione. I ricercatori hanno iniziato a criticare i loro approcci:
+   - I test di Turing furono messi in discussione attraverso, tra le altre idee, la "teoria della stanza cinese" che postulava che "la programmazione di un computer digitale può far sembrare che capisca il linguaggio ma non potrebbe produrre una vera comprensione". ([fonte](https://plato.stanford.edu/entries/chinese-room/))
+   - L'etica dell'introduzione di intelligenze artificiali come la "terapeuta" ELIZA nella società è stata messa in discussione.
+
+Allo stesso tempo, iniziarono a formarsi varie scuole di pensiero sull'AI. È stata stabilita una dicotomia tra pratiche ["scruffy" contro "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). I laboratori _scruffy_ ottimizzavano i programmi per ore fino a quando non ottenevano i risultati desiderati. I laboratori _Neat_ "si focalizzavano sulla logica e sulla risoluzione formale dei problemi". ELIZA e SHRDLU erano ben noti _sistemi scruffy_. Negli anni '80, quando è emersa la richiesta di rendere riproducibili i sistemi ML, l'_approccio neat_ ha gradualmente preso il sopravvento in quanto i suoi risultati sono più spiegabili.
+
+## Sistemi esperti degli anni '80
+
+Man mano che il settore cresceva, i suoi vantaggi per le imprese diventavano più chiari e negli anni '80 lo stesso accadeva con la proliferazione di "sistemi esperti". "I sistemi esperti sono stati tra le prime forme di software di intelligenza artificiale (AI) di vero successo". ([fonte](https://wikipedia.org/wiki/Expert_system)).
+
+Questo tipo di sistema è in realtà _ibrido_, costituito in parte da un motore di regole che definisce i requisiti aziendali e un motore di inferenza che sfrutta il sistema di regole per dedurre nuovi fatti.
+
+Questa era ha visto anche una crescente attenzione rivolta alle reti neurali.
+
+## 1987 - 1993: AI 'Chill'
+
+La proliferazione di hardware specializzato per sistemi esperti ha avuto lo sfortunato effetto di diventare troppo specializzato. L'ascesa dei personal computer ha anche gareggiato con questi grandi sistemi centralizzati specializzati. La democratizzazione dell'informatica era iniziata e alla fine ha spianato la strada alla moderna esplosione dei big data.
+
+## 1993 - 2011
+
+Questa epoca ha visto una nuova era per ML e AI per essere in grado di risolvere alcuni dei problemi che erano stati causati in precedenza dalla mancanza di dati e potenza di calcolo. La quantità di dati ha iniziato ad aumentare rapidamente e a diventare più ampiamente disponibile, nel bene e nel male, soprattutto con l'avvento degli smartphone intorno al 2007. La potenza di calcolo si è ampliata in modo esponenziale e gli algoritmi si sono evoluti di pari passo. Il campo ha iniziato a maturare quando i giorni a ruota libera del passato hanno iniziato a cristallizzarsi in una vera disciplina.
+
+## Adesso
+
+Oggi, machine learning e intelligenza artificiale toccano quasi ogni parte della nostra vita. Questa era richiede un'attenta comprensione dei rischi e dei potenziali effetti di questi algoritmi sulle vite umane. Come ha affermato Brad Smith di Microsoft, "La tecnologia dell'informazione solleva questioni che vanno al cuore delle protezioni fondamentali dei diritti umani come la privacy e la libertà di espressione. Questi problemi aumentano la responsabilità delle aziende tecnologiche che creano questi prodotti. A nostro avviso, richiedono anche un'attenta regolamentazione del governo e lo sviluppo di norme sugli usi accettabili" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi informatici e il software e gli algoritmi che eseguono. Ci si augura che questo programma di studi aiuti ad acquisire una migliore comprensione in modo che si possa decidere in autonomia.
+
+[![La storia del deeplearningLa](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 " storia del deep learning")
+> 🎥 Fare clic sull'immagine sopra per un video: Yann LeCun discute la storia del deep learning in questa lezione
+
+---
+
+## 🚀 Sfida
+
+Approfondire uno di questi momenti storici e scoprire
+ di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Ecco gli elementi da guardare e ascoltare:
+
+[Questo podcast in cui Amy Boyd discute l'evoluzione dell'AI](http://runasradio.com/Shows/Show/739)
+
+[![La storia dell'AI di Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La storia dell'AI di Amy Boyd")
+
+## Compito
+
+[Creare una sequenza temporale](assignment.it.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index f9b4c0457..6ba32096a 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -3,7 +3,7 @@
 ![機械学習の歴史をまとめたスケッチ](../../../sketchnotes/ml-history.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ja)
 
 この授業では、機械学習と人工知能の歴史における主要な出来事を紹介します。
 
@@ -99,7 +99,7 @@
 
 これらの歴史的瞬間の1つを掘り下げて、その背後にいる人々について学びましょう。魅力的な人々がいますし、文化的に空白の状態で科学的発見がなされたことはありません。どういったことが見つかるでしょうか？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ja)
 
 ## 振り返りと自習
 
@@ -111,4 +111,4 @@
 
 ## 課題
 
-[時系列を制作してください](../assignment.md)
+[年表を作成する](./assignment.ja.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.ko.md b/1-Introduction/2-history-of-ML/translations/README.ko.md
new file mode 100644
index 000000000..d630201e2
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/README.ko.md
@@ -0,0 +1,118 @@
+# 머신러닝의 역사
+
+![Summary of History of machine learning in a sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+
+이 강의에서, 머신러닝과 인공 지능의 역사에서 주요 마일스톤을 살펴보려 합니다.
+
+인공 지능, AI의 역사는 머신러닝의 역사와 서로 엮여 있으며, ML을 받쳐주는 알고리즘과 계산 기술이 AI의 개발에 기여했습니다. 독특한 탐구 영역으로 이런 분야는 1950년에 구체적으로 시작했지만, 중요한 [algorithmical, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning)로 이 시대를 오버랩했다고 생각하는 게 유용합니다. 실제로, 사람들은 [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence)동안 이 질문을 생각해왔습니다: 이 아티클은 'thinking machine'라는 개념의 역사적 지적 토대에 대하여 이야기 합니다.
+
+## 주목할 발견
+
+- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem)과 전임자. 이 정리와 적용은 사전지식 기반으로 이벤트가 발생할 확률을 설명할 추론의 기초가 됩니다.
+- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by 프랑스 수학자 Adrien-Marie Legendre. Regression 단위에서 배울 이 이론은, 데이터 피팅에 도움이 됩니다.
+- 1913 러시아 수학자 Andrey Markov의 이름에서 유래된 [Markov Chains](https://wikipedia.org/wiki/Markov_chain)는 이전 상태를 기반으로 가능한 이벤트의 시퀀스를 설명하는 데 사용됩니다.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron)은 미국 심리학자 Frank Rosenblatt이 개발한 linear classifier의 한 타입으로 딥러닝 발전을 뒷받칩니다.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor)는 원래 경로를 맵핑하기 위한 알고리즘입니다. ML context에서 패턴을 감지할 때 사용합니다.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation)은 [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network)를 학습할 때 사용합니다.
+- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network)는 시간 그래프를 생성하는 feedforward neural networks에서 파생한 인공 신경망입니다.
+
+✅ 조금 조사해보세요. ML과 AI의 역사에서 중요한 다른 날짜는 언제인가요?
+
+## 1950: 생각하는 기계
+
+20세기의 최고 과학자로 [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century)에 선택된, Alan Turing은, 'machine that can think.'라는 개념의 기반을 구축하는 데에 기여한 것으로 평가되고 있습니다.
+NLP 강의에서 살필 [Turing Test](https://www.bbc.com/news/technology-18475646)를 만들어서 부분적으로 이 개념에 대한 경험적인 반대하는 사람들과 대립했습니다.
+
+## 1956: Dartmouth 여름 연구 프로젝트
+
+"The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth))에서 "인공 지능"이라는 용어가 만들어졌습니다.
+
+> 학습의 모든 측면이나 지능의 다른 기능은 원칙적으로 정확하게 서술할 수 있어서 이를 따라 할 기계를 만들 수 있습니다.
+
+수석 연구원인, 수학 교수 John McCarthy는, "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."이라고 희망했습니다. 참가한 사람들 중에서는 Marvin Minsky도 있었습니다.
+
+이 워크숍은 "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems." ([source](https://wikipedia.org/wiki/Dartmouth_workshop))을 포함해서 여러 토론을 시작하고 장려한 것으로 평가됩니다.
+
+## 1956 - 1974: "The golden years"
+
+1950년대부터 70년대 중순까지 AI로 많은 문제를 해결할 수 있다고 믿은 낙관주의가 커졌습니다. 1967년  Marvin Minsky는 "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)이라고 자신있게 말했습니다.
+
+natural language processing 연구가 발전하고, 검색이 개선되어 더 강력해졌으며, 단순한 언어 지침으로 간단한 작업을 완료하는 'micro-worlds'라는 개념이 생겼습니다.
+
+정부 지원을 받으며 연구했으며, 계산과 알고리즘이 발전하면서, 지능적 기계의 프로토 타입이 만들어졌습니다. 이런 기계 중에 일부는 아래와 같습니다:
+
+* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), '지능적'으로 작업하는 방법을 조종하고 결정할 수 있습니다.
+
+    ![Shakey, an intelligent robot](../images/shakey.jpg)
+    > Shakey in 1972
+
+* 초기 'chatterbot'인, Eliza는, 사람들과 이야기하고 원시적 '치료사' 역할을 할 수 있었습니다. NLP 강의에서 Eliza에 대하여 자세히 알아봅시다. 
+
+    ![Eliza, a bot](../images/eliza.png)
+    > A version of Eliza, a chatbot
+
+* "Blocks world"는 블록을 쌓고 분류할 수 있는 마이크로-월드의 예시이며, 결정하는 기계를 가르칠 실험을 테스트할 수 있었습니다. [SHRDLU](https://wikipedia.org/wiki/SHRDLU)와 같은 라이브러리로 만들어진 발명은 language processing를 발전시키는 데 도움이 되었습니다.
+
+    [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
+    
+    > 🎥 영상을 보려면 이미지 클릭: Blocks world with SHRDLU
+
+## 1974 - 1980: "AI Winter"
+
+1970년 중순에, '인공 기계'를 만드는 복잡도가 과소 평가되면서, 주어진 컴퓨터 파워를 고려해보니, 그 약속은 과장된 것이 분명해졌습니다. 자금이 고갈되고 현장에 대한 자신감도 느려졌습니다. 신뢰에 영향을 준 이슈는 아래에 있습니다:
+
+- **제한**. 컴퓨터 성능이 너무 제한되었습니다.
+- **결합 파열**. 훈련에 필요한 파라미터의 양이 컴퓨터 성능, 기능과 별개로 컴퓨터의 요청에 따라 늘어났습니다.
+- **데이터 부족**. 알고리즘을 테스트, 개발, 그리고 개선할 수 없게 데이터가 부족했습니다.
+- **올바른 질문인가요?**. 질문받은 그 질문에 바로 물었습니다. 연구원들은 그 접근 방식에 비판했습니다:
+  - 튜링 테스트는 "programming a digital computer may make it appear to understand language but could not produce real understanding." ([source](https://plato.stanford.edu/entries/chinese-room/))하다고 가정한, 'chinese room theory'의 다른 아이디어에 의해 의문이 생겼습니다.
+  - "치료사" ELIZA와 같은 인공 지능을 사회에 도입하며 윤리에 도전했습니다.
+
+동 시간대에, 다양한 AI 학교가 형성되기 시작했습니다. ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) 사이에 이분법이 확립되었습니다. _Scruffy_ 연구실은 원하는 결과를 얻을 때까지 몇 시간 동안 프로그램을 트윅했습니다. _Neat_ 연구실은 논리와 공식적 문제를 해결하는 데에 초점을 맞추었습니다. ELIZA와 SHRDLU는 잘 알려진 _scruffy_ 시스템입니다. 1980년대에, ML 시스템을 재현할 수 있어야 된다는 요구사항이 생겼고, _neat_ 방식이 더 결과를 설명할 수 있어서 점차 선두를 차지했습니다. 
+
+## 1980s 전문가 시스템
+
+이 분야가 성장하며, 비즈니스에 대한 이점이 명확해졌고, 1980년대에 '전문가 시스템'이 확산되었습니다. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)).
+
+이 시스템의 타입은, 실제로 비즈니스 요구사항을 정의하는 룰 엔진과 새로운 사실 추론하는 룰 시스템을 활용한 추론 엔진으로 부분적 구성된 _hybrid_ 입니다.
+
+이런 시대에도 neural networks에 대한 관심이 늘어났습니다.
+
+## 1987 - 1993: AI 'Chill'
+
+전문화된 전문가 시스템 하드웨어의 확산은 너무나도 고차원되는 불운한 결과를 가져왔습니다. 개인용 컴퓨터의 부상은 크고, 전문화된, 중앙화 시스템과 경쟁했습니다. 컴퓨팅의 민주화가 시작되었고, 결국 현대의 빅 데이터 폭발을 위한 길을 열었습니다.
+
+## 1993 - 2011
+
+이 시대에는 ML과 AI가 과거 데이터와 컴퓨터 파워 부족으로 인해 발생했던 문제 중 일부 해결할 수 있는 새로운 시대가 열렸습니다. 데이터의 양은 급격히 늘어나기 시작했고, 2007년에 스마트폰이 나오면서 좋든 나쁘든 더 넓게 사용할 수 있게 되었습니다. 컴퓨터 파워는 크게 확장되었고, 알고리즘도 함께 발전했습니다. 과거 자유롭던 시대에서 진정한 규율로 이 분야는 성숙해지기 시작했습니다.
+
+## 현재
+
+오늘 날, 머신러닝과 AI는 인생의 대부분에 영향을 미칩니다. 이 시대에는 이러한 알고리즘이 인간의 인생에 미치는 위험과 잠재적인 영향에 대한 주의깊은 이해도가 요구됩니다. Microsoft의 Brad Smith가 언급합니다 "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+미래가 어떻게 변할지 알 수 없지만, 컴퓨터 시스템과 이를 실행하는 소프트웨어와 알고리즘을 이해하는 것은 중요합니다. 이 커리큘럼으로 더 잘 이해하고 스스로 결정할 수 있게 되기를 바랍니다.
+
+[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
+> 🎥 영상 보려면 위 이미지 클릭: Yann LeCun이 강의에서 딥러닝의 역사를 이야기 합니다.
+
+---
+## 🚀 도전
+
+역사적인 순간에 사람들 뒤에서 한 가지를 집중적으로 파고 있는 자를 자세히 알아보세요. 매력있는 캐릭터가 있으며, 문화가 사라진 곳에서는 과학적인 발견을 하지 못합니다. 당신은 어떤 발견을 해보았나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+
+## 검토 & 자기주도 학습
+
+보고 들을 수 있는 항목은 아래와 같습니다:
+
+[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
+
+[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+
+## 과제
+
+[Create a timeline](../assignment.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.tr.md b/1-Introduction/2-history-of-ML/translations/README.tr.md
index a67f45ec4..af2346fb7 100644
--- a/1-Introduction/2-history-of-ML/translations/README.tr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.tr.md
@@ -3,7 +3,7 @@
 ![Bir taslak-notta makine öğrenimi geçmişinin özeti](../../../sketchnotes/ml-history.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac) tarafından hazırlanan taslak-not
 
-## [Ders öncesi test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=tr)
+## [Ders öncesi test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=tr)
 
 Bu derste, makine öğrenimi ve yapay zeka tarihindeki önemli kilometre taşlarını inceleyeceğiz.
 
@@ -102,7 +102,7 @@ Geleceğin neler getireceğini birlikte göreceğiz, ancak bu bilgisayar sisteml
 
 Bu tarihi anlardan birine girin ve arkasındaki insanlar hakkında daha fazla bilgi edinin. Büyüleyici karakterler var ve kültürel bir boşlukta hiçbir bilimsel keşif yaratılmadı. Ne keşfedersiniz?
 
-## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=tr)
+## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=tr)
 
 ## İnceleme ve Bireysel Çalışma
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index 51e66ecd2..65ace971b 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -1,116 +1,116 @@
 # 机器学习的历史 
 
 ![机器学习历史概述](../../../sketchnotes/ml-history.png)
-> 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
+> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 在本课中，我们将走过机器学习和人工智能历史上的主要里程碑。 
 
-人工智能（AI）作为一个领域的历史与机器学习的历史交织在一起，因为支持机器学习的算法和计算能力的进步推动了AI的发展。记住，虽然这些领域作为不同研究领域在20世纪50年代才开始具体化，但重要的[算法、统计、数学、计算和技术发现](https://wikipedia.org/wiki/Timeline_of_machine_learning) 要早于和重叠了这个时代。 事实上，[数百年来](https://wikipedia.org/wiki/History_of_artificial_intelligence)人们一直在思考这些问题：本文讨论了“思维机器”这一概念的历史知识基础。 
+人工智能（AI）作为一个领域的历史与机器学习的历史交织在一起，因为支持机器学习的算法和计算能力的进步推动了AI的发展。记住，虽然这些领域作为不同研究领域在 20 世纪 50 年代才开始具体化，但重要的[算法、统计、数学、计算和技术发现](https://wikipedia.org/wiki/Timeline_of_machine_learning) 要早于和重叠了这个时代。 事实上，[数百年来](https://wikipedia.org/wiki/History_of_artificial_intelligence)人们一直在思考这些问题：本文讨论了“思维机器”这一概念的历史知识基础。 
 
 ## 主要发现
 
 - 1763, 1812 [贝叶斯定理](https://wikipedia.org/wiki/Bayes%27_theorem) 及其前身。该定理及其应用是推理的基础，描述了基于先验知识的事件发生的概率。
-- 1805 [最小二乘理论](https://wikipedia.org/wiki/Least_squares)由法国数学家Adrien-Marie Legendre提出。 你将在我们的回归单元中了解这一理论，它有助于数据拟合。
-- 1913 [马尔可夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家Andrey Markov的名字命名，用于描述基于先前状态的一系列可能事件。
-- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心理学家Frank Rosenblatt发明的一种线性分类器，是深度学习发展的基础。
-- 1967 [最近邻](https://wikipedia.org/wiki/Nearest_neighbor)是一种最初设计用于映射路线的算法。 在ML中，它用于检测模式。
+- 1805 [最小二乘理论](https://wikipedia.org/wiki/Least_squares)由法国数学家 Adrien-Marie Legendre 提出。 你将在我们的回归单元中了解这一理论，它有助于数据拟合。
+- 1913 [马尔可夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家 Andrey Markov 的名字命名，用于描述基于先前状态的一系列可能事件。
+- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心理学家 Frank Rosenblatt 发明的一种线性分类器，是深度学习发展的基础。
+- 1967 [最近邻](https://wikipedia.org/wiki/Nearest_neighbor)是一种最初设计用于映射路线的算法。 在 ML 中，它用于检测模式。
 - 1970 [反向传播](https://wikipedia.org/wiki/Backpropagation)用于训练[前馈神经网络](https://wikipedia.org/wiki/Feedforward_neural_network)。
 - 1982 [循环神经网络](https://wikipedia.org/wiki/Recurrent_neural_network) 是源自产生时间图的前馈神经网络的人工神经网络。
 
-✅ 做点调查。在ML和AI的历史上，还有哪些日期是重要的？
+✅ 做点调查。在 ML 和 AI 的历史上，还有哪些日期是重要的？
 ## 1950: 会思考的机器 
 
-Alan Turing，一个真正杰出的人，[在2019年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为20世纪最伟大的科学家，他认为有助于为“会思考的机器”的概念打下基础。他通过创建 [图灵测试](https://www.bbc.com/news/technology-18475646)来解决反对者和他自己对这一概念的经验证据的需求，你将在我们的 NLP 课程中进行探索。
+Alan Turing，一个真正杰出的人，[在 2019 年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为 20 世纪最伟大的科学家，他认为有助于为“会思考的机器”的概念打下基础。他通过创建 [图灵测试](https://www.bbc.com/news/technology-18475646)来解决反对者和他自己对这一概念的经验证据的需求，你将在我们的 NLP 课程中进行探索。
 
 ## 1956: 达特茅斯夏季研究项目
 
-“达特茅斯夏季人工智能研究项目是人工智能领域的一个开创性事件，”正是在这里，人们创造了“人工智能”一词（[来源](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth))。
+“达特茅斯夏季人工智能研究项目是人工智能领域的一个开创性事件，”正是在这里，人们创造了“人工智能”一词（[来源](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)）。
 
 > 原则上，学习的每个方面或智能的任何其他特征都可以被精确地描述，以至于可以用机器来模拟它。 
 
-首席研究员、数学教授John McCarthy希望“基于这样一种猜想，即学习的每个方面或智能的任何其他特征原则上都可以如此精确地描述，以至于可以制造出一台机器来模拟它。” 参与者包括该领域的另一位杰出人物Marvin Minsky。
+首席研究员、数学教授 John McCarthy 希望“基于这样一种猜想，即学习的每个方面或智能的任何其他特征原则上都可以如此精确地描述，以至于可以制造出一台机器来模拟它。” 参与者包括该领域的另一位杰出人物 Marvin Minsky。
 
-研讨会被认为发起并鼓励了一些讨论，包括“符号方法的兴起、专注于有限领域的系统（早期专家系统），以及演绎系统与归纳系统的对比。”([来源](https://wikipedia.org/wiki/Dartmouth_workshop))。
+研讨会被认为发起并鼓励了一些讨论，包括“符号方法的兴起、专注于有限领域的系统（早期专家系统），以及演绎系统与归纳系统的对比。”（[来源](https://wikipedia.org/wiki/Dartmouth_workshop)）。
 
 ## 1956 - 1974: “黄金岁月”
 
-从20世纪50年代到70年代中期，乐观情绪高涨，希望人工智能能够解决许多问题。1967年，Marvin Minsky自信地说，“一代人之内。。。创造‘人工智能’的问题将得到实质性的解决。”（Minsky，Marvin（1967），《计算：有限和无限机器》，新泽西州恩格伍德克利夫斯：Prentice Hall）
+从 20 世纪 50 年代到 70 年代中期，乐观情绪高涨，希望人工智能能够解决许多问题。1967 年，Marvin Minsky 自信地说，“一代人之内...创造‘人工智能’的问题将得到实质性的解决。”（Minsky，Marvin（1967），《计算：有限和无限机器》，新泽西州恩格伍德克利夫斯：Prentice Hall）
 
 自然语言处理研究蓬勃发展，搜索被提炼并变得更加强大，创造了“微观世界”的概念，在这个概念中，简单的任务是用简单的语言指令完成的。
 
 这项研究得到了政府机构的充分资助，在计算和算法方面取得了进展，并建造了智能机器的原型。其中一些机器包括：
 
-* [机器人Shakey](https://wikipedia.org/wiki/Shakey_the_robot)，他们可以“聪明地”操纵和决定如何执行任务。
+* [机器人 Shakey](https://wikipedia.org/wiki/Shakey_the_robot)，他们可以“聪明地”操纵和决定如何执行任务。
 
     ![Shakey, 智能机器人](../images/shakey.jpg)
-    > 1972 年的Shakey
+    > 1972 年的 Shakey
 
-* Eliza，一个早期的“聊天机器人”，可以与人交谈并充当原始的“治疗师”。 你将在NLP课程中了解有关Eliza的更多信息。 
+* Eliza，一个早期的“聊天机器人”，可以与人交谈并充当原始的“治疗师”。 你将在 NLP 课程中了解有关 Eliza 的更多信息。 
 
     ![Eliza, 机器人](../images/eliza.png)
-    > Eliza的一个版本，一个聊天机器人 
+    > Eliza 的一个版本，一个聊天机器人 
 
-* “积木世界”是一个微观世界的例子，在那里积木可以堆叠和分类，并且可以测试教机器做出决策的实验。 使用[SHRDLU](https://wikipedia.org/wiki/SHRDLU)等库构建的高级功能有助于推动语言处理向前发展。
+* “积木世界”是一个微观世界的例子，在那里积木可以堆叠和分类，并且可以测试教机器做出决策的实验。 使用 [SHRDLU](https://wikipedia.org/wiki/SHRDLU) 等库构建的高级功能有助于推动语言处理向前发展。
 
-    [![积木世界与SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "积木世界与SHRDLU")
+    [![积木世界与 SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "积木世界与SHRDLU")
     
-    > 🎥 点击上图观看视频： 积木世界与SHRDLU
+    > 🎥 点击上图观看视频： 积木世界与 SHRDLU
 
-## 1974 - 1980: AI的寒冬
+## 1974 - 1980: AI 的寒冬
 
-到了20世纪70年代中期，很明显制造“智能机器”的复杂性被低估了，而且考虑到可用的计算能力，它的前景被夸大了。资金枯竭，市场信心放缓。影响信心的一些问题包括：
+到了 20 世纪 70 年代中期，很明显制造“智能机器”的复杂性被低估了，而且考虑到可用的计算能力，它的前景被夸大了。资金枯竭，市场信心放缓。影响信心的一些问题包括：
 
 - **限制**。计算能力太有限了
 - **组合爆炸**。随着对计算机的要求越来越高，需要训练的参数数量呈指数级增长，而计算能力却没有平行发展。
 - **缺乏数据**。 缺乏数据阻碍了测试、开发和改进算法的过程。 
 - **我们是否在问正确的问题？**。 被问到的问题也开始受到质疑。 研究人员开始对他们的方法提出批评： 
   - 图灵测试受到质疑的方法之一是“中国房间理论”，该理论认为，“对数字计算机进行编程可能使其看起来能理解语言，但不能产生真正的理解。” ([来源](https://plato.stanford.edu/entries/chinese-room/))
-  - 将“治疗师”ELIZA这样的人工智能引入社会的伦理受到了挑战。
+  - 将“治疗师”ELIZA 这样的人工智能引入社会的伦理受到了挑战。
 
-与此同时，各种人工智能学派开始形成。 在[“scruffy”与“neat AI”](https://wikipedia.org/wiki/Neats_and_scruffies)之间建立了二分法。 _Scruffy_ 实验室对程序进行了数小时的调整，直到获得所需的结果。 _Neat_ 实验室“专注于逻辑和形式问题的解决”。 ELIZA 和 SHRDLU 是众所周知的 _scruffy_ 系统。 在 1980 年代，随着使 ML 系统可重现的需求出现，_neat_ 方法逐渐走上前沿，因为其结果更易于解释。
+与此同时，各种人工智能学派开始形成。 在 [“scruffy” 与 “neat AI”](https://wikipedia.org/wiki/Neats_and_scruffies) 之间建立了二分法。 _Scruffy_ 实验室对程序进行了数小时的调整，直到获得所需的结果。 _Neat_ 实验室“专注于逻辑和形式问题的解决”。 ELIZA 和 SHRDLU 是众所周知的 _scruffy_ 系统。 在 1980 年代，随着使 ML 系统可重现的需求出现，_neat_ 方法逐渐走上前沿，因为其结果更易于解释。
 
 ## 1980s 专家系统
 
-随着这个领域的发展，它对商业的好处变得越来越明显，在20世纪80年代，‘专家系统’的泛滥也是如此。“专家系统是首批真正成功的人工智能 (AI) 软件形式之一。” ([来源](https://wikipedia.org/wiki/Expert_system))。
+随着这个领域的发展，它对商业的好处变得越来越明显，在 20 世纪 80 年代，‘专家系统’的泛滥也是如此。“专家系统是首批真正成功的人工智能 (AI) 软件形式之一。” （[来源](https://wikipedia.org/wiki/Expert_system)）。
 
 这种类型的系统实际上是混合系统，部分由定义业务需求的规则引擎和利用规则系统推断新事实的推理引擎组成。
 
 在这个时代，神经网络也越来越受到重视。
 
-## 1987 - 1993: AI的冷静期
+## 1987 - 1993: AI 的冷静期
 
 专业的专家系统硬件的激增造成了过于专业化的不幸后果。个人电脑的兴起也与这些大型、专业化、集中化系统展开了竞争。计算机的平民化已经开始，它最终为大数据的现代爆炸铺平了道路。
 
 ## 1993 - 2011
 
-这个时代见证了一个新的时代，ML和AI能够解决早期由于缺乏数据和计算能力而导致的一些问题。数据量开始迅速增加，变得越来越广泛，无论好坏，尤其是2007年左右智能手机的出现，计算能力呈指数级增长，算法也随之发展。这个领域开始变得成熟，因为过去那些随心所欲的日子开始具体化为一种真正的纪律。
+这个时代见证了一个新的时代，ML 和 AI 能够解决早期由于缺乏数据和计算能力而导致的一些问题。数据量开始迅速增加，变得越来越广泛，无论好坏，尤其是 2007 年左右智能手机的出现，计算能力呈指数级增长，算法也随之发展。这个领域开始变得成熟，因为过去那些随心所欲的日子开始具体化为一种真正的纪律。
 
 ## 现在
 
-今天，机器学习和人工智能几乎触及我们生活的每一个部分。这个时代要求仔细了解这些算法对人类生活的风险和潜在影响。正如微软的Brad Smith所言，“信息技术引发的问题触及隐私和言论自由等基本人权保护的核心。这些问题加重了制造这些产品的科技公司的责任。在我们看来，它们还呼吁政府进行深思熟虑的监管，并围绕可接受的用途制定规范”([来源](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/))。
+今天，机器学习和人工智能几乎触及我们生活的每一个部分。这个时代要求仔细了解这些算法对人类生活的风险和潜在影响。正如微软的 Brad Smith 所言，“信息技术引发的问题触及隐私和言论自由等基本人权保护的核心。这些问题加重了制造这些产品的科技公司的责任。在我们看来，它们还呼吁政府进行深思熟虑的监管，并围绕可接受的用途制定规范”（[来源](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)）。
 
 未来的情况还有待观察，但了解这些计算机系统以及它们运行的软件和算法是很重要的。我们希望这门课程能帮助你更好的理解，以便你自己决定。
 
 [![深度学习的历史](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "深度学习的历史")
-> 🎥 点击上图观看视频：Yann LeCun在本次讲座中讨论深度学习的历史 
+> 🎥 点击上图观看视频：Yann LeCun 在本次讲座中讨论深度学习的历史 
 
 ---
 ## 🚀挑战
 
 深入了解这些历史时刻之一，并更多地了解它们背后的人。这里有许多引人入胜的人物，没有一项科学发现是在文化真空中创造出来的。你发现了什么？
 
-## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## 复习与自学
 
 以下是要观看和收听的节目：
 
-[这是Amy Boyd讨论人工智能进化的播客](http://runasradio.com/Shows/Show/739)
+[这是 Amy Boyd 讨论人工智能进化的播客](http://runasradio.com/Shows/Show/739)
 
 [![Amy Boyd的《人工智能史》](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd的《人工智能史》")
 
 ## 任务
 
-[创建时间线](../assignment.md)
+[创建时间线](assignment.zh-cn.md)
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.fr.md b/1-Introduction/2-history-of-ML/translations/assignment.fr.md
new file mode 100644
index 000000000..c562516e4
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.fr.md
@@ -0,0 +1,11 @@
+# Créer une frise chronologique
+
+## Instructions
+
+Utiliser [ce repo](https://github.com/Digital-Humanities-Toolkit/timeline-builder), créer une frise chronologique de certains aspects de l'histoire des algorithmes, des mathématiques, des statistiques, de l'IA ou du machine learning, ou une combinaison de ceux-ci. Vous pouvez vous concentrer sur une personne, une idée ou une longue période d'innovations. Assurez-vous d'ajouter des éléments multimédias.
+
+## Rubrique
+
+| Critères | Exemplaire                                                       | Adéquate                             | A améliorer                                                        |
+| -------- | ---------------------------------------------------------------- | ------------------------------------ | ------------------------------------------------------------------ |
+|          | Une chronologie déployée est présentée sous forme de page GitHub | Le code est incomplet et non déployé | La chronologie est incomplète, pas bien recherchée et pas déployée |
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.id.md b/1-Introduction/2-history-of-ML/translations/assignment.id.md
new file mode 100644
index 000000000..0ee7c0096
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.id.md
@@ -0,0 +1,11 @@
+# Membuat sebuah *timeline*
+
+## Instruksi
+
+Menggunakan [repo ini](https://github.com/Digital-Humanities-Toolkit/timeline-builder), buatlah sebuah *timeline* dari beberapa aspek sejarah algoritma, matematika, statistik, AI, atau ML, atau kombinasi dari semuanya. Kamu dapat fokus pada satu orang, satu ide, atau rentang waktu pemikiran yang panjang. Pastikan untuk menambahkan elemen multimedia. 
+
+## Rubrik
+
+| Kriteria | Sangat Bagus                                         | Cukup                                | Perlu Peningkatan                                                |
+| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
+|          | *Timeline* yang dideploy disajikan sebagai halaman GitHub | Kode belum lengkap dan belum dideploy | *Timeline* belum lengkap, belum diriset dengan baik dan belum dideploy |
\ No newline at end of file
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.ja.md b/1-Introduction/2-history-of-ML/translations/assignment.ja.md
new file mode 100644
index 000000000..f5f787992
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 年表を作成する
+
+## 指示
+
+[このリポジトリ](https://github.com/Digital-Humanities-Toolkit/timeline-builder) を使って、アルゴリズム・数学・統計学・人工知能・機械学習、またはこれらの組み合わせに対して、歴史のひとつの側面に関する年表を作成してください。焦点を当てるのは、ひとりの人物・ひとつのアイディア・長期間にわたる思想のいずれのものでも構いません。マルチメディアの要素を必ず加えるようにしてください。
+
+## 評価基準
+
+| 基準 | 模範的                                 | 十分                                 | 要改善                                                       |
+| ---- | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------ |
+|      | GitHub page に年表がデプロイされている | コードが未完成でデプロイされていない | 年表が未完成で、十分に調査されておらず、デプロイされていない |
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md b/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..adf3ee15a
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 建立一个时间轴
+
+## 说明
+
+使用这个 [仓库](https://github.com/Digital-Humanities-Toolkit/timeline-builder)，创建一个关于算法、数学、统计学、人工智能、机器学习的某个方面或者可以综合多个以上学科来讲。你可以着重介绍某个人，某个想法，或者一个经久不衰的思想。请确保添加了多媒体元素在你的时间线中。
+
+## 评判标准
+
+| 标准         | 优秀                               | 中规中矩               | 仍需努力                                    |
+| ------------ | ---------------------------------- | ---------------------- | ------------------------------------------ |
+|              | 有一个用 GitHub page 展示的 timeline | 代码还不完整并且没有部署 | 时间线不完整，没有经过充分的研究，并且没有部署 |
diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
index 79bf48192..7e9c8f6d8 100644
--- a/1-Introduction/3-fairness/README.md
+++ b/1-Introduction/3-fairness/README.md
@@ -3,7 +3,7 @@
 ![Summary of Fairness in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
  
 ## Introduction
 
@@ -184,7 +184,7 @@ To prevent biases from being introduced in the first place, we should:
 
 Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? 
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
 ## Review & Self Study 
  
 In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.  
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
new file mode 100644
index 000000000..980cbd88d
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -0,0 +1,213 @@
+# Keadilan dalam Machine Learning 
+
+![Ringkasan dari Keadilan dalam Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-fairness.png)
+> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+ 
+## Pengantar
+
+Dalam kurikulum ini, kamu akan mulai mengetahui bagaimana Machine Learning bisa memengaruhi kehidupan kita sehari-hari. Bahkan sekarang, sistem dan model terlibat dalam tugas pengambilan keputusan sehari-hari, seperti diagnosis kesehatan atau mendeteksi penipuan. Jadi, penting bahwa model-model ini bekerja dengan baik untuk memberikan hasil yang adil bagi semua orang.
+
+Bayangkan apa yang bisa terjadi ketika data yang kamu gunakan untuk membangun model ini tidak memiliki demografi tertentu, seperti ras, jenis kelamin, pandangan politik, agama, atau secara tidak proporsional mewakili demografi tersebut. Bagaimana jika keluaran dari model diinterpretasikan lebih menyukai beberapa demografis tertentu? Apa konsekuensi untuk aplikasinya? 
+
+Dalam pelajaran ini, kamu akan:
+
+- Meningkatkan kesadaran dari pentingnya keadilan dalam Machine Learning.
+- Mempelajari tentang berbagai kerugian terkait keadilan.
+- Learn about unfairness assessment and mitigation.
+- Mempelajari tentang mitigasi dan penilaian ketidakadilan.
+
+## Prasyarat
+
+Sebagai prasyarat, silakan ikuti jalur belajar "Prinsip AI yang Bertanggung Jawab" dan tonton video di bawah ini dengan topik: 
+
+Pelajari lebih lanjut tentang AI yang Bertanggung Jawab dengan mengikuti [Jalur Belajar](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) ini
+
+[![Pendekatan Microsoft untuk AI yang Bertanggung Jawab](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Pendekatan Microsoft untuk AI yang Bertanggung Jawab")
+
+> 🎥 Klik gambar diatas untuk menonton video: Pendekatan Microsoft untuk AI yang Bertanggung Jawab
+
+## Ketidakadilan dalam data dan algoritma
+
+> "Jika Anda menyiksa data cukup lama, data itu akan mengakui apa pun " - Ronald Coase
+
+Pernyataan ini terdengar ekstrem, tetapi memang benar bahwa data dapat dimanipulasi untuk mendukung kesimpulan apa pun. Manipulasi semacam itu terkadang bisa terjadi secara tidak sengaja. Sebagai manusia, kita semua memiliki bias, dan seringkali sulit untuk secara sadar mengetahui kapan kamu memperkenalkan bias dalam data.
+
+Menjamin keadilan dalam AI dan machine learning tetap menjadi tantangan sosioteknik yang kompleks. Artinya, hal itu tidak bisa ditangani baik dari perspektif sosial atau teknis semata. 
+
+### Kerugian Terkait Keadilan  
+
+Apa yang dimaksud dengan ketidakadilan? "Ketidakadilan" mencakup dampak negatif atau "bahaya" bagi sekelompok orang, seperti yang didefinisikan dalam hal ras, jenis kelamin, usia, atau status disabilitas. 
+
+Kerugian utama yang terkait dengan keadilan dapat diklasifikasikan sebagai: 
+
+- **Alokasi**, jika suatu jenis kelamin atau etnisitas misalkan lebih disukai daripada yang lain.
+- **Kualitas layanan**. Jika kamu melatih data untuk satu skenario tertentu tetapi kenyataannya jauh lebih kompleks, hasilnya adalah layanan yang berkinerja buruk.
+- **Stereotip**. Mengaitkan grup tertentu dengan atribut yang ditentukan sebelumnya.
+- **Fitnah**. Untuk mengkritik dan melabeli sesuatu atau seseorang secara tidak adil.
+- **Representasi yang kurang atau berlebihan**. Idenya adalah bahwa kelompok tertentu tidak terlihat dalam profesi tertentu, dan layanan atau fungsi apa pun yang terus dipromosikan yang menambah kerugian.
+
+Mari kita lihat contoh-contohnya.
+
+### Alokasi
+
+Bayangkan sebuah sistem untuk menyaring pengajuan pinjaman. Sistem cenderung memilih pria kulit putih sebagai kandidat yang lebih baik daripada kelompok lain. Akibatnya, pinjaman ditahan dari pemohon tertentu. 
+
+Contoh lain adalah alat perekrutan eksperimental yang dikembangkan oleh perusahaan besar untuk menyaring kandidat. Alat tersebut secara sistematis mendiskriminasi satu gender dengan menggunakan model yang dilatih untuk lebih memilih kata-kata yang terkait dengan gender lain. Hal ini mengakibatkan kandidat yang resumenya berisi kata-kata seperti "tim rugby wanita" tidak masuk kualifikasi. 
+
+✅ Lakukan sedikit riset untuk menemukan contoh dunia nyata dari sesuatu seperti ini
+
+### Kualitas Layanan
+
+Para peneliti menemukan bahwa beberapa pengklasifikasi gender komersial memiliki tingkat kesalahan yang lebih tinggi di sekitar gambar wanita dengan warna kulit lebih gelap dibandingkan dengan gambar pria dengan warna kulit lebih terang. [Referensi](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
+
+Contoh terkenal lainnya adalah dispenser sabun tangan yang sepertinya tidak bisa mendeteksi orang dengan kulit gelap. [Referensi](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### Stereotip
+
+Pandangan gender stereotip ditemukan dalam terjemahan mesin. Ketika menerjemahkan "dia (laki-laki) adalah seorang perawat dan dia (perempuan) adalah seorang dokter" ke dalam bahasa Turki, masalah muncul. Turki adalah bahasa tanpa gender yang memiliki satu kata ganti, "o" untuk menyampaikan orang ketiga tunggal, tetapi menerjemahkan kalimat kembali dari Turki ke Inggris menghasilkan stereotip dan salah sebagai "dia (perempuan) adalah seorang perawat dan dia (laki-laki) adalah seorang dokter". 
+
+![terjemahan ke bahasa Turki](../images/gender-bias-translate-en-tr.png)
+
+![terjemahan kembali ke bahasa Inggris](../images/gender-bias-translate-tr-en.png)
+
+### Fitnah
+
+Sebuah teknologi pelabelan gambar yang terkenal salah memberi label gambar orang berkulit gelap sebagai gorila. Pelabelan yang salah berbahaya bukan hanya karena sistem membuat kesalahan karena secara khusus menerapkan label yang memiliki sejarah panjang yang sengaja digunakan untuk merendahkan orang kulit hitam. 
+
+[![AI: Bukankah Aku Seorang Wanita?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "Bukankah Aku Seorang Wanita?")
+> 🎥 Klik gambar diatas untuk sebuah video: AI, Bukankah Aku Seorang Wanita? - menunjukkan kerugian yang disebabkan oleh pencemaran nama baik yang menyinggung ras oleh AI
+
+### Representasi yang kurang atau berlebihan
+
+Hasil pencarian gambar yang condong ke hal tertentu (skewed) dapat menjadi contoh yang bagus dari bahaya ini. Saat menelusuri gambar profesi dengan persentase pria yang sama atau lebih tinggi daripada wanita, seperti teknik, atau CEO, perhatikan hasil yang lebih condong ke jenis kelamin tertentu. 
+
+![Pencarian CEO di Bing](../images/ceos.png)
+> Pencarian di Bing untuk 'CEO' ini menghasilkan hasil yang cukup inklusif
+
+Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunjukkan lebih dari satu jenis bahaya. Selain itu, setiap kasus bervariasi dalam tingkat keparahannya. Misalnya, memberi label yang tidak adil kepada seseorang sebagai penjahat adalah bahaya yang jauh lebih parah daripada memberi label yang salah pada gambar. Namun, penting untuk diingat bahwa bahkan kerugian yang relatif tidak parah dapat membuat orang merasa terasing atau diasingkan dan dampak kumulatifnya bisa sangat menekan. 
+ 
+✅ **Diskusi**: Tinjau kembali beberapa contoh dan lihat apakah mereka menunjukkan bahaya yang berbeda.  
+
+|                            | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan |
+| -------------------------- | :-----: | :--------------: | :-------: | :----: | :--------------------------------------: |
+| Sistem perekrutan otomatis |    x    |        x         |     x     |        |                    x                     |
+| Terjemahan mesin           |         |                  |           |        |                                          |
+| Melabeli foto              |         |                  |           |        |                                          |
+
+
+## Mendeteksi Ketidakadilan 
+
+Ada banyak alasan mengapa sistem tertentu berperilaku tidak adil. Bias sosial, misalnya, mungkin tercermin dalam kumpulan data yang digunakan untuk melatih. Misalnya, ketidakadilan perekrutan mungkin telah diperburuk oleh ketergantungan yang berlebihan pada data historis. Dengan menggunakan pola dalam resume yang dikirimkan ke perusahaan selama periode 10 tahun, model tersebut menentukan bahwa pria lebih berkualitas karena mayoritas resume berasal dari pria, yang mencerminkan dominasi pria di masa lalu di industri teknologi. 
+
+Data yang tidak memadai tentang sekelompok orang tertentu dapat menjadi alasan ketidakadilan. Misalnya, pengklasifikasi gambar memiliki tingkat kesalahan yang lebih tinggi untuk gambar orang berkulit gelap karena warna kulit yang lebih gelap kurang terwakili dalam data. 
+
+Asumsi yang salah yang dibuat selama pengembangan menyebabkan ketidakadilan juga. Misalnya, sistem analisis wajah yang dimaksudkan untuk memprediksi siapa yang akan melakukan kejahatan berdasarkan gambar wajah orang dapat menyebabkan asumsi yang merusak. Hal ini dapat menyebabkan kerugian besar bagi orang-orang yang salah diklasifikasikan. 
+
+## Pahami model kamu dan bangun dalam keadilan
+
+Meskipun banyak aspek keadilan tidak tercakup dalam metrik keadilan kuantitatif, dan tidak mungkin menghilangkan bias sepenuhnya dari sistem untuk menjamin keadilan, Kamu tetap bertanggung jawab untuk mendeteksi dan mengurangi masalah keadilan sebanyak mungkin. 
+
+Saat Kamu bekerja dengan model pembelajaran mesin, penting untuk memahami model Kamu dengan cara memastikan interpretasinya dan dengan menilai serta mengurangi ketidakadilan. 
+
+Mari kita gunakan contoh pemilihan pinjaman untuk mengisolasi kasus untuk mengetahui tingkat dampak setiap faktor pada prediksi. 
+
+## Metode Penilaian
+
+1. **Identifikasi bahaya (dan manfaat)**. Langkah pertama adalah mengidentifikasi bahaya dan manfaat. Pikirkan tentang bagaimana tindakan dan keputusan dapat memengaruhi calon pelanggan dan bisnis itu sendiri.
+  
+1. **Identifikasi kelompok yang terkena dampak**. Setelah Kamu memahami jenis kerugian atau manfaat apa yang dapat terjadi, identifikasi kelompok-kelompok yang mungkin terpengaruh. Apakah kelompok-kelompok ini ditentukan oleh jenis kelamin, etnis, atau kelompok sosial? 
+
+1. **Tentukan metrik keadilan**. Terakhir, tentukan metrik sehingga Kamu memiliki sesuatu untuk diukur dalam pekerjaan Kamu untuk memperbaiki situasi. 
+
+### Identifikasi bahaya (dan manfaat)
+
+Apa bahaya dan manfaat yang terkait dengan pinjaman? Pikirkan tentang skenario negatif palsu dan positif palsu: 
+
+**False negatives** (ditolak, tapi Y=1) - dalam hal ini, pemohon yang akan mampu membayar kembali pinjaman ditolak. Ini adalah peristiwa yang merugikan karena sumber pinjaman ditahan dari pemohon yang memenuhi syarat.
+
+**False positives** (diterima, tapi Y=0) - dalam hal ini, pemohon memang mendapatkan pinjaman tetapi akhirnya wanprestasi. Akibatnya, kasus pemohon akan dikirim ke agen penagihan utang yang dapat mempengaruhi permohonan pinjaman mereka di masa depan. 
+
+### Identifikasi kelompok yang terkena dampak
+
+Langkah selanjutnya adalah menentukan kelompok mana yang kemungkinan akan terpengaruh. Misalnya, dalam kasus permohonan kartu kredit, sebuah model mungkin menentukan bahwa perempuan harus menerima batas kredit yang jauh lebih rendah dibandingkan dengan pasangan mereka yang berbagi aset rumah tangga. Dengan demikian, seluruh demografi yang ditentukan berdasarkan jenis kelamin menjadi terpengaruh. 
+
+### Tentukan metrik keadilan
+
+Kamu telah mengidentifikasi bahaya dan kelompok yang terpengaruh, dalam hal ini digambarkan berdasarkan jenis kelamin. Sekarang, gunakan faktor terukur (*quantified factors*) untuk memisahkan metriknya. Misalnya, dengan menggunakan data di bawah ini, Kamu dapat melihat bahwa wanita memiliki tingkat *false positive* terbesar dan pria memiliki yang terkecil, dan kebalikannya berlaku untuk *false negative*. 
+
+✅ Dalam pelajaran selanjutnya tentang *Clustering*, Kamu akan melihat bagaimana membangun 'confusion matrix' ini dalam kode 
+
+|            | False positive rate | False negative rate | count |
+| ---------- | ------------------- | ------------------- | ----- |
+| Women      | 0.37                | 0.27                | 54032 |
+| Men        | 0.31                | 0.35                | 28620 |
+| Non-binary | 0.33                | 0.31                | 1266  |
+
+ 
+Tabel ini memberitahu kita beberapa hal. Pertama, kami mencatat bahwa ada sedikit orang non-biner dalam data. Datanya condong (*skewed*), jadi Kamu harus berhati-hati dalam menafsirkan angka-angka ini. 
+
+Dalam hal ini, kita memiliki 3 grup dan 2 metrik. Ketika kita memikirkan tentang bagaimana sistem kita memengaruhi kelompok pelanggan dengan permohonan pinjaman mereka, ini mungkin cukup, tetapi ketika Kamu ingin menentukan jumlah grup yang lebih besar, Kamu mungkin ingin menyaringnya menjadi kumpulan ringkasan yang lebih kecil. Untuk melakukannya, Kamu dapat menambahkan lebih banyak metrik, seperti perbedaan terbesar atau rasio terkecil dari setiap *false negative* dan *false positive*. 
+
+✅ Berhenti dan Pikirkan: Kelompok lain yang apa lagi yang mungkin terpengaruh untuk pengajuan pinjaman? 
+ 
+## Mengurangi ketidakadilan 
+
+Untuk mengurangi ketidakadilan, jelajahi model untuk menghasilkan berbagai model yang dimitigasi dan bandingkan pengorbanan yang dibuat antara akurasi dan keadilan untuk memilih model yang paling adil. 
+
+Pelajaran pengantar ini tidak membahas secara mendalam mengenai detail mitigasi ketidakadilan algoritmik, seperti pendekatan pasca-pemrosesan dan pengurangan (*post-processing and reductions approach*), tetapi berikut adalah *tool* yang mungkin ingin Kamu coba. 
+
+### Fairlearn 
+ 
+[Fairlearn](https://fairlearn.github.io/) adalah sebuah *package* Python open-source yang memungkinkan Kamu untuk menilai keadilan sistem Kamu dan mengurangi ketidakadilan.   
+
+*Tool* ini membantu Kamu menilai bagaimana prediksi model memengaruhi kelompok yang berbeda, memungkinkan Kamu untuk membandingkan beberapa model dengan menggunakan metrik keadilan dan kinerja, dan menyediakan serangkaian algoritma untuk mengurangi ketidakadilan dalam klasifikasi dan regresi biner. 
+
+- Pelajari bagaimana cara menggunakan komponen-komponen yang berbeda dengan mengunjungi [GitHub](https://github.com/fairlearn/fairlearn/) Fairlearn
+
+- Jelajahi [panduan pengguna](https://fairlearn.github.io/main/user_guide/index.html), [contoh-contoh](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Coba beberapa [sampel notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+
+- Pelajari [bagaimana cara mengaktifkan penilaian keadilan](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dari model machine learning di Azure Machine Learning.
+
+- Lihat [sampel notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) ini untuk skenario penilaian keadilan yang lebih banyak di Azure Machine Learning. 
+
+---
+## 🚀 Tantangan 
+
+Untuk mencegah kemunculan bias pada awalnya, kita harus: 
+
+- memiliki keragaman latar belakang dan perspektif di antara orang-orang yang bekerja pada sistem 
+- berinvestasi dalam dataset yang mencerminkan keragaman masyarakat kita 
+- mengembangkan metode yang lebih baik untuk mendeteksi dan mengoreksi bias ketika itu terjadi 
+
+Pikirkan tentang skenario kehidupan nyata di mana ketidakadilan terbukti dalam pembuatan dan penggunaan model. Apa lagi yang harus kita pertimbangkan? 
+
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+## Ulasan & Belajar Mandiri 
+ 
+Dalam pelajaran ini, Kamu telah mempelajari beberapa dasar konsep keadilan dan ketidakadilan dalam pembelajaran mesin. 
+ 
+Tonton workshop ini untuk menyelami lebih dalam kedalam topik: 
+
+- YouTube: Kerugian terkait keadilan dalam sistem AI: Contoh, penilaian, dan mitigasi oleh Hanna Wallach dan Miro Dudik [Kerugian terkait keadilan dalam sistem AI: Contoh, penilaian, dan mitigasi - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+
+Kamu juga dapat membaca:
+
+- Pusat sumber daya RAI Microsoft: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+
+- Grup riset FATE Microsoft: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+
+Jelajahi *toolkit* Fairlearn
+
+[Fairlearn](https://fairlearn.org/)
+
+Baca mengenai *tools* Azure Machine Learning untuk memastikan keadilan
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+
+## Tugas
+
+[Jelajahi Fairlearn](assignment.id.md) 
diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md
index 3c167f096..29ab88c31 100644
--- a/1-Introduction/3-fairness/translations/README.it.md
+++ b/1-Introduction/3-fairness/translations/README.it.md
@@ -1,212 +1,212 @@
-# Equità e machine learning
-
-![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png)
-> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
-
-## Introduzione
-
-In questo programma di studi, si inizierà a scoprire come machine learning può e sta influenzando la  vita quotidiana. Anche ora, sistemi e modelli sono coinvolti nelle attività decisionali quotidiane, come le diagnosi sanitarie o l'individuazione di frodi. Quindi è importante che questi modelli funzionino bene per fornire risultati equi per tutti.
-
-Si immagini cosa può accadere quando i dati che si stanno utilizzando per costruire questi modelli mancano di determinati dati demografici, come razza, genere, visione politica, religione, o rappresentano tali dati demografici in modo sproporzionato. E quando il risultato del modello viene interpretato per favorire alcuni gruppi demografici? Qual è la conseguenza per l'applicazione?
-
-In questa lezione, si dovrà:
-
-- Aumentare la propria consapevolezza sull'importanza dell'equità nel machine learning.
-- Informarsi sui danni legati all'equità.
-- Apprendere ulteriori informazioni sulla valutazione e la mitigazione dell'ingiustizia.
-
-## Prerequisito
-
-Come prerequisito, si segua il percorso di apprendimento "Principi di AI Responsabile" e si guardi il video qui sotto sull'argomento:
-
-Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
-
-[![L'approccio di Microsoft all'AI responsabileL'](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile")
-
-> 🎥 Fare clic sull'immagine sopra per un video: L'approccio di Microsoft all'AI Responsabile
-
-## Iniquità nei dati e negli algoritmi
-
-> "Se si torturano i dati abbastanza a lungo, essi confesseranno qualsiasi cosa" - Ronald Coase
-
-Questa affermazione suona estrema, ma è vero che i dati possono essere manipolati per supportare qualsiasi conclusione. Tale manipolazione a volte può avvenire involontariamente. Come esseri umani, abbiamo tutti dei pregiudizi, ed è spesso difficile sapere consapevolmente quando si introduce un pregiudizio nei dati.
-
-Garantire l'equità nell'intelligenza artificiale e machine learning rimane una sfida socio-tecnica complessa. Ciò significa che non può essere affrontata da prospettive puramente sociali o tecniche.
-
-### Danni legati all'equità
-
-Cosa si intende per ingiustizia? L'"ingiustizia" comprende gli impatti negativi, o "danni", per un gruppo di persone, come quelli definiti in termini di razza, genere, età o stato di disabilità.
-
-I principali danni legati all'equità possono essere classificati come:
-
-- **Allocazione**, se un genere o un'etnia, ad esempio, sono preferiti a un altro.
-- **Qualità di servizio** Se si addestrano i dati per uno scenario specifico, ma la realtà è molto più complessa, si ottiene un servizio scadente.
-- **Stereotipi**. Associazione di un dato gruppo con attributi preassegnati.
-- **Denigrazione**. Criticare ed etichettare ingiustamente qualcosa o qualcuno.
-- **Sovra o sotto rappresentazione**. L'idea è che un certo gruppo non è visto in una certa professione, e qualsiasi servizio o funzione che continua a promuovere ciò, contribuisce al danno.
-
-Si dia un'occhiata agli esempi.
-
-### Allocazione
-
-Si consideri un ipotetico sistema per la scrematura delle domande di prestito. Il sistema tende a scegliere gli uomini bianchi come candidati migliori rispetto ad altri gruppi. Di conseguenza, i prestiti vengono negati ad alcuni richiedenti.
-
-Un altro esempio potrebbe essere uno strumento sperimentale di assunzione sviluppato da una grande azienda per selezionare i candidati. Lo strumento discrimina sistematicamente un genere utilizzando i modelli che sono stati addestrati a preferire parole associate con altro. Ha portato a penalizzare i candidati i cui curricula contengono parole come "squadra di rugby femminile".
-
-✅ Si compia una piccola ricerca per trovare un esempio reale di qualcosa del genere
-
-### Qualità di Servizio
-
-I ricercatori hanno scoperto che diversi classificatori di genere commerciali avevano tassi di errore più elevati intorno alle immagini di donne con tonalità della pelle più scura rispetto alle immagini di uomini con tonalità della pelle più chiare. [Riferimento](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/)
-
-Un altro esempio infamante è un distributore di sapone per le mani che sembrava non essere in grado di percepire le persone con la pelle scura. [Riferimento](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
-
-### Stereotipi
-
-La visione di genere stereotipata è stata trovata nella traduzione automatica. Durante la traduzione in turco "he is a nurse and she is a doctor" (lui è un'infermiere e lei un medico), sono stati riscontrati problemi. Il turco è una lingua senza genere che ha un pronome, "o" per trasmettere una terza persona singolare, ma tradurre la frase dal turco all'inglese produce lo stereotipo e scorretto come "she is a nurse and he is a doctor" (lei è un'infermiera e lui è un medico).
-
-![traduzione in turco](../images/gender-bias-translate-en-tr.png)
-
-![Traduzione in inglese](../images/gender-bias-translate-tr-en.png)
-
-### Denigrazione
-
-Una tecnologia di etichettatura delle immagini ha contrassegnato in modo infamante le immagini di persone dalla pelle scura come gorilla. L'etichettatura errata è dannosa non solo perché il sistema ha commesso un errore, ma anche perché ha applicato specificamente un'etichetta che ha una lunga storia di essere intenzionalmente utilizzata per denigrare i neri.
-
-[![AI: Non sono una donna?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, non sono una donna?")
-> 🎥 Cliccare sull'immagine sopra per un video: AI, Ain't I a Woman - una performance che mostra il danno causato dalla denigrazione razzista da parte dell'AI
-
-### Sovra o sotto rappresentazione
-
-I risultati di ricerca di immagini distorti possono essere un buon esempio di questo danno. Quando si cercano immagini di professioni con una percentuale uguale o superiore di uomini rispetto alle donne, come l'ingegneria o CEO, si osserva che i risultati sono più fortemente distorti verso un determinato genere.
-
-![Ricerca CEO di Bing](../images/ceos.png)
-> Questa ricerca su Bing per "CEO" produce risultati piuttosto inclusivi
-
-Questi cinque principali tipi di danno non si escludono a vicenda e un singolo sistema può presentare più di un tipo di danno. Inoltre, ogni caso varia nella sua gravità. Ad esempio, etichettare ingiustamente qualcuno come criminale è un danno molto più grave che etichettare erroneamente un'immagine. È importante, tuttavia, ricordare che anche danni relativamente non gravi possono far sentire le persone alienate o emarginate e l'impatto cumulativo può essere estremamente opprimente.
-
-✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
-
-|                         | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Sistema di assunzione automatizzato |     x      |         x          |      x       |             |               x                |
-| Traduzione automatica     |            |                    |              |             |                                |
-| Eitchettatura foto          |            |                    |              |             |                                |
-
-## Rilevare l'ingiustizia
-
-Ci sono molte ragioni per cui un dato sistema si comporta in modo scorretto. I pregiudizi sociali, ad esempio, potrebbero riflettersi nell'insieme di dati utilizzati per addestrarli. Ad esempio, l'ingiustizia delle assunzioni potrebbe essere stata esacerbata dall'eccessivo affidamento sui dati storici. Utilizzando i modelli nei curricula inviati all'azienda per un periodo di 10 anni, il modello ha determinato che gli uomini erano più qualificati perché la maggior parte dei curricula proveniva da uomini, un riflesso del passato dominio maschile nell'industria tecnologica.
-
-Dati inadeguati su un determinato gruppo di persone possono essere motivo di ingiustizia. Ad esempio, i classificatori di immagini hanno un tasso di errore più elevato per le immagini di persone dalla pelle scura perché le tonalità della pelle più scure sono sottorappresentate nei dati.
-
-Anche le ipotesi errate fatte durante lo sviluppo causano iniquità. Ad esempio, un sistema di analisi facciale destinato a prevedere chi commetterà un crimine basato sulle immagini dei volti delle persone può portare a ipotesi dannose. Ciò potrebbe portare a danni sostanziali per le persone classificate erroneamente.
-
-## Si comprendano i propri modelli e si costruiscano in modo onesto
-
-Sebbene molti aspetti dell'equità non vengano catturati nelle metriche di equità quantitativa e non sia possibile rimuovere completamente i pregiudizi da un sistema per garantire l'equità, si è comunque responsabili di rilevare e mitigare il più possibile i problemi di equità.
-
-Quando si lavora con modelli di machine learning, è importante comprendere i propri modelli assicurandone l'interpretabilità e valutando e mitigando l'ingiustizia.
-
-Si utilizza l'esempio di selezione del prestito per isolare il caso e determinare il livello di impatto di ciascun fattore sulla previsione.
-
-## Metodi di valutazione
-
-1. **Identificare i danni (e benefici)**. Il primo passo è identificare danni e benefici. Si pensi a come azioni e decisioni possono influenzare sia i potenziali clienti che un'azienda stessa.
-
-1. **Identificare i gruppi interessati**. Una volta compreso il tipo di danni o benefici che possono verificarsi, identificare i gruppi che potrebbero essere interessati. Questi gruppi sono definiti per genere, etnia o gruppo sociale?
-
-1. **Definire le metriche di equità**. Infine, si definisca una metrica in modo da avere qualcosa su cui misurare il proprio lavoro per migliorare la situazione.
-
-### **Identificare danni (e benefici)**
-
-Quali sono i danni e i benefici associati al prestito? Si pensi agli scenari di falsi negativi e falsi positivi:
-
-**Falsi negativi** (rifiutato, ma Y=1) - in questo caso viene rifiutato un richiedente che sarà in grado di rimborsare un prestito. Questo è un evento avverso perché le risorse dei prestiti non sono erogate a richiedenti qualificati.
-
-**Falsi positivi** (accettato, ma Y=0) - in questo caso, il richiedente ottiene un prestito ma alla fine fallisce. Di conseguenza, il caso del richiedente verrà inviato a un'agenzia di recupero crediti che può influire sulle sue future richieste di prestito.
-
-### **Identificare i gruppi interessati**
-
-Il passo successivo è determinare quali gruppi potrebbero essere interessati. Ad esempio, nel caso di una richiesta di carta di credito, un modello potrebbe stabilire che le donne dovrebbero ricevere limiti di credito molto più bassi rispetto ai loro coniugi che condividono i beni familiari. Un intero gruppo demografico, definito in base al genere, è così interessato.
-
-### **Definire le metriche di equità**
-
-Si sono identificati i danni e un gruppo interessato, in questo caso, delineato per genere. Ora, si usino i fattori quantificati per disaggregare le loro metriche. Ad esempio, utilizzando i dati di seguito, si può vedere che le donne hanno il più alto tasso di falsi positivi e gli uomini il più piccolo, e che è vero il contrario per i falsi negativi.
-
-✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
-
-|            | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio |
-| ---------- | ------------------- | ------------------- | ----- |
-| Donna | 0,37 | 0,27 | 54032 |
-| Uomo | 0,31 | 0.35 | 28620 |
-| Non binario | 0,33 | 0,31 | 1266 |
-
-Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano  questi numeri.
-
-In questo caso, ci sono 3 gruppi e 2 metriche. Quando si pensa a come il nostro sistema influisce sul gruppo di clienti con i loro richiedenti di prestito, questo può essere sufficiente, ma quando si desidera definire un numero maggiore di gruppi, è possibile distillare questo in insiemi più piccoli di riepiloghi. Per fare ciò, si possono aggiungere più metriche, come la differenza più grande o il rapporto più piccolo di ogni falso negativo e falso positivo.
-
-✅ Ci si fermi a pensare: quali altri gruppi potrebbero essere interessati dalla richiesta di prestito?
-
-## Mitigare l'ingiustizia
-
-Per mitigare l'ingiustizia, si esplori il modello per generare vari modelli mitigati e si confrontino i compromessi tra accuratezza ed equità per selezionare il modello più equo.
-
-Questa lezione introduttiva non approfondisce i dettagli dell'algoritmo della mitigazione dell'ingiustizia, come l'approccio di post-elaborazione e riduzione, ma ecco uno strumento che si potrebbe voler provare.
-
-### Fairlearn
-
-[Fairlearn](https://fairlearn.github.io/) è un pacchetto Python open source che consente di valutare l'equità dei propri sistemi e mitigare l'ingiustizia.
-
-Lo strumento consente di valutare in che modo le previsioni di un modello influiscono su diversi gruppi, consentendo di confrontare più modelli utilizzando metriche di equità e prestazioni e fornendo una serie di algoritmi per mitigare l'ingiustizia nella classificazione binaria e nella regressione.
-
-- Si scopra come utilizzare i diversi componenti controllando il GitHub di [Fairlearn](https://github.com/fairlearn/fairlearn/)
-
-- Si esplori la [guida per l'utente](https://fairlearn.github.io/main/user_guide/index.html), e gli [esempi](https://fairlearn.github.io/main/auto_examples/index.html)
-
-- Si provino alcuni [notebook di esempio](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
-
-- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dei modelli di Machine Learning in Azure Machine Learning.
-
-- Si dia un'occhiata a questi [notebook di esempio](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) per ulteriori scenari di valutazione dell'equità in Azure Machine Learning.
-
----
-
-## 🚀 Sfida
-
-Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
-
-- avere una diversità di background e prospettive tra le persone che lavorano sui sistemi
-- investire in insiemi di dati che riflettano le diversità della società
-- sviluppare metodi migliori per rilevare e correggere i pregiudizi quando si verificano
-
-Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
-
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
-
-## Revisione e Auto Apprendimento
-
-In questa lezione si sono apprese alcune nozioni di base sui concetti di equità e ingiustizia in machine learning.
-
-Si guardi questo workshop per approfondire gli argomenti:
-
-- YouTube: Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione di Hanna Wallach e Miro Dudik [Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
-
-Si legga anche:
-
-- Centro risorse RAI di Microsoft: [risorse AI responsabili – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4)
-
-- Gruppo di ricerca FATE di Microsoft[: FATE: equità, responsabilità, trasparenza ed etica nell'intelligenza artificiale - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
-
-Si esplori il toolkit Fairlearn
-
-[Fairlearn](https://fairlearn.org/)
-
-Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità
-
-- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa)
-
-## Compito
-
-[Esplorare Fairlearn](assignment.it.md)
+# Equità e machine learning
+
+![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/?loc=it)
+
+## Introduzione
+
+In questo programma di studi, si inizierà a scoprire come machine learning può e sta influenzando la  vita quotidiana. Anche ora, sistemi e modelli sono coinvolti nelle attività decisionali quotidiane, come le diagnosi sanitarie o l'individuazione di frodi. Quindi è importante che questi modelli funzionino bene per fornire risultati equi per tutti.
+
+Si immagini cosa può accadere quando i dati che si stanno utilizzando per costruire questi modelli mancano di determinati dati demografici, come razza, genere, visione politica, religione, o rappresentano tali dati demografici in modo sproporzionato. E quando il risultato del modello viene interpretato per favorire alcuni gruppi demografici? Qual è la conseguenza per l'applicazione?
+
+In questa lezione, si dovrà:
+
+- Aumentare la propria consapevolezza sull'importanza dell'equità nel machine learning.
+- Informarsi sui danni legati all'equità.
+- Apprendere ulteriori informazioni sulla valutazione e la mitigazione dell'ingiustizia.
+
+## Prerequisito
+
+Come prerequisito, si segua il percorso di apprendimento "Principi di AI Responsabile" e si guardi il video qui sotto sull'argomento:
+
+Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
+
+[![L'approccio di Microsoft all'AI responsabileL'](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile")
+
+> 🎥 Fare clic sull'immagine sopra per un video: L'approccio di Microsoft all'AI Responsabile
+
+## Iniquità nei dati e negli algoritmi
+
+> "Se si torturano i dati abbastanza a lungo, essi confesseranno qualsiasi cosa" - Ronald Coase
+
+Questa affermazione suona estrema, ma è vero che i dati possono essere manipolati per supportare qualsiasi conclusione. Tale manipolazione a volte può avvenire involontariamente. Come esseri umani, abbiamo tutti dei pregiudizi, ed è spesso difficile sapere consapevolmente quando si introduce un pregiudizio nei dati.
+
+Garantire l'equità nell'intelligenza artificiale e machine learning rimane una sfida socio-tecnica complessa. Ciò significa che non può essere affrontata da prospettive puramente sociali o tecniche.
+
+### Danni legati all'equità
+
+Cosa si intende per ingiustizia? L'"ingiustizia" comprende gli impatti negativi, o "danni", per un gruppo di persone, come quelli definiti in termini di razza, genere, età o stato di disabilità.
+
+I principali danni legati all'equità possono essere classificati come:
+
+- **Allocazione**, se un genere o un'etnia, ad esempio, sono preferiti a un altro.
+- **Qualità di servizio** Se si addestrano i dati per uno scenario specifico, ma la realtà è molto più complessa, si ottiene un servizio scadente.
+- **Stereotipi**. Associazione di un dato gruppo con attributi preassegnati.
+- **Denigrazione**. Criticare ed etichettare ingiustamente qualcosa o qualcuno.
+- **Sovra o sotto rappresentazione**. L'idea è che un certo gruppo non è visto in una certa professione, e qualsiasi servizio o funzione che continua a promuovere ciò, contribuisce al danno.
+
+Si dia un'occhiata agli esempi.
+
+### Allocazione
+
+Si consideri un ipotetico sistema per la scrematura delle domande di prestito. Il sistema tende a scegliere gli uomini bianchi come candidati migliori rispetto ad altri gruppi. Di conseguenza, i prestiti vengono negati ad alcuni richiedenti.
+
+Un altro esempio potrebbe essere uno strumento sperimentale di assunzione sviluppato da una grande azienda per selezionare i candidati. Lo strumento discrimina sistematicamente un genere utilizzando i modelli che sono stati addestrati a preferire parole associate con altro. Ha portato a penalizzare i candidati i cui curricula contengono parole come "squadra di rugby femminile".
+
+✅ Si compia una piccola ricerca per trovare un esempio reale di qualcosa del genere
+
+### Qualità di Servizio
+
+I ricercatori hanno scoperto che diversi classificatori di genere commerciali avevano tassi di errore più elevati intorno alle immagini di donne con tonalità della pelle più scura rispetto alle immagini di uomini con tonalità della pelle più chiare. [Riferimento](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/)
+
+Un altro esempio infamante è un distributore di sapone per le mani che sembrava non essere in grado di percepire le persone con la pelle scura. [Riferimento](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### Stereotipi
+
+La visione di genere stereotipata è stata trovata nella traduzione automatica. Durante la traduzione in turco "he is a nurse and she is a doctor" (lui è un'infermiere e lei un medico), sono stati riscontrati problemi. Il turco è una lingua senza genere che ha un pronome, "o" per trasmettere una terza persona singolare, ma tradurre la frase dal turco all'inglese produce lo stereotipo e scorretto come "she is a nurse and he is a doctor" (lei è un'infermiera e lui è un medico).
+
+![traduzione in turco](../images/gender-bias-translate-en-tr.png)
+
+![Traduzione in inglese](../images/gender-bias-translate-tr-en.png)
+
+### Denigrazione
+
+Una tecnologia di etichettatura delle immagini ha contrassegnato in modo infamante le immagini di persone dalla pelle scura come gorilla. L'etichettatura errata è dannosa non solo perché il sistema ha commesso un errore, ma anche perché ha applicato specificamente un'etichetta che ha una lunga storia di essere intenzionalmente utilizzata per denigrare i neri.
+
+[![AI: Non sono una donna?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, non sono una donna?")
+> 🎥 Cliccare sull'immagine sopra per un video: AI, Ain't I a Woman - una performance che mostra il danno causato dalla denigrazione razzista da parte dell'AI
+
+### Sovra o sotto rappresentazione
+
+I risultati di ricerca di immagini distorti possono essere un buon esempio di questo danno. Quando si cercano immagini di professioni con una percentuale uguale o superiore di uomini rispetto alle donne, come l'ingegneria o CEO, si osserva che i risultati sono più fortemente distorti verso un determinato genere.
+
+![Ricerca CEO di Bing](../images/ceos.png)
+> Questa ricerca su Bing per "CEO" produce risultati piuttosto inclusivi
+
+Questi cinque principali tipi di danno non si escludono a vicenda e un singolo sistema può presentare più di un tipo di danno. Inoltre, ogni caso varia nella sua gravità. Ad esempio, etichettare ingiustamente qualcuno come criminale è un danno molto più grave che etichettare erroneamente un'immagine. È importante, tuttavia, ricordare che anche danni relativamente non gravi possono far sentire le persone alienate o emarginate e l'impatto cumulativo può essere estremamente opprimente.
+
+✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
+
+|                                     | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
+| ----------------------------------- | :---------: | :-----------------: | :--------: | :----------: | :----------------------------: |
+| Sistema di assunzione automatizzato |      x      |          x          |     x      |              |               x                |
+| Traduzione automatica               |             |                     |            |              |                                |
+| Eitchettatura foto                  |             |                     |            |              |                                |
+
+## Rilevare l'ingiustizia
+
+Ci sono molte ragioni per cui un dato sistema si comporta in modo scorretto. I pregiudizi sociali, ad esempio, potrebbero riflettersi nell'insieme di dati utilizzati per addestrarli. Ad esempio, l'ingiustizia delle assunzioni potrebbe essere stata esacerbata dall'eccessivo affidamento sui dati storici. Utilizzando i modelli nei curricula inviati all'azienda per un periodo di 10 anni, il modello ha determinato che gli uomini erano più qualificati perché la maggior parte dei curricula proveniva da uomini, un riflesso del passato dominio maschile nell'industria tecnologica.
+
+Dati inadeguati su un determinato gruppo di persone possono essere motivo di ingiustizia. Ad esempio, i classificatori di immagini hanno un tasso di errore più elevato per le immagini di persone dalla pelle scura perché le tonalità della pelle più scure sono sottorappresentate nei dati.
+
+Anche le ipotesi errate fatte durante lo sviluppo causano iniquità. Ad esempio, un sistema di analisi facciale destinato a prevedere chi commetterà un crimine basato sulle immagini dei volti delle persone può portare a ipotesi dannose. Ciò potrebbe portare a danni sostanziali per le persone classificate erroneamente.
+
+## Si comprendano i propri modelli e si costruiscano in modo onesto
+
+Sebbene molti aspetti dell'equità non vengano catturati nelle metriche di equità quantitativa e non sia possibile rimuovere completamente i pregiudizi da un sistema per garantire l'equità, si è comunque responsabili di rilevare e mitigare il più possibile i problemi di equità.
+
+Quando si lavora con modelli di machine learning, è importante comprendere i propri modelli assicurandone l'interpretabilità e valutando e mitigando l'ingiustizia.
+
+Si utilizza l'esempio di selezione del prestito per isolare il caso e determinare il livello di impatto di ciascun fattore sulla previsione.
+
+## Metodi di valutazione
+
+1. **Identificare i danni (e benefici)**. Il primo passo è identificare danni e benefici. Si pensi a come azioni e decisioni possono influenzare sia i potenziali clienti che un'azienda stessa.
+
+1. **Identificare i gruppi interessati**. Una volta compreso il tipo di danni o benefici che possono verificarsi, identificare i gruppi che potrebbero essere interessati. Questi gruppi sono definiti per genere, etnia o gruppo sociale?
+
+1. **Definire le metriche di equità**. Infine, si definisca una metrica in modo da avere qualcosa su cui misurare il proprio lavoro per migliorare la situazione.
+
+### **Identificare danni (e benefici)**
+
+Quali sono i danni e i benefici associati al prestito? Si pensi agli scenari di falsi negativi e falsi positivi:
+
+**Falsi negativi** (rifiutato, ma Y=1) - in questo caso viene rifiutato un richiedente che sarà in grado di rimborsare un prestito. Questo è un evento avverso perché le risorse dei prestiti non sono erogate a richiedenti qualificati.
+
+**Falsi positivi** (accettato, ma Y=0) - in questo caso, il richiedente ottiene un prestito ma alla fine fallisce. Di conseguenza, il caso del richiedente verrà inviato a un'agenzia di recupero crediti che può influire sulle sue future richieste di prestito.
+
+### **Identificare i gruppi interessati**
+
+Il passo successivo è determinare quali gruppi potrebbero essere interessati. Ad esempio, nel caso di una richiesta di carta di credito, un modello potrebbe stabilire che le donne dovrebbero ricevere limiti di credito molto più bassi rispetto ai loro coniugi che condividono i beni familiari. Un intero gruppo demografico, definito in base al genere, è così interessato.
+
+### **Definire le metriche di equità**
+
+Si sono identificati i danni e un gruppo interessato, in questo caso, delineato per genere. Ora, si usino i fattori quantificati per disaggregare le loro metriche. Ad esempio, utilizzando i dati di seguito, si può vedere che le donne hanno il più alto tasso di falsi positivi e gli uomini il più piccolo, e che è vero il contrario per i falsi negativi.
+
+✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
+
+|             | percentuale di falsi positivi | Percentuale di falsi negativi | conteggio |
+| ----------- | ----------------------------- | ----------------------------- | --------- |
+| Donna       | 0,37                          | 0,27                          | 54032     |
+| Uomo        | 0,31                          | 0.35                          | 28620     |
+| Non binario | 0,33                          | 0,31                          | 1266      |
+
+Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano  questi numeri.
+
+In questo caso, ci sono 3 gruppi e 2 metriche. Quando si pensa a come il nostro sistema influisce sul gruppo di clienti con i loro richiedenti di prestito, questo può essere sufficiente, ma quando si desidera definire un numero maggiore di gruppi, è possibile distillare questo in insiemi più piccoli di riepiloghi. Per fare ciò, si possono aggiungere più metriche, come la differenza più grande o il rapporto più piccolo di ogni falso negativo e falso positivo.
+
+✅ Ci si fermi a pensare: quali altri gruppi potrebbero essere interessati dalla richiesta di prestito?
+
+## Mitigare l'ingiustizia
+
+Per mitigare l'ingiustizia, si esplori il modello per generare vari modelli mitigati e si confrontino i compromessi tra accuratezza ed equità per selezionare il modello più equo.
+
+Questa lezione introduttiva non approfondisce i dettagli dell'algoritmo della mitigazione dell'ingiustizia, come l'approccio di post-elaborazione e riduzione, ma ecco uno strumento che si potrebbe voler provare.
+
+### Fairlearn
+
+[Fairlearn](https://fairlearn.github.io/) è un pacchetto Python open source che consente di valutare l'equità dei propri sistemi e mitigare l'ingiustizia.
+
+Lo strumento consente di valutare in che modo le previsioni di un modello influiscono su diversi gruppi, consentendo di confrontare più modelli utilizzando metriche di equità e prestazioni e fornendo una serie di algoritmi per mitigare l'ingiustizia nella classificazione binaria e nella regressione.
+
+- Si scopra come utilizzare i diversi componenti controllando il GitHub di [Fairlearn](https://github.com/fairlearn/fairlearn/)
+
+- Si esplori la [guida per l'utente](https://fairlearn.github.io/main/user_guide/index.html), e gli [esempi](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Si provino alcuni [notebook di esempio](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
+
+- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dei modelli di Machine Learning in Azure Machine Learning.
+
+- Si dia un'occhiata a questi [notebook di esempio](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) per ulteriori scenari di valutazione dell'equità in Azure Machine Learning.
+
+---
+
+## 🚀 Sfida
+
+Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
+
+- avere una diversità di background e prospettive tra le persone che lavorano sui sistemi
+- investire in insiemi di dati che riflettano le diversità della società
+- sviluppare metodi migliori per rilevare e correggere i pregiudizi quando si verificano
+
+Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+In questa lezione si sono apprese alcune nozioni di base sui concetti di equità e ingiustizia in machine learning.
+
+Si guardi questo workshop per approfondire gli argomenti:
+
+- YouTube: Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione di Hanna Wallach e Miro Dudik [Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
+
+Si legga anche:
+
+- Centro risorse RAI di Microsoft: [risorse AI responsabili – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4)
+
+- Gruppo di ricerca FATE di Microsoft[: FATE: equità, responsabilità, trasparenza ed etica nell'intelligenza artificiale - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
+
+Si esplori il toolkit Fairlearn
+
+[Fairlearn](https://fairlearn.org/)
+
+Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa)
+
+## Compito
+
+[Esplorare Fairlearn](assignment.it.md)
diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index e84483590..ffa878c17 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -3,7 +3,7 @@
 ![機械学習における公平性をまとめたスケッチ](../../../sketchnotes/ml-fairness.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ja)
  
 ## イントロダクション
 
@@ -88,11 +88,11 @@ AIや機械学習における公平性の保証は、依然として複雑な社
  
 ✅ **ディスカッション**: いくつかの例を再検討し、異なる害を示しているかどうかを確認してください。 
 
-|                         | アロケーション | サービスの質 | 固定観念 | 誹謗中傷 | 過剰表現/過小表現 |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| 採用システムの自動化 |     x      |         x          |      x       |             |               x                |
-| 機械翻訳     |            |                    |              |             |                                |
-| 写真のラベリング          |            |                    |              |             |                                |
+|                      | アロケーション | サービスの質 | 固定観念 | 誹謗中傷 | 過剰表現/過小表現 |
+| -------------------- | :------------: | :----------: | :------: | :------: | :---------------: |
+| 採用システムの自動化 |       x        |      x       |    x     |          |         x         |
+| 機械翻訳             |                |              |          |          |                   |
+| 写真のラベリング     |                |              |          |          |                   |
 
 
 ## 不公平の検出
@@ -134,11 +134,11 @@ AIや機械学習における公平性の保証は、依然として複雑な社
 
 ✅ 今後の"クラスタリング"のレッスンでは、この"混同行列"をコードで構築する方法をご紹介します。
 
-|            | 偽陽性率 | 偽陰性率 | サンプル数 |
-| ---------- | ------------------- | ------------------- | ----- |
-| 女性      | 0.37                | 0.27                | 54032 |
-| 男性        | 0.31                | 0.35                | 28620 |
-| どちらにも属さない | 0.33                | 0.31                | 1266  |
+|                    | 偽陽性率 | 偽陰性率 | サンプル数 |
+| ------------------ | -------- | -------- | ---------- |
+| 女性               | 0.37     | 0.27     | 54032      |
+| 男性               | 0.31     | 0.35     | 28620      |
+| どちらにも属さない | 0.33     | 0.31     | 1266       |
 
 この表から、いくつかのことがわかります。まず、データに含まれる男性と女性どちらでもない人が比較的少ないことがわかります。従ってこのデータは歪んでおり、この数字をどう解釈するかに注意が必要です。
 
@@ -178,7 +178,7 @@ AIや機械学習における公平性の保証は、依然として複雑な社
 
 モデルの構築や使用において、不公平が明らかになるような現実のシナリオを考えてみてください。他にどのようなことを考えるべきでしょうか？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ja)
 ## Review & Self Study 
  
 このレッスンでは、機械学習における公平、不公平の概念の基礎を学びました。
@@ -201,4 +201,4 @@ Azure Machine Learningによる、公平性を確保するためのツールに
 
 ## 課題
 
-[Fairlearnを調査する](../assignment.md) 
+[Fairlearnを調査する](./assignment.ja.md) 
diff --git a/1-Introduction/3-fairness/translations/README.ko.md b/1-Introduction/3-fairness/translations/README.ko.md
new file mode 100644
index 000000000..7cbc8e353
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/README.ko.md
@@ -0,0 +1,214 @@
+# 머신러닝의 공정성 
+ 
+![Summary of Fairness in Machine Learning in a sketchnote](../../../sketchnotes/ml-fairness.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+ 
+## 소개
+
+이 커리큘럼에서, 머신러닝이 우리의 생활에 어떻게 영향을 미칠 수 있는 지 알아보겠습니다. 지금도, 시스템과 모델은 건강 관리 진단 또는 사기 탐지와 같이 일상의 의사-결정 작업에 관여하고 있습니다. 따라서 모두에게 공정한 결과를 주기 위해서는 모델이 잘 작동하는게 중요합니다.
+
+모델을 구축할 때 사용하는 데이터에 인종, 성별, 정치적 관점, 종교와 같이 특정 인구 통계가 부족하거나 불균형하게 나타내는 경우, 어떤 일이 발생할 지 상상해봅시다. 모델의 결과가 일부 인구 통계에 유리하도록 해석하는 경우는 어떨까요? 애플리케이션의 결과는 어떨까요?
+
+이 강의에서, 아래 내용을 합니다:
+
+- 머신러닝에서 공정성의 중요도에 대한 인식을 높입니다.
+- 공정성-관련 피해에 대하여 알아봅니다.
+- 불공정성 평가와 완화에 대하여 알아봅니다.
+
+## 전제 조건
+
+전제 조건으로, "Responsible AI Principles" 학습 과정을 수강하고 주제에 대한 영상을 시청합니다:
+
+[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)를 따라서 Responsible AI에 대하여 더 자세히 알아보세요
+
+[![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
+
+> 🎥 영상을 보려면 이미지 클릭: Microsoft's Approach to Responsible AI
+
+## 데이터와 알고리즘의 불공정성
+
+> "If you torture the data long enough, it will confess to anything" - Ronald Coase
+
+이 소리는 극단적이지만, 결론을 돕기 위하여 데이터를 조작할 수 있다는 건 사실입니다. 의도치않게 발생할 수 있습니다. 사람으로서, 모두 편견을 가지고 있고, 데이터에 편향적일 때 의식하는 것은 어렵습니다.
+
+AI와 머신러닝의 공정성을 보장하는 건 계속 복잡한 사회기술적 도전 과제로 남고 있습니다. 순수하게 사화나 기술 관점에서 다룰 수 없다고 의미합니다.
+
+### 공정성-관련 피해
+
+불공정이란 무엇일까요? "Unfairness"은 인종, 성별, 나이, 또는 장애 등급으로 정의된 인구 그룹에 대한 부정적인 영향 혹은 "harms"를 포함합니다. 
+
+공정성-관련된 주요 피해는 다음처럼 분류할 수 있습니다:
+
+- **할당**, 예를 들자면 성별이나 인종이 다른 사람들보다 선호되는 경우
+- **서비스 품질**. 복잡할 때 한 특정 시나리오에 맞춰 데이터를 훈련하면, 서비스 성능이 낮아집니다.
+- **고정관념**. 지정된 그룹을 사전에 할당한 속성에 넘깁니다.
+- **명예훼손**. 무언가 누군가 부당한 비판하고 라벨링합니다.
+- **과도- 또는 과소- 평가**. 아이디어는 특정 공언에서 그룹을 볼 수 없으며, 같이 피해를 입히는 서비스 혹은 기능을 꾸준히 홍보합니다.
+
+이 예시를 보겠습니다.
+
+### 할당
+
+대출 심사하는 가상의 시스템을 생각해보세요. 이 시스템은 백인 남성을 다른 그룹보다 더 선택하는 경향이 있습니다. 결과적으로, 특정 지원자는 대출이 미뤄집니다.
+
+또 다른 예시로는 후보를 뽑기 위해 대기업에서 개발한 실험적 채용 도구입니다. 모델을 사용하여 다른 단어를 선호하도록 훈련하며 하나의 성별을 특정할 수 있는 도구입니다. 이력서에 "women’s rugby team" 같은 단어가 포함되면 패널티가 주어졌습니다.
+
+✅ 이런 실제 사례를 찾기 위하여 약간 조사해보세요
+
+### 서비스 품질
+
+연구원들은 여러가지 상용 성별 분류기에서 피부가 하얀 남성 이미지와 다르게 피부가 어두운 여성 이미지에서 오류 비율이 더 높다는 것을 발견했습니다. [Reference](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
+
+또 다른 이면에는 피부가 어두운 사람을 잘 인식하지 못하는 비누 디스펜서도 있습니다. [Reference](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+
+### 고정관념
+
+기계 번역에서 성별에 대한 고정관념이 발견되었습니다. “he is a nurse and she is a doctor”라고 터키어로 번역할 때, 문제가 발생했습니다. 터키어는 3인칭을 전달하면서 "o"가 하나인 성별을 가지리지 않지만, 터키어에서 영어로 다시 문장을 번역해보면 “she is a nurse and he is a doctor”라는 고정관념과 부정확하게 반환됩니다.
+
+![translation to Turkish](../images/gender-bias-translate-en-tr.png)
+
+![translation back to English](../images/gender-bias-translate-tr-en.png)
+
+### 명예훼손
+
+이미지 라벨링 기술은 어두운-피부 사람의 이미지를 고릴라로 잘 못 분류했습니다. 잘 못 라벨링된 현상은 denigrate Black people된 오랜 역사를 라벨링하며 적용했으며 시스템이 실수했을 때 해롭습니다.
+
+[![AI: Ain't I a Woman?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
+> 🎥 영상을 보려면 이미지 클릭: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
+
+### 과도- 또는 과소- 평가
+ 
+왜곡된 이미지 검색 결과가 이러한 피해의 올바른 예시가 됩니다. 공학, 또는 CEO와 같이, 여자보다 남자가 높거나 비슷한 비율의 직업 이미지를 검색할 때 특정 성별에 대하여 더 치우친 결과를 보여 줍니다.
+
+![Bing CEO search](../images/ceos.png)
+> This search on Bing for 'CEO' produces pretty inclusive results
+
+5가지의 주요 피해 타입은 mutually exclusive적이지 않으며, 하나의 시스템이 여러 타입의 피해를 나타낼 수 있습니다. 또한, 각 사례들은 심각성이 다릅니다. 예를 들자면, 누군가 범죄자로 부적절하게 노출하는 것은 이미지를 잘못 보여주는 것보다 더 심한 피해입니다. 그러나, 중요한 점은, 상대적으로 심하지 않은 피해도 사람들이 소외감을 느끼거나 피하게 만들 수 있고 쌓인 영향은 꽤 부담이 될 수 있다는 점입니다.
+ 
+✅ **토론**: 몇 가지 예시를 다시 보고 다른 피해가 발생했는지 확인해봅시다.
+
+|                         | Allocation | Quality of service | Stereotyping | Denigration | Over- or under- representation |
+| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
+| Automated hiring system |     x      |         x          |      x       |             |               x                |
+| Machine translation     |            |                    |              |             |                                |
+| Photo labeling          |            |                    |              |             |                                |
+
+
+## 불공정성 감지 
+
+주어진 시스템이 부당하게 동작하는 것은 여러 이유가 존재합니다. 사회 편견을, 예시로 들자면, 훈련에 사용한 데이터셋에 영향을 줄 수 있습니다. 예를 들자면, 채용 불공정성은 이전 데이터에 과하게 의존하여 더욱 악화되었을 가능성이 있습니다. 10년 넘게 회사에 제출된 이력서에서 패턴을 사용했으므로, 이 모델은 대부분의 이력서가 기술업의 과거 지배력을 반영했던 남자가 냈기 때문에 남자가 자격이 있다고 판단했습니다.
+
+특정 그룹의 사람에 대한 적절하지 못한 데이터가 불공정의 이유가 될 수 있습니다. 예를 들자면, 이미지 분류기는 데이터에서 더 어두운 피부 톤를 underrepresented 했으므로 어두운-피부 사람 이미지에 대해 오류 비율이 더 높습니다.
+
+개발하면서 잘 못된 가정을 하면 불공정성을 발생합니다. 예를 들자면, 사람들의 얼굴 이미지를 기반으로 범죄를 저지를 것 같은 사람을 예측하기 위한 얼굴 분석 시스템은 올바르지 못한 가정으로 이어질 수 있습니다. 이는 잘 못 분류된 사람들에게 큰 피해를 줄 수 있습니다.
+
+## 모델 이해하고 공정성 구축하기
+ 
+공정성의 많은 측면은 정량 공정성 지표에 보이지 않고, 공정성을 보장하기 위하여 시스템에서 편향성을 완전히 제거할 수 없지만, 여전히 공정성 문제를 최대한 파악하고 완화할 책임은 있습니다.
+
+머신러닝 모델을 작업할 때, interpretability를 보장하고 불공정성을 평가하며 완화하여 모델을 이해하는 것이 중요합니다.
+
+대출 선택 예시로 케이스를 분리하고 예측에 대한 각 영향 수준을 파악해보겠습니다.
+
+## 평가 방식
+
+1. **피해 (와 이익) 식별하기**. 첫 단계는 피해와 이익을 식별하는 것입니다. 행동과 결정이 잠재적 고객과 비지니스에 어떻게 영향을 미칠 지 생각해봅니다.
+  
+1. **영향받는 그룹 식별하기**. 어떤 종류의 피해나 이익을 발생할 수 있는지 파악했다면, 영향을 받을 수 있는 그룹을 식별합니다. 그룹은 성별, 인종, 또는 사회 집단으로 정의되나요? 
+
+1. **공정성 지표 정의하기**. 마지막으로, 지표를 정의하여 상황을 개선할 작업에서 특정할 무언가를 가집니다.
+
+### 피해 (와 이익) 식별하기
+
+대출과 관련한 피해와 이익은 어떤 것일까요? false negatives와 false positive 시나리오로 생각해보세요:
+
+**False negatives** (거절, but Y=1) - 이 케이스와 같은 경우, 대출금을 상환할 수 있는 신청자가 거절됩니다. 자격있는 신청자에게 대출이 보류되기 때문에 불리한 이벤트입니다.
+
+**False positives** (승인, but Y=0) - 이 케이스와 같은 경우, 신청자는 대출을 받지만 상환하지 못합니다. 결론적으로, 신청자의 케이스는 향후 대출에 영향을 미칠 수 있는 채권 추심으로 넘어갑니다. 
+
+### 영향 받는 그룹 식별
+
+다음 단계는 영향을 받을 것 같은 그룹을 정의하는 것입니다. 예를 들자면, 신용카드를 신청하는 케이스인 경우, 모델은 여자가 가계 재산을 공유하는 배우자에 비해서 매우 낮은 신용도를 받아야 한다고 결정할 수 있습니다. 성별에 의해서, 정의된 전체 인구 통계가 영향 받습니다.
+
+### 공정성 지표 정의
+ 
+피해와 영향받는 그룹을 식별했습니다, 이 케이스와 같은 경우에는, 성별로 표기됩니다. 이제, 정량화된 원인으로 지표를 세분화합니다. 예시로, 아래 데이터를 사용하면, false positive 비율은 여자의 비율이 가장 크고 남자의 비율이 가장 낮으며, false negatives에서는 반대됩니다.
+
+✅ Clustering에 대한 향후 강의에서는, 이 'confusion matrix'을 코드로 어떻게 작성하는 지 봅시다
+
+|            | False positive rate | False negative rate | count |
+| ---------- | ------------------- | ------------------- | ----- |
+| Women      | 0.37                | 0.27                | 54032 |
+| Men        | 0.31                | 0.35                | 28620 |
+| Non-binary | 0.33                | 0.31                | 1266  |
+
+ 
+이 테이블은 몇 가지를 알려줍니다. 먼저, 데이터에 non-binary people이 비교적 적다는 것을 알 수 있습니다. 이 데이터는 왜곡되었으므로, 이런 숫자를 해석하는 것은 조심해야 합니다. 
+
+이러한 케이스는, 3개의 그룹과 2개의 지표가 존재합니다. 시스템이 대출 신청자와 함께 소비자 그룹에 어떤 영향을 미치는지 알아볼 때는, 충분할 수 있지만, 더 많은 수의 그룹을 정의하려는 경우, 더 작은 요약 셋으로 추출할 수 있습니다. 이를 위해서, 각 false negative와 false positive의 가장 큰 차이 또는 최소 비율과 같은, 지표를 더 추가할 수 있습니다. 
+ 
+✅ Stop and Think: 대출 신청에 영향을 받을 수 있는 다른 그룹이 있을까요?
+ 
+## 불공정성 완화
+ 
+불공정성 완화하려면, 모델을 탐색해서 다양하게 완화된 모델을 만들고 가장 공정한 모델을 선택하기 위하여 정확성과 공정성 사이 트레이드오프해서 비교합니다.
+
+입문 강의에서는 post-processing 및 reductions approach과 같은 알고리즘 불공정성 완화에 대한, 세부적인 사항에 대해 깊게 설명하지 않지만, 여기에서 시도할 수 있는 도구가 있습니다. 
+
+### Fairlearn 
+ 
+[Fairlearn](https://fairlearn.github.io/)은 시스템의 공정성을 평가하고 불공정성을 완화할 수있는 오픈소스 Python 패키지입니다.
+
+이 도구는 모델의 예측이 다른 그룹에 미치는 영향을 평가하며 돕고, 공정성과 성능 지표를 사용하여 여러 모델을 비교할 수 있으며, binary classification과 regression의 불공정성을 완화하는 알고리즘 셋을 제공할 수 있습니다.
+
+- Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/)를 확인하고 다양한 컴포넌트를 어떻게 쓰는 지 알아보기.
+
+- [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html) 탐색해보기.
+
+- [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks) 시도해보기. 
+  
+- Azure Machine Learning에서 머신러닝 모델의 [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) 알아보기. 
+  
+- Azure Machine Learning에서 더 공정한 평가 시나리오에 대하여 [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) 확인해보기. 
+
+---
+## 🚀 도전 
+ 
+편견이 처음부터 들어오는 것을 막으려면, 이렇게 해야 합니다:
+
+- 시스템을 작동하는 사람들 사이 다양한 배경과 관점을 가집니다
+- 사회의 다양성을 반영하는 데이터 셋에 투자합니다
+- 편향적일 때에 더 좋은 방법을 개발합니다
+
+모델을 구축하고 사용하면서 불공정한 실-생활 시나리오를 생각해보세요. 어떻게 고려해야 하나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+
+## 검토 & 자기주도 학습
+ 
+이 강의에서, 머신러닝의 공정성과 불공정성 개념에 대한 몇 가지 기본사항을 배웠습니다.
+ 
+워크숍을 보고 토픽에 대하여 깊게 알아봅니다:
+
+- YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+
+또한, 읽어봅시다: 
+
+- Microsoft의 RAI 리소스 센터: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+
+- Microsoft의 FATE research 그룹: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+
+Fairlearn toolkit 탐색합니다
+
+[Fairlearn](https://fairlearn.org/)
+
+공정성을 보장하기 위한 Azure Machine Learning 도구에 대해 읽어봅시다
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+
+## 과제
+
+[Explore Fairlearn](../assignment.md) 
diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 3b75ddab9..5eec4587c 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -1,9 +1,9 @@
 # 机器学习中的公平性
  
 ![机器学习中的公平性概述](../../../sketchnotes/ml-fairness.png)
-> 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
+> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
  
 ## 介绍
 
@@ -76,29 +76,29 @@
 一种图像标记技术，臭名昭著地将深色皮肤的人的图像错误地标记为大猩猩。错误的标签是有害的，不仅仅是因为这个系统犯了一个错误，而且它还特别使用了一个长期以来被故意用来诋毁黑人的标签。
 
 [![AI: 我不是女人吗？](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, 我不是女人吗？")
-> 🎥 点击上图观看视频：AI，我不是女人吗 - 一场展示AI种族主义诋毁造成的伤害的表演
+> 🎥 点击上图观看视频：AI，我不是女人吗 - 一场展示 AI 种族主义诋毁造成的伤害的表演
 
 ### 代表性过高或过低
  
 有倾向性的图像搜索结果就是一个很好的例子。在搜索男性比例等于或高于女性的职业的图片时，比如工程或首席执行官，要注意那些更倾向于特定性别的结果。
 
 ![必应CEO搜索](../images/ceos.png)
-> 在Bing上搜索“CEO”会得到非常全面的结果
+> 在 Bing 上搜索“CEO”会得到非常全面的结果
 
 这五种主要类型的危害不是相互排斥的，一个单一的系统可以表现出一种以上的危害。此外，每个案例的严重程度各不相同。例如，不公平地给某人贴上罪犯的标签比给形象贴上错误的标签要严重得多。然而，重要的是要记住，即使是相对不严重的伤害也会让人感到疏远或被孤立，累积的影响可能会非常压抑。
  
 ✅ **讨论**：重温一些例子，看看它们是否显示出不同的危害。
 
-|                         | 分配 | 服务质量 | 刻板印象 | 诋毁 | 代表性过高或过低 |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| 自动招聘系统 |     x      |         x          |      x       |             |               x                |
-| 机器翻译     |            |                    |              |             |                                |
-| 照片加标签          |            |                    |              |             |                                |
+|              | 分配  | 服务质量 | 刻板印象 | 诋毁  | 代表性过高或过低 |
+| ------------ | :---: | :------: | :------: | :---: | :--------------: |
+| 自动招聘系统 |   x   |    x     |    x     |       |        x         |
+| 机器翻译     |       |          |          |       |                  |
+| 照片加标签   |       |          |          |       |                  |
 
 
 ## 检测不公平
 
-给定系统行为不公平的原因有很多。例如，社会偏见可能会反映在用于训练它们的数据集中。例如，过度依赖历史数据可能会加剧招聘不公平。通过使用过去10年提交给公司的简历中的模式，该模型确定男性更合格，因为大多数简历来自男性，这反映了过去男性在整个科技行业的主导地位。
+给定系统行为不公平的原因有很多。例如，社会偏见可能会反映在用于训练它们的数据集中。例如，过度依赖历史数据可能会加剧招聘不公平。通过使用过去 10 年提交给公司的简历中的模式，该模型确定男性更合格，因为大多数简历来自男性，这反映了过去男性在整个科技行业的主导地位。
 
 关于特定人群的数据不足可能是不公平的原因。例如，图像分类器对于深肤色人的图像具有较高的错误率，因为数据中没有充分代表较深的肤色。
 
@@ -124,9 +124,9 @@
 
 与贷款相关的危害和好处是什么？想想假阴性和假阳性的情况：
 
-**假阴性**（拒绝，但Y=1）-在这种情况下，将拒绝有能力偿还贷款的申请人。这是一个不利的事件，因为贷款的资源是从合格的申请人扣留。
+**假阴性**（拒绝，但 Y=1）-在这种情况下，将拒绝有能力偿还贷款的申请人。这是一个不利的事件，因为贷款的资源是从合格的申请人扣留。
 
-**假阳性**（接受，但Y=0）-在这种情况下，申请人确实获得了贷款，但最终违约。因此，申请人的案件将被送往一个债务催收机构，这可能会影响他们未来的贷款申请。
+**假阳性**（接受，但 Y=0）-在这种情况下，申请人确实获得了贷款，但最终违约。因此，申请人的案件将被送往一个债务催收机构，这可能会影响他们未来的贷款申请。
 
 ### 确定受影响的群体
 
@@ -138,16 +138,16 @@
 
 ✅ 在以后关于聚类的课程中，你将看到如何在代码中构建这个“混淆矩阵”
 
-|            | 假阳性率 | 假阴性率 | 数量 |
-| ---------- | ------------------- | ------------------- | ----- |
-| 女性      | 0.37                | 0.27                | 54032 |
-| 男性        | 0.31                | 0.35                | 28620 |
-| 未列出性别 | 0.33                | 0.31                | 1266  |
+|            | 假阳性率 | 假阴性率 | 数量  |
+| ---------- | -------- | -------- | ----- |
+| 女性       | 0.37     | 0.27     | 54032 |
+| 男性       | 0.31     | 0.35     | 28620 |
+| 未列出性别 | 0.33     | 0.31     | 1266  |
 
  
 这个表格告诉我们几件事。首先，我们注意到数据中的未列出性别的人相对较少。数据是有偏差的，所以你需要小心解释这些数字。
 
-在本例中，我们有3个组和2个度量。当我们考虑我们的系统如何影响贷款申请人的客户群时，这可能就足够了，但是当你想要定义更多的组时，你可能需要将其提取到更小的摘要集。为此，你可以添加更多的度量，例如每个假阴性和假阳性的最大差异或最小比率。
+在本例中，我们有 3 个组和 2 个度量。当我们考虑我们的系统如何影响贷款申请人的客户群时，这可能就足够了，但是当你想要定义更多的组时，你可能需要将其提取到更小的摘要集。为此，你可以添加更多的度量，例如每个假阴性和假阳性的最大差异或最小比率。
  
 ✅ 停下来想一想：还有哪些群体可能会受到贷款申请的影响？
  
@@ -159,19 +159,19 @@
 
 ### Fairlearn 
  
-[Fairlearn](https://fairlearn.github.io/) 是一个开源Python包，可让你评估系统的公平性并减轻不公平性。
+[Fairlearn](https://fairlearn.github.io/) 是一个开源 Python 包，可让你评估系统的公平性并减轻不公平性。
 
 该工具可帮助你评估模型的预测如何影响不同的组，使你能够通过使用公平性和性能指标来比较多个模型，并提供一组算法来减轻二元分类和回归中的不公平性。
 
-- 通过查看Fairlearn的[GitHub](https://github.com/fairlearn/fairlearn/)了解如何使用不同的组件
+- 通过查看 Fairlearn 的 [GitHub](https://github.com/fairlearn/fairlearn/) 了解如何使用不同的组件
 
 - 浏览[用户指南](https://fairlearn.github.io/main/user_guide/index.html), [示例](https://fairlearn.github.io/main/auto_examples/index.html)
 
-- 尝试一些 [示例Notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+- 尝试一些 [示例 Notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
   
 - 了解Azure机器学习中机器学习模型[如何启用公平性评估](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa)。
   
-- 看看这些[示例Notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)了解Azure机器学习中的更多公平性评估场景。
+- 看看这些[示例 Notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)了解 Azure 机器学习中的更多公平性评估场景。
 
 ---
 ## 🚀 挑战 
@@ -186,29 +186,29 @@
 
 想想现实生活中的场景，在模型构建和使用中明显存在不公平。我们还应该考虑什么？
 
-## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
 ## 复习与自学
  
 在本课中，你学习了机器学习中公平和不公平概念的一些基础知识。
 
 观看本次研讨会，深入探讨以下主题：
 
-- YouTube:人工智能系统中与公平相关的危害：示例、评估和缓解Hanna Wallach和Miro Dudik[人工智能系统中与公平相关的危害：示例、评估和缓解-YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+- YouTube:人工智能系统中与公平相关的危害：示例、评估和缓解 Hanna Wallach 和 Miro Dudik[人工智能系统中与公平相关的危害：示例、评估和缓解-YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
 
 另外，请阅读：
 
 - 微软RAI资源中心：[负责人工智能资源-微软人工智能](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
 
-- 微软FATE研究小组：[FATE：AI 中的公平、问责、透明和道德-微软研究院](https://www.microsoft.com/research/theme/fate/) 
+- 微软 FATE 研究小组：[FATE：AI 中的公平、问责、透明和道德-微软研究院](https://www.microsoft.com/research/theme/fate/) 
 
-探索Fairlearn工具箱
+探索 Fairlearn 工具箱
 
 [Fairlearn](https://fairlearn.org/)
 
-了解Azure机器学习的工具以确保公平性
+了解 Azure 机器学习的工具以确保公平性
 
-- [Azure机器学习](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+- [Azure 机器学习](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
 
 ## 任务
 
-[探索Fairlearn](../assignment.md) 
+[探索 Fairlearn](assignment.zh-cn.md) 
diff --git a/1-Introduction/3-fairness/translations/assignment.es.md b/1-Introduction/3-fairness/translations/assignment.es.md
new file mode 100644
index 000000000..cf83256ef
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.es.md
@@ -0,0 +1,11 @@
+# Explore Fairlearn
+
+## Instrucciones
+
+En esta lección, aprendió sobre Fairlearn, un "proyecto open-source impulsado por la comunidad para ayudar a los científicos de datos a mejorar la equidad de los sistemas de AI." Para esta tarea, explore uno de los  [cuadernos](https://fairlearn.org/v0.6.2/auto_examples/index.html) de Fairlearn e informe sus hallazgos en un documento o presentación.
+
+## Rúbrica
+
+| Criterios | Ejemplar | Adecuado | Necesita mejorar |
+| -------- | --------- | -------- | ----------------- |
+|          |  Un documento o presentación powerpoint es presentado discutiendo los sistemas de Fairlearn, el cuadernos que fue ejecutado, y las conclusiones extraídas al ejecutarlo        |   Un documento es presentado sin conclusiones       |  No se presenta ningún documento    |
diff --git a/1-Introduction/3-fairness/translations/assignment.id.md b/1-Introduction/3-fairness/translations/assignment.id.md
new file mode 100644
index 000000000..90389a14d
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.id.md
@@ -0,0 +1,11 @@
+# Jelajahi Fairlearn
+
+## Instruksi
+
+Dalam pelajaran ini kamu telah belajar mengenai Fairlearn, sebuah "proyek *open-source* berbasis komunitas untuk membantu para *data scientist* meningkatkan keadilan dari sistem AI." Untuk penugasan kali ini, jelajahi salah satu dari [notebook](https://fairlearn.org/v0.6.2/auto_examples/index.html) yang disediakan Fairlearn dan laporkan penemuanmu dalam sebuah paper atau presentasi.
+
+## Rubrik
+
+| Kriteria | Sangat Bagus | Cukup | Perlu Peningkatan |
+| -------- | --------- | -------- | ----------------- |
+|          |  Sebuah *paper* atau presentasi powerpoint yang membahas sistem Fairlearn, *notebook* yang dijalankan, dan kesimpulan yang diambil dari hasil menjalankannya         |   Sebuah paper yang dipresentasikan tanpa kesimpulan       |  Tidak ada paper yang dipresentasikan                 |
diff --git a/1-Introduction/3-fairness/translations/assignment.ja.md b/1-Introduction/3-fairness/translations/assignment.ja.md
new file mode 100644
index 000000000..dbf7b2b46
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# Fairlearnを調査する
+
+## 指示
+
+このレッスンでは、「データサイエンティストがAIシステムの公平性を向上させるための、オープンソースでコミュニティ主導のプロジェクト」であるFairlearnについて学習しました。この課題では、Fairlearnの [ノートブック](https://fairlearn.org/v0.6.2/auto_examples/index.html) のうちのひとつを調査し、わかったことをレポートやプレゼンテーションの形で報告してください。
+
+## 評価基準
+
+| 基準 | 模範的                                                                                                                                  | 十分                               | 要改善                     |
+| ---- | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | -------------------------- |
+|      | Fairlearnのシステム・実行したノートブック・実行によって得られた結果が、レポートやパワーポイントのプレゼンテーションとして提示されている | 結論のないレポートが提示されている | レポートが提示されていない |
diff --git a/1-Introduction/3-fairness/translations/assignment.zh-cn.md b/1-Introduction/3-fairness/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..a81241994
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 探索 Fairlearn
+
+## 说明
+
+在这节课中，你了解了 Fairlearn，一个“开源的，社区驱动的项目，旨在帮助数据科学家们提高人工智能系统的公平性”。在这项作业中，探索 Fairlearn [笔记本](https://fairlearn.org/v0.6.2/auto_examples/index.html)中的一个例子，之后你可以用论文或者 ppt 的形式叙述你学习后的发现。
+
+## 评判标准
+
+| 标准 | 优秀 | 中规中矩 | 仍需努力 |
+| -------- | --------- | -------- | ----------------- |
+|          |  提交了一篇论文或者ppt 关于讨论 Fairlearn 系统、挑选运行的例子、和运行这个例子后所得出来的心得结论        |   提交了一篇没有结论的论文       |  没有提交论文                 |
diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index ae9d2c44b..1b87fd7ce 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -4,8 +4,9 @@ The process of building, using, and maintaining machine learning models and the
 
 - Understand the processes underpinning machine learning at a high level.
 - Explore base concepts such as 'models', 'predictions', and 'training data'.
-  
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+
 ## Introduction
 
 On a high level, the craft of creating machine learning (ML) processes is comprised of a number of steps:
@@ -39,14 +40,20 @@ To be able to answer your question with any kind of certainty, you need a good a
 
 ✅ After collecting and processing your data, take a moment to see if its shape will allow you to address your intended question. It may be that the data will not perform well in your given task, as we discover in our [Clustering](../../5-Clustering/1-Visualize/README.md) lessons!
 
-### Selecting your feature variable
+### Features and Target
+
+A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `X` in code, represent the input variable which will be used to train model.
+
+A target is a thing you are trying to predict. Target usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**? Sometimes target is also referred as label attribute.
 
-A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**?
+### Selecting your feature variable
 
 🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
+
 ### Visualize your data
 
-An important aspect of the data scientist's toolkit is the power to  visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+An important aspect of the data scientist's toolkit is the power to visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+
 ### Split your dataset
 
 Prior to training, you need to split your dataset into two or more parts of unequal size that still represent the data well.
@@ -61,10 +68,12 @@ Using your training data, your goal is to build a model, or a statistical repres
 
 ### Decide on a training method
 
-Depending on your question and the nature of your data, your will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
+Depending on your question and the nature of your data, you will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
+
 ### Train a model
 
-Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y').
+Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your feature variable as an array of values (usually 'X') and a target variable (usually 'y').
+
 ### Evaluate the model
 
 Once the training process is complete (it can take many iterations, or 'epochs', to train a large model), you will be able to evaluate the model's quality by using test data to gauge its performance. This data is a subset of the original data that the model has not previously analyzed. You can print out a table of metrics about your model's quality.
@@ -94,7 +103,7 @@ In these lessons, you will discover how to use these steps to prepare, build, te
 
 Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## Review & Self Study
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md
old mode 100644
new mode 100755
index e69de29bb..0121527ef
--- a/1-Introduction/4-techniques-of-ML/translations/README.es.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md
@@ -0,0 +1,112 @@
+# Técnicas de Machine Learning
+
+El proceso de creación, uso y mantenimiento de modelos de machine learning, y los datos que se utilizan, es un proceso muy diferente de muchos otros flujos de trabajo de desarrollo. En esta lección, demistificaremos el proceso, y describiremos las principales técnicas que necesita saber. Vas a:  
+
+- Comprender los procesos que sustentan el machine learning a un alto nivel.
+- Explorar conceptos básicos como 'modelos', 'predicciones', y 'datos de entrenamiento'
+
+ 
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## Introducción
+
+A un alto nivel, el arte de crear procesos de machine learning (ML) se compone de una serie de pasos:
+
+1. **Decidir sobre la pregunta**. La mayoría de los procesos de ML, comienzan por hacer una pregunta que no puede ser respondida por un simple programa condicional o un motor basado en reglas. Esas preguntas a menudo giran en torno a predicciones basadas en una recopilación de datos.
+2. **Recopile y prepare datos**. Para poder responder a su pregunta, necesita datos. La calidad y, a veces, cantidad de sus datos determinarán que tan bien puede responder a su pregunta inicial. La visualización de datos es un aspecto importante de esta fase. Esta fase también incluye dividir los datos en un grupo de entrenamiento y pruebas para construir un modelo.
+3. **Elige un método de entrenamiento**. Dependiendo de su pregunta y la naturaleza de sus datos, debe elegir cómo desea entrenar un modelo para reflejar mejor sus datos y hacer predicciones precisas contra ellos. Esta es la parte de su proceso de ML que requiere experiencia específica y, a menudo, una cantidad considerable de experimetación.
+4. **Entrena el model**. Usando sus datos de entrenamiento, usará varios algoritmos para entrenar un modelo para reconocer patrones en los datos. El modelo puede aprovechar las ponderaciones internas que se pueden ajustar para privilegiar ciertas partes de los datos sobre otras para construir un modelo mejor.
+5. **Evaluar el modelo**. Utiliza datos nunca antes vistos (sus datos de prueba) de su conjunto recopilado para ver cómo se está desempeñando el modelo.
+6. **Ajuste de parámetros**. Según el rendimiento de su modelo, puede rehacer el proceso utilizando diferentes parámetros, o variables, que controlan el comportamiento de los algoritmos utlizados para entrenarl el modelo.
+7. **Predecir**. Utilice nuevas entradas para probar la precisión de su modelo.
+
+## Que pregunta hacer
+
+Las computadoras son particularmente hábiles para descubrir patrones ocultos en los datos. Esta utlidad es muy útil para los investigadores que tienen preguntas sobre un dominio determinado que no pueden responderse fácilmente mediante la creación de un motor de reglas basado en condicionales. Dada una tarea actuarial, por ejemplo, un científico de datos podría construir reglas artesanales sobre la mortalidad de los fumadores frente a los no fumadores.
+
+Sin embargo, cuandos se incorporan muchas otras variables a la ecuación, un modelo de ML podría resultar más eficiente para predecir las tasas de mortalidad futuras en funciòn de los antecedentes de salud. Un ejemplo más alegre podría hacer predicciones meteorólogicas para el mes de abril en una ubicación determinada que incluya latitud, longitud, cambio climático, proximidad al océano, patrones de la corriente en chorro, y más. 
+
+✅ Esta [presentación de diapositivas](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sobre modelos meteorológicos ofrece una perspectiva histórica del uso de ML en el análisis meteorológico.
+
+## Tarea previas a la construcción
+
+Antes de comenzar a construir su modelo, hay varias tareas que debe comletar. Para probar su pregunta y formar una hipótesis basada en las predicciones de su modelo, debe identificar y configurar varios elementos.
+
+### Datos
+
+Para poder responder su pregunta con cualquier tipo de certeza, necesita una buena cantidad de datos del tipo correcto.
+Hay dos cosas que debe hacer en este punto:
+
+- **Recolectar datos**. Teniendo en cuenta la lección anterior sobre la equidad en el análisis de datos, recopile sus datos con cuidado. Tenga en cuenta la fuente de estos datos, cualquier sesgo inherente que pueda tener y documente su origen.
+- **Preparar datos**. Hay varios pasos en el proceso de preparación de datos. Podría necesitar recopilar datos y normalizarlos si provienen de diversas fuentes. Puede mejorar la calidad y cantidad de los datos mediante varios métodos, como convertir strings en números (como hacemos en [Clustering](../../5-Clustering/1-Visualize/README.md)). También puede generar nuevos datos, basados en los originales (como hacemos en [Clasificación](../../4-Classification/1-Introduction/README.md)). Puede limpiar y editar los datos (como lo haremos antes de la lección [Web App](../../3-Web-App/README.md)). Por último, es posible que también deba aleotizarlo y mezclarlo, según sus técnicas de entrenamiento.
+
+✅ Despúes de recopilar y procesar sus datos, tómese un momento para ver si su forma le permitirá responder a su pregunta. ¡Puede ser que los datos no funcionen bien en su tarea dada, como descubriremos en nuestras lecciones de[Clustering](../../5-Clustering/1-Visualize/README.md)!
+
+### Características y destino
+
+Una característica es una propiedad medible de los datos.En muchos conjuntos de datos se expresa como un encabezado de columna como 'date' 'size' o 'color'. La variable de entidad, normalmente representada como `X` en el código, representa la variable de entrada que se utilizará para entrenar el modelo.
+
+Un objetivo es una cosa que está tratando de predecir. Target generalmente representado como `y` en el código, representa la respuesta a la pregunta que está tratando de hacer de sus datos: en diciembre, ¿qué color de calabazas serán más baratas? en San Francisco, ¿qué barrios tendrán el mejor precio de bienes raíces? A veces, target también se conoce como atributo label.
+
+### Seleccionando su variable característica
+
+🎓 **Selección y extracción de características** ¿ Cómo sabe que variable elegir al construir un modelo? Probablemente pasará por un proceso de selección o extracción de características para elegir las variables correctas para mayor un mayor rendimiento del modelo. Sin embargo, no son lo mismo: "La extracción de características crea nuevas características a partir de funciones de las características originales, mientras que la selección de características devuelve un subconjunto de las características." ([fuente](https://wikipedia.org/wiki/Feature_selection))
+
+### Visualiza tus datos
+
+Un aspecto importante del conjunto de herramientas del científico de datos es el poder de visualizar datos utilizando varias bibliotecas excelentes como Seaborn o MatPlotLib. Representar sus datos visualmente puede permitirle descubrir correlaciones ocultas que puede aprovechar. Sus visualizaciones también pueden ayudarlo a descubrir sesgos o datos desequilibrados. (como descubrimos en [Clasificación](../../4-Classification/2-Classifiers-1/README.md)).
+
+### Divide tu conjunto de datos
+
+Antes del entrenamiento, debe dividir su conjunto de datos en dos o más partes de tamaño desigual que aún represente bien los datos.
+
+- **Entrenamiento**. Esta parte del conjunto de datos se ajusta a su modelo para entrenarlo. Este conjunto constituye la mayor parte del conjunto de datos original.
+- **Pruebas**. Un conjunto de datos de pruebas es un grupo independiente de datos, a menudo recopilado a partir de los datos originales, que se utiliza para confirmar el rendimiento del modelo construido.
+- **Validación**. Un conjunto de validación es un pequeño grupo independiente de ejemplos que se usa para ajustar los hiperparámetros o la arquitectura del modelo para mejorar el modelo. Dependiendo del tamaño de de su conjunto de datos y de la pregunta que se está haciendo, es posible que no necesite crear este tercer conjunto (como notamos en [Pronóstico se series de tiempo](../../7-TimeSeries/1-Introduction/README.md)).
+
+## Contruye un modelo
+
+Usando sus datos de entrenamiento, su objetivo es construir un modelo, o una representación estadística de sus datos, usando varios algoritmos para **entrenarlo**. El entrenamiento de un modelo lo expone a los datos y le permite hacer suposiciones sobre los patrones percibidos que descubre, valida y rechaza.
+
+### Decide un método de entrenamiento
+
+Dependiendo de su pregunta y la naturaleza de sus datos, elegirá un método para entrenarlos. Pasando por la [documentación de Scikit-learn ](https://scikit-learn.org/stable/user_guide.html) - que usamos en este curso - puede explorar muchas formas de entrenar un modelo. Dependiendo de su experiencia, es posible que deba probar varios métodos diferentes para construir el mejor modelo. Es probable que pase por un proceso en el que los científicos de datos evalúan el rendimiento de un modelo alimentándolo con datos no vistos anteriormente por el modelo, verificando la precisión, el sesgo, y otros problemas que degradan la calidad, y seleccionando el método de entrenamieto más apropiado para la tarea en custión.
+### Entrena un modelo
+
+Armado con sus datos de entrenamiento, está listo para "ajustarlo" para crear un modelo. Notará que en muchas bibliotecas de ML encontrará el código 'model.fit' - es en este momento que envía su variable de característica como una matriz de valores (generalmente `X`) y una variable de destino (generalmente `y`).
+
+### Evaluar el modelo
+
+Una vez que se completa el proceso de entrenamiento (puede tomar muchas iteraciones, o 'épocas', entrenar un modelo de gran tamaño), podrá evaluar la calidad del modelo utilizando datos de prueba para medir su rendimiento. Estos datos son un subconjunto de los datos originales que el modelo no ha analizado previamente. Puede imprimir una tabla de métricas sobre la calidad de su modelo.
+
+🎓 **Ajuste del modelo (Model fitting)**
+
+En el contexto del machine learning, el ajuste del modelo se refiere a la precisión de la función subyacente del modelo cuando intenta analizar datos con los que no está familiarizado.
+
+🎓 **Ajuste insuficiente (Underfitting)** y **sobreajuste (overfitting)** son problemas comunes que degradan la calidad del modelo, ya que el modelo no encaja suficientemente bien, o encaja demasiado bien. Esto hace que el modelo haga predicciones demasiado estrechamente alineadas o demasiado poco alineadas con sus datos de entrenamiento. Un modelo sobreajustadoo (overfitting) predice demasiado bien los datos de entrenamiento porque ha aprendido demasiado bien los detalles de los datos y el ruido. Un modelo insuficentemente ajustado (Underfitting) es es preciso, ya que ni puede analizar con precisión sus datos de entrenamiento ni los datos que aún no ha 'visto'.
+
+![overfitting model](images/overfitting.png)
+> Infografía de  [Jen Looper](https://twitter.com/jenlooper)
+
+## Ajuste de parámetros
+
+Una vez que haya completado su entrenamiento inicial, observe la calidad del modelo y considere mejorarlo ajustando sus 'hiperparámetros'. Lea más sobre el proceso [en la documentación](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Predicción
+
+Este es el momento en el que puede usar datos completamente nuevos para probar la precisión de su modelo. En una configuración de ML aplicada, donde está creando activos web para usar el modelo en producción, este proceo puede implicar la recopilación de la entrada del usuario (presionar un botón, por ejemplo) para establecer una variable y enviarla al modelo para la inferencia, o evaluación.
+En estas lecciones, descubrirá cómo utilizar estos pasos para preparar, construir, probar, evaluar, y predecir - todos los gestos de un científico de datos y más, a medida que avanza en su viaje para convertirse en un ingeniero de machine learning 'full stack'.
+---
+
+## 🚀Desafío
+
+Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. ¿Dónde te ves ahora mismo en el proceso? ¿Dónde predice que encontrará dificultades? ¿Qué te parece fácil? 
+
+## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+
+## Revisión & Autoestudio
+
+Busque en línea entrevistas con científicos de datos que analicen su trabajo diario. Aquí está [uno](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Asignación
+
+[Entrevistar a un científico de datos](assignment.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
new file mode 100644
index 000000000..699603702
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -0,0 +1,111 @@
+# Teknik-teknik Machine Learning
+
+Proses membangun, menggunakan, dan memelihara model machine learning dan data yang digunakan adalah proses yang sangat berbeda dari banyak alur kerja pengembangan lainnya. Dalam pelajaran ini, kita akan mengungkap prosesnya dan menguraikan teknik utama yang perlu Kamu ketahui. Kamu akan: 
+
+- Memahami gambaran dari proses yang mendasari machine learning.
+- Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '*training data*'. 
+  
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## Pengantar
+
+Gambaran membuat proses machine learning (ML) terdiri dari sejumlah langkah: 
+
+1. **Menentukan pertanyaan**. Sebagian besar proses ML dimulai dengan mengajukan pertanyaan yang tidak dapat dijawab oleh program kondisional sederhana atau mesin berbasis aturan (*rules-based engine*). Pertanyaan-pertanyaan ini sering berkisar seputar prediksi berdasarkan kumpulan data.
+2. **Mengumpulkan dan menyiapkan data**. Untuk dapat menjawab pertanyaanmu, Kamu memerlukan data. Bagaimana kualitas dan terkadang kuantitas data kamu akan menentukan seberapa baik kamu dapat menjawab pertanyaan awal kamu. Memvisualisasikan data merupakan aspek penting dari fase ini. Fase ini juga mencakup pemisahan data menjadi kelompok *training* dan *testing* untuk membangun model.
+3. **Memilih metode training**. Tergantung dari pertanyaan dan sifat datamu, Kamu perlu memilih bagaimana kamu ingin men-training sebuah model untuk mencerminkan data kamu dengan baik dan membuat prediksi yang akurat terhadapnya. Ini adalah bagian dari proses ML yang membutuhkan keahlian khusus dan seringkali perlu banyak eksperimen.
+4. **Melatih model**. Dengan menggunakan data *training*, kamu akan menggunakan berbagai algoritma untuk melatih model guna mengenali pola dalam data. Modelnya mungkin bisa memanfaatkan *internal weight* yang dapat disesuaikan untuk memberi hak istimewa pada bagian tertentu dari data dibandingkan bagian lainnya untuk membangun model yang lebih baik. 
+5. **Mengevaluasi model**. Gunakan data yang belum pernah dilihat sebelumnya (data *testing*) untuk melihat bagaimana kinerja model. 
+6. **Parameter tuning**. Berdasarkan kinerja modelmu, Kamu dapat mengulang prosesnya menggunakan parameter atau variabel yang berbeda, yang mengontrol perilaku algoritma yang digunakan untuk melatih model. 
+7. **Prediksi**. Gunakan input baru untuk menguji keakuratan model kamu. 
+
+## Pertanyaan apa yang harus ditanyakan?
+
+Komputer sangat ahli dalam menemukan pola tersembunyi dalam data. Hal ini sangat membantu peneliti yang memiliki pertanyaan tentang domain tertentu yang tidak dapat dijawab dengan mudah dari hanya membuat mesin berbasis aturan kondisional (*conditionally-based rules engine*). Untuk tugas aktuaria misalnya, seorang data scientist mungkin dapat membuat aturan secara manual seputar mortalitas perokok vs non-perokok. 
+
+Namun, ketika banyak variabel lain dimasukkan ke dalam persamaan, model ML mungkin terbukti lebih efisien untuk memprediksi tingkat mortalitas di masa depan berdasarkan riwayat kesehatan masa lalu. Contoh yang lebih menyenangkan mungkin membuat prediksi cuaca untuk bulan April di lokasi tertentu berdasarkan data yang mencakup garis lintang, garis bujur, perubahan iklim, kedekatan dengan laut, pola aliran udara (Jet Stream), dan banyak lagi. 
+
+✅ [Slide deck](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) ini menawarkan perspektif historis pada model cuaca dengan menggunakan ML dalam analisis cuaca.
+
+## Tugas Pra-Pembuatan
+
+Sebelum mulai membangun model kamu, ada beberapa tugas yang harus kamu selesaikan. Untuk menguji pertanyaan kamu dan membentuk hipotesis berdasarkan prediksi model, Kamu perlu mengidentifikasi dan mengonfigurasi beberapa elemen. 
+
+### Data
+
+Untuk dapat menjawab pertanyaan kamu dengan kepastian, Kamu memerlukan sejumlah besar data dengan jenis yang tepat. Ada dua hal yang perlu kamu lakukan pada saat ini: 
+
+- **Mengumpulkan data**. Ingat pelajaran sebelumnya tentang keadilan dalam analisis data, kumpulkan data kamu dengan hati-hati. Waspadai sumber datanya, bias bawaan apa pun yang mungkin dimiliki, dan dokumentasikan asalnya. 
+- **Menyiapkan data**. Ada beberapa langkah dalam proses persiapan data. Kamu mungkin perlu menyusun data dan melakukan normalisasi jika berasal dari berbagai sumber. Kamu dapat meningkatkan kualitas dan kuantitas data melalui berbagai metode seperti mengonversi string menjadi angka (seperti yang kita lakukan di [Clustering](../../5-Clustering/1-Visualize/translations/README.id.md)). Kamu mungkin juga bisa membuat data baru berdasarkan data yang asli (seperti yang kita lakukan di [Classification](../../4-Classification/1-Introduction/translations/README.id.md)). Kamu bisa membersihkan dan mengubah data (seperti yang kita lakukan sebelum pelajaran [Web App](../3-Web-App/translations/README.id.md)). Terakhir, Kamu mungkin juga perlu mengacaknya dan mengubah urutannya, tergantung pada teknik *training* kamu. 
+
+✅ Setelah mengumpulkan dan memproses data kamu, luangkan waktu sejenak untuk melihat apakah bentuknya memungkinkan kamu untuk menjawab pertanyaan yang kamu maksudkan. Mungkin data tidak akan berkinerja baik dalam tugas yang kamu berikan, seperti yang kita temukan dalam pelajaran [Clustering](../../5-Clustering/1-Visualize/translations/README.id.md).
+
+### Fitur dan Target
+
+Fitur adalah properti terukur dari data Anda. Dalam banyak set data, data tersebut dinyatakan sebagai judul kolom seperti 'date' 'size' atau 'color'. Variabel fitur Anda, biasanya direpresentasikan sebagai `X` dalam kode, mewakili variabel input yang akan digunakan untuk melatih model.
+
+A target is a thing you are trying to predict. Target usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what color pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate price? Sometimes target is also referred as label attribute.
+
+### Memilih variabel fiturmu
+
+🎓 **Feature Selection dan Feature Extraction** Bagaimana kamu tahu variabel mana yang harus dipilih saat membangun model? Kamu mungkin akan melalui proses pemilihan fitur (*Feature Selection*) atau ekstraksi fitur (*Feature Extraction*) untuk memilih variabel yang tepat untuk membuat model yang berkinerja paling baik. Namun, keduanya tidak sama: "Ekstraksi fitur membuat fitur baru dari fungsi fitur asli, sedangkan pemilihan fitur mengembalikan subset fitur." ([sumber](https://wikipedia.org/wiki/Feature_selection))
+### Visualisasikan datamu
+
+Aspek penting dari toolkit data scientist adalah kemampuan untuk memvisualisasikan data menggunakan beberapa *library* seperti Seaborn atau MatPlotLib. Merepresentasikan data kamu secara visual memungkinkan kamu mengungkap korelasi tersembunyi yang dapat kamu manfaatkan. Visualisasimu mungkin juga membantu kamu mengungkap data yang bias atau tidak seimbang (seperti yang kita temukan dalam [Classification](../../4-Classification/2-Classifiers-1/translations/README.id.md)).
+### Membagi dataset
+
+Sebelum memulai *training*, Kamu perlu membagi dataset menjadi dua atau lebih bagian dengan ukuran yang tidak sama tapi masih mewakili data dengan baik.
+
+- **Training**. Bagian dataset ini digunakan untuk men-training model kamu. Bagian dataset ini merupakan mayoritas dari dataset asli. 
+- **Testing**. Sebuah dataset tes adalah kelompok data independen, seringkali dikumpulkan dari data yang asli yang akan digunakan untuk mengkonfirmasi kinerja dari model yang dibuat.
+- **Validating**. Dataset validasi adalah kumpulan contoh mandiri yang lebih kecil yang kamu gunakan untuk menyetel hyperparameter atau arsitektur model untuk meningkatkan model. Tergantung dari ukuran data dan pertanyaan yang kamu ajukan, Kamu mungkin tidak perlu membuat dataset ketiga ini (seperti yang kita catat dalam [Time Series Forecasting](../7-TimeSeries/1-Introduction/translations/README.id.md)).
+
+## Membuat sebuah model
+
+Dengan menggunakan data *training*, tujuan kamu adalah membuat model atau representasi statistik data kamu menggunakan berbagai algoritma untuk **melatihnya**. Melatih model berarti mengeksposnya dengan data dan mengizinkannya membuat asumsi tentang pola yang ditemukan, divalidasi, dan diterima atau ditolak. 
+
+### Tentukan metode training
+
+Tergantung dari pertanyaan dan sifat datamu, Kamu akan memilih metode untuk melatihnya. Buka dokumentasi [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) yang kita gunakan dalam pelajaran ini, kamu bisa menjelajahi banyak cara untuk melatih sebuah model. Tergantung dari pengalamanmu, kamu mungkin perlu mencoba beberapa metode yang berbeda untuk membuat model yang terbaik. Kemungkinan kamu akan melalui proses di mana data scientist mengevaluasi kinerja model dengan memasukkan data yang belum pernah dilihat, memeriksa akurasi, bias, dan masalah penurunan kualitas lainnya, dan memilih metode training yang paling tepat untuk tugas yang ada.
+
+### Melatih sebuah model
+
+Berbekan dengan data pelatihan Anda, Anda siap untuk 'menyesuaikan' untuk membuat model. Anda akan melihat bahwa di banyak perpustakaan ML Anda akan menemukan kode 'model.fit' - saat inilah Anda mengirim variabel fitur Anda sebagai array nilai (biasanya `X`) dan variabel target (biasanya `y`).
+
+### Mengevaluasi model
+
+Setelah proses *training* selesai (ini mungkin membutuhkan banyak iterasi, atau 'epoch', untuk melatih model besar), Kamu akan dapat mengevaluasi kualitas model dengan menggunakan data tes untuk mengukur kinerjanya. Data ini merupakan subset dari data asli yang modelnya belum pernah dianalisis sebelumnya. Kamu dapat mencetak tabel metrik tentang kualitas model kamu. 
+
+🎓 **Model fitting**
+
+Dalam konteks machine learning, *model fitting* mengacu pada keakuratan dari fungsi yang mendasari model saat mencoba menganalisis data yang tidak familiar. 
+
+🎓 **Underfitting** dan **overfitting** adalah masalah umum yang menurunkan kualitas model, karena model tidak cukup akurat atau terlalu akurat. Hal ini menyebabkan model membuat prediksi yang terlalu selaras atau tidak cukup selaras dengan data trainingnya. Model overfit memprediksi data *training* terlalu baik karena telah mempelajari detail dan noise data dengan terlalu baik. Model underfit tidak akurat karena tidak dapat menganalisis data *training* atau data yang belum pernah dilihat sebelumnya secara akurat. 
+
+![overfitting model](../images/overfitting.png)
+> Infografis oleh [Jen Looper](https://twitter.com/jenlooper)
+
+## Parameter tuning
+
+Setelah *training* awal selesai, amati kualitas model dan pertimbangkan untuk meningkatkannya dengan mengubah 'hyperparameter' nya. Baca lebih lanjut tentang prosesnya [di dalam dokumentasi](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Prediksi
+
+Ini adalah saat di mana Kamu dapat menggunakan data yang sama sekali baru untuk menguji akurasi model kamu. Dalam setelan ML 'terapan', di mana kamu membangun aset web untuk menggunakan modelnya dalam produksi, proses ini mungkin melibatkan pengumpulan input pengguna (misalnya menekan tombol) untuk menyetel variabel dan mengirimkannya ke model untuk inferensi, atau evaluasi. 
+
+Dalam pelajaran ini, Kamu akan menemukan cara untuk menggunakan langkah-langkah ini untuk mempersiapkan, membangun, menguji, mengevaluasi, dan memprediksi - semua gestur data scientist dan banyak lagi, seiring kemajuanmu dalam perjalanan menjadi 'full stack' ML engineer. 
+
+---
+
+## 🚀Tantangan
+
+Gambarlah sebuah flow chart yang mencerminkan langkah-langkah seorang praktisi ML. Di mana kamu melihat diri kamu saat ini dalam prosesnya? Di mana kamu memprediksi kamu akan menemukan kesulitan? Apa yang tampak mudah bagi kamu? 
+
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+
+## Ulasan & Belajar Mandiri
+
+Cari di Internet mengenai wawancara dengan data scientist yang mendiskusikan pekerjaan sehari-hari mereka. Ini [salah satunya](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Tugas
+
+[Wawancara dengan data scientist](assignment.id.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
index 2fe5794b7..b13512029 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.it.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -1,110 +1,114 @@
-# Tecniche di Machine Learning
-
-Il processo di creazione, utilizzo e mantenimento dei modelli di machine learning e dei dati che utilizzano è un processo molto diverso da molti altri flussi di lavoro di sviluppo. In questa lezione si demistifica il processo, e si delineano le principali tecniche che occorre conoscere. Si dovrà:
-
-- Comprendere i processi ad alto livello alla base di machine learning.
-- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
-
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
-
-## Introduzione
-
-Ad alto livello, il mestiere di creare processi di apprendimento automatico (ML) comprende una serie di passaggi:
-
-1. **Decidere circa la domanda**. La maggior parte dei processi ML inizia ponendo una domanda alla quale non è possibile ottenere risposta da un semplice programma condizionale o da un motore basato su regole. Queste domande spesso ruotano attorno a previsioni basate su una raccolta di dati.
-2. **Raccogliere e preparare i dati**. Per poter rispondere alla domanda, servono dati. La qualità e, a volte, la quantità dei dati determineranno quanto bene sarà possibile rispondere alla domanda iniziale. La visualizzazione dei dati è un aspetto importante di questa fase. Questa fase include anche la suddivisione dei dati in un gruppo di addestramento (training) e test per costruire un modello.
-3. **Scegliere un metodo di addestramento**. A seconda della domanda e della natura dei dati, è necessario scegliere come si desidera addestrare un modello per riflettere al meglio i dati e fare previsioni accurate su di essi. Questa è la parte del processo di ML che richiede competenze specifiche e, spesso, una notevole quantità di sperimentazione.
-4. **Addestrare il modello**. Usando i dati di addestramento, si utilizzeranno vari algoritmi per addestrare un modello a riconoscere modelli nei dati. Il modello potrebbe sfruttare pesi interni che possono essere regolati per privilegiare alcune parti dei dati rispetto ad altre per costruire un modello migliore.
-5. **Valutare il modello**. Si utilizzano dati mai visti prima (i dati di test) da quelli raccolti per osservare le prestazioni del modello.
-6. **Regolazione dei parametri**. In base alle prestazioni del modello, si può ripetere il processo utilizzando parametri differenti, o variabili, che controllano il comportamento degli algoritmi utilizzati per addestrare il modello.
-7. **Prevedere**. Usare nuovi input per testare la precisione del modello.
-
-## Che domanda fare
-
-I computer sono particolarmente abili nello scoprire modelli nascosti nei dati. Questa caratteristica è molto utile per i ricercatori che hanno domande su un determinato campo a cui non è possibile rispondere facilmente creando un motore di regole basato su condizioni. Dato un compito attuariale, ad esempio, un data scientist potrebbe essere in grado di costruire manualmente regole sulla mortalità dei fumatori rispetto ai non fumatori.
-
-Quando molte altre variabili vengono introdotte nell'equazione, tuttavia, un modello ML potrebbe rivelarsi più efficiente per prevedere i tassi di mortalità futuri in base alla storia sanitaria passata. Un esempio più allegro potrebbe essere fare previsioni meteorologiche per il mese di aprile in una determinata località sulla base di dati che includono latitudine, longitudine, cambiamento climatico, vicinanza all'oceano, modelli della corrente a getto e altro ancora.
-
-✅ Questa [presentazione](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sui modelli meteorologici offre una prospettiva storica per l'utilizzo di ML nell'analisi meteorologica.
-
-## Attività di pre-costruzione
-
-Prima di iniziare a costruire il proprio modello, ci sono diverse attività da completare. Per testare la domanda e formare un'ipotesi basata sulle previsioni di un modello, occorre identificare e configurare diversi elementi.
-
-### Dati
-
-Per poter rispondere con sicurezza alla domanda, serve una buona quantità di dati del tipo giusto. Ci sono due cose da fare a questo punto:
-
-- **Raccogliere dati**. Tenendo presente la lezione precedente sull'equità nell'analisi dei dati, si raccolgano i dati con cura. Ci sia consapevolezza delle fonti di questi dati, di eventuali pregiudizi intrinseci che potrebbero avere e si documenti la loro origine.
-- **Preparare i dati**. Ci sono diversi passaggi nel processo di preparazione dei dati. Potrebbe essere necessario raccogliere i dati e normalizzarli se provengono da fonti diverse. Si può migliorare la qualità e la quantità dei dati attraverso vari metodi come la conversione di stringhe in numeri (come si fa in [Clustering](../../../5-Clustering/1-Visualize/transaltions/README.it.md)). Si potrebbero anche generare nuovi dati, basati sull'originale (come si fa in [Classificazione](../../../4-Classification/1-Introduction/translations/README.it.md)). Si possono pulire e modificare i dati (come verrà fatto prima della lezione sull'[app Web](../../../3-Web-App/translations/README.it.md) ). Infine, si potrebbe anche aver bisogno di renderli casuali e mescolarli, a seconda delle proprie tecniche di addestramento.
-
-✅ Dopo aver raccolto ed elaborato i propri dati, si prenda un momento per vedere se la loro forma consentirà di rispondere alla domanda prevista. Potrebbe essere che i dati non funzionino bene nello svolgere il compito assegnato, come si scopre nelle lezioni di [Clustering](../../../5-Clustering/1-Visualize/translations/README.it.md)!
-
-### Selezione della variabile caratteristica
-
-Una [caratteristica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) è una proprietà misurabile dei propri dati. In molti insiemi di dati è espressa come un'intestazione di colonna come "data", "dimensione" o "colore". La propria variabile caratteristica, solitamente rappresentata come `y` nel codice, rappresenta la risposta alla domanda che si sta cercando di porre ai propri dati: a dicembre, di che **colore** saranno le zucche più economiche? A San Francisco, quali quartieri avranno il miglior **prezzo** immobiliare?
-
-🎓 **Selezione ed estrazione della caratteristica** Come si fa a sapere quale variabile scegliere quando si costruisce un modello? Probabilmente si dovrà passare attraverso un processo di selezione o estrazione delle caratteristiche per scegliere le variabili giuste per il modello più efficace. Tuttavia, non è la stessa cosa: "L'estrazione delle caratteristiche crea nuove caratteristiche dalle funzioni delle caratteristiche originali, mentre la selezione delle caratteristiche restituisce un sottoinsieme delle caratteristiche". ([fonte](https://it.wikipedia.org/wiki/Selezione_delle_caratteristiche))
-
-### Visualizzare i dati
-
-Un aspetto importante del bagaglio del data scientist è la capacità di visualizzare i dati utilizzando diverse eccellenti librerie come Seaborn o MatPlotLib. Rappresentare visivamente i propri dati potrebbe consentire di scoprire correlazioni nascoste che si possono sfruttare. Le visualizzazioni potrebbero anche aiutare a scoprire pregiudizi o dati sbilanciati (come si scopre in [Classificazione](../../../4-Classification/2-Classifiers-1/translations/README.it.md)).
-
-### Dividere l'insieme di dati
-
-Prima dell'addestramento, è necessario dividere l'insieme di dati in due o più parti di dimensioni diverse che rappresentano comunque bene i dati.
-
-- **Addestramento**. Questa parte dell'insieme di dati è adatta al proprio modello per addestrarlo. Questo insieme costituisce la maggior parte dell'insieme di dati originale.
-- **Test**. Un insieme di dati di test è un gruppo indipendente di dati, spesso raccolti dai dati originali, che si utilizzano per confermare le prestazioni del modello creato.
-- **Convalida**. Un insieme di convalida è un gruppo indipendente più piccolo di esempi da usare per ottimizzare gli iperparametri, o architettura, del modello per migliorarlo. A seconda delle dimensioni dei propri dati e della domanda che si sta ponendo, si potrebbe non aver bisogno di creare questo terzo insieme (come si nota in [Previsione delle Serie Temporali](../../../7-TimeSeries/1-Introduction/translations/README.it.md)).
-
-## Costruire un modello
-
-Utilizzando i dati di addestramento, l'obiettivo è costruire un modello o una rappresentazione statistica dei propri dati, utilizzando vari algoritmi per **addestrarlo** . L'addestramento di un modello lo espone ai dati e consente di formulare ipotesi sui modelli percepiti che scopre, convalida e accetta o rifiuta.
-
-### Decidere un metodo di addestramento
-
-A seconda della domanda e della natura dei dati, si sceglierà un metodo per addestrarlo. Passando attraverso [la documentazione di Scikit-learn](https://scikit-learn.org/stable/user_guide.html), che si usa in questo corso, si possono esplorare molti modi per addestrare un modello. A seconda della propria esperienza, si potrebbe dover provare diversi metodi per creare il modello migliore. È probabile che si attraversi un processo in cui i data scientist valutano le prestazioni di un modello fornendogli dati non visti, verificandone l'accuratezza, i pregiudizi e altri problemi che degradano la qualità e selezionando il metodo di addestramento più appropriato per l'attività da svolgere.
-
-### Allenare un modello
-
-Occorre armarsi dei propri dati di allenamento, per essere pronti per "adattarli" per creare un modello. Si noterà che in molte librerie ML si trova il codice "model.fit" - è in questo momento che si inviano i propri dati come un vettore di valori (di solito "X") e una variabile di caratteristica (di solito "y" ).
-
-### Valutare il modello
-
-Una volta completato il processo di addestramento (potrebbero essere necessarie molte iterazioni, o "epoche", per addestrare un modello di grandi dimensioni), si sarà in grado di valutare la qualità del modello utilizzando i dati di test per valutarne le prestazioni. Questi dati sono un sottoinsieme dei dati originali che il modello non ha analizzato in precedenza. Si può stampare una tabella di metriche sulla qualità del proprio modello.
-
-🎓 **Adattamento del modello**
-
-Nel contesto di machine learning, l'adattamento del modello si riferisce all'accuratezza della funzione sottostante del modello mentre tenta di analizzare dati con cui non ha familiarità.
-
-🎓 **Inadeguatezza** o **sovraadattamento** sono problemi comuni che degradano la qualità del modello, poiché il modello non si adatta abbastanza bene o troppo bene. Ciò fa sì che il modello esegua previsioni troppo allineate o troppo poco allineate con i suoi dati di addestramento. Un modello overfit (sovraaddestrato) prevede troppo bene i dati di addestramento perché ha appreso troppo bene i dettagli e il rumore dei dati. Un modello underfit (inadeguato) non è accurato in quanto non può né analizzare accuratamente i suoi dati di allenamento né i dati che non ha ancora "visto".
-
-![modello sovraaddestrato](../images/overfitting.png)
-> Infografica di [Jen Looper](https://twitter.com/jenlooper)
-
-## Sintonia dei parametri
-
-Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
-
-## Previsione
-
-Questo è il momento in cui si possono utilizzare dati completamente nuovi per testare l'accuratezza del proprio modello. In un'impostazione ML "applicata", in cui si creano risorse Web per utilizzare il modello in produzione, questo processo potrebbe comportare la raccolta dell'input dell'utente (ad esempio, la pressione di un pulsante) per impostare una variabile e inviarla al modello per l'inferenza, oppure valutazione.
-
-In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, costruire, testare, valutare e prevedere - tutti gesti di un data scientist e altro ancora, mentre si avanza nel proprio viaggio per diventare un ingegnere ML "full stack".
-
----
-
-## 🚀 Sfida
-
-Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
-
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
-
-## Revisione e Auto Apprendimento
-
-Cercare online le interviste con i data scientist che discutono del loro lavoro quotidiano. Eccone [una](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
-
-## Compito
-
-[Intervista a un data scientist](assignment.it.md)
+# Tecniche di Machine Learning
+
+Il processo di creazione, utilizzo e mantenimento dei modelli di machine learning e dei dati che utilizzano è un processo molto diverso da molti altri flussi di lavoro di sviluppo. In questa lezione si demistifica il processo, e si delineano le principali tecniche che occorre conoscere. Si dovrà:
+
+- Comprendere i processi ad alto livello alla base di machine learning.
+- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/?loc=it)
+
+## Introduzione
+
+Ad alto livello, il mestiere di creare processi di apprendimento automatico (ML) comprende una serie di passaggi:
+
+1. **Decidere circa la domanda**. La maggior parte dei processi ML inizia ponendo una domanda alla quale non è possibile ottenere risposta da un semplice programma condizionale o da un motore basato su regole. Queste domande spesso ruotano attorno a previsioni basate su una raccolta di dati.
+2. **Raccogliere e preparare i dati**. Per poter rispondere alla domanda, servono dati. La qualità e, a volte, la quantità dei dati determineranno quanto bene sarà possibile rispondere alla domanda iniziale. La visualizzazione dei dati è un aspetto importante di questa fase. Questa fase include anche la suddivisione dei dati in un gruppo di addestramento (training) e test per costruire un modello.
+3. **Scegliere un metodo di addestramento**. A seconda della domanda e della natura dei dati, è necessario scegliere come si desidera addestrare un modello per riflettere al meglio i dati e fare previsioni accurate su di essi. Questa è la parte del processo di ML che richiede competenze specifiche e, spesso, una notevole quantità di sperimentazione.
+4. **Addestrare il modello**. Usando i dati di addestramento, si utilizzeranno vari algoritmi per addestrare un modello a riconoscere modelli nei dati. Il modello potrebbe sfruttare pesi interni che possono essere regolati per privilegiare alcune parti dei dati rispetto ad altre per costruire un modello migliore.
+5. **Valutare il modello**. Si utilizzano dati mai visti prima (i dati di test) da quelli raccolti per osservare le prestazioni del modello.
+6. **Regolazione dei parametri**. In base alle prestazioni del modello, si può ripetere il processo utilizzando parametri differenti, o variabili, che controllano il comportamento degli algoritmi utilizzati per addestrare il modello.
+7. **Prevedere**. Usare nuovi input per testare la precisione del modello.
+
+## Che domanda fare
+
+I computer sono particolarmente abili nello scoprire modelli nascosti nei dati. Questa caratteristica è molto utile per i ricercatori che hanno domande su un determinato campo a cui non è possibile rispondere facilmente creando un motore di regole basato su condizioni. Dato un compito attuariale, ad esempio, un data scientist potrebbe essere in grado di costruire manualmente regole sulla mortalità dei fumatori rispetto ai non fumatori.
+
+Quando molte altre variabili vengono introdotte nell'equazione, tuttavia, un modello ML potrebbe rivelarsi più efficiente per prevedere i tassi di mortalità futuri in base alla storia sanitaria passata. Un esempio più allegro potrebbe essere fare previsioni meteorologiche per il mese di aprile in una determinata località sulla base di dati che includono latitudine, longitudine, cambiamento climatico, vicinanza all'oceano, modelli della corrente a getto e altro ancora.
+
+✅ Questa [presentazione](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sui modelli meteorologici offre una prospettiva storica per l'utilizzo di ML nell'analisi meteorologica.
+
+## Attività di pre-costruzione
+
+Prima di iniziare a costruire il proprio modello, ci sono diverse attività da completare. Per testare la domanda e formare un'ipotesi basata sulle previsioni di un modello, occorre identificare e configurare diversi elementi.
+
+### Dati
+
+Per poter rispondere con sicurezza alla domanda, serve una buona quantità di dati del tipo giusto. Ci sono due cose da fare a questo punto:
+
+- **Raccogliere dati**. Tenendo presente la lezione precedente sull'equità nell'analisi dei dati, si raccolgano i dati con cura. Ci sia consapevolezza delle fonti di questi dati, di eventuali pregiudizi intrinseci che potrebbero avere e si documenti la loro origine.
+- **Preparare i dati**. Ci sono diversi passaggi nel processo di preparazione dei dati. Potrebbe essere necessario raccogliere i dati e normalizzarli se provengono da fonti diverse. Si può migliorare la qualità e la quantità dei dati attraverso vari metodi come la conversione di stringhe in numeri (come si fa in [Clustering](../../../5-Clustering/1-Visualize/transaltions/README.it.md)). Si potrebbero anche generare nuovi dati, basati sull'originale (come si fa in [Classificazione](../../../4-Classification/1-Introduction/translations/README.it.md)). Si possono pulire e modificare i dati (come verrà fatto prima della lezione sull'[app Web](../../../3-Web-App/translations/README.it.md) ). Infine, si potrebbe anche aver bisogno di renderli casuali e mescolarli, a seconda delle proprie tecniche di addestramento.
+
+✅ Dopo aver raccolto ed elaborato i propri dati, si prenda un momento per vedere se la loro forma consentirà di rispondere alla domanda prevista. Potrebbe essere che i dati non funzionino bene nello svolgere il compito assegnato, come si scopre nelle lezioni di [Clustering](../../../5-Clustering/1-Visualize/translations/README.it.md)!
+
+### Caratteristiche e destinazione
+
+Una caratteristica è una proprietà misurabile dei dati. In molti set di dati è espresso come intestazione di colonna come 'date' 'size' o 'color'. La variabile di caratteristica, solitamente rappresentata come `X` nel codice, rappresenta la variabile di input che verrà utilizzata per il training del modello.
+
+Un obiettivo è una cosa che stai cercando di prevedere. Target solitamente rappresentato come `y` nel codice, rappresenta la risposta alla domanda che stai cercando di porre dei tuoi dati: a dicembre, di che colore saranno le zucche più economiche? a San Francisco, quali quartieri avranno il miglior prezzo immobiliare? A volte la destinazione viene anche definita attributo label.
+
+### Selezione della variabile caratteristica
+
+🎓 **Selezione ed estrazione della caratteristica** Come si fa a sapere quale variabile scegliere quando si costruisce un modello? Probabilmente si dovrà passare attraverso un processo di selezione o estrazione delle caratteristiche per scegliere le variabili giuste per il modello più efficace. Tuttavia, non è la stessa cosa: "L'estrazione delle caratteristiche crea nuove caratteristiche dalle funzioni delle caratteristiche originali, mentre la selezione delle caratteristiche restituisce un sottoinsieme delle caratteristiche". ([fonte](https://it.wikipedia.org/wiki/Selezione_delle_caratteristiche))
+
+### Visualizzare i dati
+
+Un aspetto importante del bagaglio del data scientist è la capacità di visualizzare i dati utilizzando diverse eccellenti librerie come Seaborn o MatPlotLib. Rappresentare visivamente i propri dati potrebbe consentire di scoprire correlazioni nascoste che si possono sfruttare. Le visualizzazioni potrebbero anche aiutare a scoprire pregiudizi o dati sbilanciati (come si scopre in [Classificazione](../../../4-Classification/2-Classifiers-1/translations/README.it.md)).
+
+### Dividere l'insieme di dati
+
+Prima dell'addestramento, è necessario dividere l'insieme di dati in due o più parti di dimensioni diverse che rappresentano comunque bene i dati.
+
+- **Addestramento**. Questa parte dell'insieme di dati è adatta al proprio modello per addestrarlo. Questo insieme costituisce la maggior parte dell'insieme di dati originale.
+- **Test**. Un insieme di dati di test è un gruppo indipendente di dati, spesso raccolti dai dati originali, che si utilizzano per confermare le prestazioni del modello creato.
+- **Convalida**. Un insieme di convalida è un gruppo indipendente più piccolo di esempi da usare per ottimizzare gli iperparametri, o architettura, del modello per migliorarlo. A seconda delle dimensioni dei propri dati e della domanda che si sta ponendo, si potrebbe non aver bisogno di creare questo terzo insieme (come si nota in [Previsione delle Serie Temporali](../../../7-TimeSeries/1-Introduction/translations/README.it.md)).
+
+## Costruire un modello
+
+Utilizzando i dati di addestramento, l'obiettivo è costruire un modello o una rappresentazione statistica dei propri dati, utilizzando vari algoritmi per **addestrarlo** . L'addestramento di un modello lo espone ai dati e consente di formulare ipotesi sui modelli percepiti che scopre, convalida e accetta o rifiuta.
+
+### Decidere un metodo di addestramento
+
+A seconda della domanda e della natura dei dati, si sceglierà un metodo per addestrarlo. Passando attraverso [la documentazione di Scikit-learn](https://scikit-learn.org/stable/user_guide.html), che si usa in questo corso, si possono esplorare molti modi per addestrare un modello. A seconda della propria esperienza, si potrebbe dover provare diversi metodi per creare il modello migliore. È probabile che si attraversi un processo in cui i data scientist valutano le prestazioni di un modello fornendogli dati non visti, verificandone l'accuratezza, i pregiudizi e altri problemi che degradano la qualità e selezionando il metodo di addestramento più appropriato per l'attività da svolgere.
+
+### Allenare un modello
+
+Armati dei tuoi dati di allenamento, sei pronto a "adattarlo" per creare un modello. Noterai che in molte librerie ML troverai il codice "model.fit" - è in questo momento che invii la tua variabile di funzionalità come matrice di valori (in genere `X`) e una variabile di destinazione (di solito `y`).
+
+### Valutare il modello
+
+Una volta completato il processo di addestramento (potrebbero essere necessarie molte iterazioni, o "epoche", per addestrare un modello di grandi dimensioni), si sarà in grado di valutare la qualità del modello utilizzando i dati di test per valutarne le prestazioni. Questi dati sono un sottoinsieme dei dati originali che il modello non ha analizzato in precedenza. Si può stampare una tabella di metriche sulla qualità del proprio modello.
+
+🎓 **Adattamento del modello**
+
+Nel contesto di machine learning, l'adattamento del modello si riferisce all'accuratezza della funzione sottostante del modello mentre tenta di analizzare dati con cui non ha familiarità.
+
+🎓 **Inadeguatezza** o **sovraadattamento** sono problemi comuni che degradano la qualità del modello, poiché il modello non si adatta abbastanza bene o troppo bene. Ciò fa sì che il modello esegua previsioni troppo allineate o troppo poco allineate con i suoi dati di addestramento. Un modello overfit (sovraaddestrato) prevede troppo bene i dati di addestramento perché ha appreso troppo bene i dettagli e il rumore dei dati. Un modello underfit (inadeguato) non è accurato in quanto non può né analizzare accuratamente i suoi dati di allenamento né i dati che non ha ancora "visto".
+
+![modello sovraaddestrato](../images/overfitting.png)
+> Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+## Sintonia dei parametri
+
+Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Previsione
+
+Questo è il momento in cui si possono utilizzare dati completamente nuovi per testare l'accuratezza del proprio modello. In un'impostazione ML "applicata", in cui si creano risorse Web per utilizzare il modello in produzione, questo processo potrebbe comportare la raccolta dell'input dell'utente (ad esempio, la pressione di un pulsante) per impostare una variabile e inviarla al modello per l'inferenza, oppure valutazione.
+
+In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, costruire, testare, valutare e prevedere - tutti gesti di un data scientist e altro ancora, mentre si avanza nel proprio viaggio per diventare un ingegnere ML "full stack".
+
+---
+
+## 🚀 Sfida
+
+Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Cercare online le interviste con i data scientist che discutono del loro lavoro quotidiano. Eccone [una](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Compito
+
+[Intervista a un data scientist](assignment.it.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ja.md b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
new file mode 100644
index 000000000..13dcdb312
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
@@ -0,0 +1,114 @@
+# 機械学習の手法
+
+機械学習モデルやそのモデルが使用するデータを構築・使用・管理するプロセスは、他の多くの開発ワークフローとは全く異なるものです。このレッスンでは、このプロセスを明快にして、知っておくべき主な手法の概要をまとめます。あなたは、
+
+- 機械学習を支えるプロセスを高い水準で理解します。
+- 「モデル」「予測」「訓練データ」などの基本的な概念を調べます。
+
+## [講義前の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ja)
+
+## 導入
+
+大まかに言うと、機械学習 (Machine Learning: ML) プロセスを作成する技術はいくつかのステップで構成されています。
+
+1. **質問を決める**。ほとんどの機械学習プロセスは、単純な条件のプログラムやルールベースのエンジンでは答えられないような質問をすることから始まります。このような質問は、データの集合を使った予測を中心にされることが多いです。
+2. **データを集めて準備する**。質問に答えるためにはデータが必要です。データの質と、ときには量が、最初の質問にどれだけうまく答えられるかを決めます。データの可視化がこのフェーズの重要な側面です。モデルを構築するためにデータを訓練グループとテストグループに分けることもこのフェーズに含みます。
+3. **学習方法を選ぶ**。質問の内容やデータの性質に応じて、データを最も良く反映して正確に予測できるモデルを、どのように学習するかを選ぶ必要があります。これは機械学習プロセスの中でも、特定の専門知識と、多くの場合はかなりの試行回数が必要になる部分です。
+4. **モデルを学習する**。データのパターンを認識するモデルを学習するために、訓練データと様々なアルゴリズムを使います。モデルはより良いモデルを構築するために、データの特定の部分を優先するように調整できる内部の重みを活用するかもしれません。
+5. **モデルを評価する**。モデルがどのように動作しているかを確認するために、集めたデータの中からまだ見たことのないもの（テストデータ）を使います。
+6. **パラメータチューニング**。モデルの性能によっては、モデルを学習するために使われる、各アルゴリズムの挙動を制御するパラメータや変数を変更してプロセスをやり直すこともできます。
+7. **予測する**。モデルの精度をテストするために新しい入力を使います。
+
+## どのような質問をすれば良いか
+
+コンピュータはデータの中に隠れているパターンを見つけることがとても得意です。この有用性は、条件ベースのルールエンジンを作っても簡単には答えられないような、特定の領域に関する質問を持っている研究者にとって非常に役立ちます。たとえば、ある保険数理の問題があったとして、データサイエンティストは喫煙者と非喫煙者の死亡率に関する法則を自分の手だけでも作れるかもしれません。
+
+しかし、他にも多くの変数が方程式に含まれる場合、過去の健康状態から将来の死亡率を予測する機械学習モデルの方が効率的かもしれません。もっと明るいテーマの例としては、緯度、経度、気候変動、海への近さ、ジェット気流のパターンなどのデータに基づいて、特定の場所における4月の天気を予測することができます。
+
+✅ 気象モデルに関するこの [スライド](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) は、気象解析に機械学習を使う際の歴史的な考え方を示しています。
+
+## 構築前のタスク
+
+モデルの構築を始める前に、いくつかのタスクを完了させる必要があります。質問をテストしたりモデルの予測に基づいた仮説を立てたりするためには、いくつかの要素を特定して設定する必要があります。
+
+### データ
+
+質問に確実に答えるためには、適切な種類のデータが大量に必要になります。ここではやるべきことが2つあります。
+
+- **データを集める**。データ解析における公平性に関する前回の講義を思い出しながら、慎重にデータを集めてください。特定のバイアスを持っているかもしれないデータのソースに注意し、それを記録しておいてください。
+- **データを準備する**。データを準備するプロセスにはいくつかのステップがあります。異なるソースからデータを集めた場合、照合と正規化が必要になるかもしれません。（[クラスタリング](../../../5-Clustering/1-Visualize/README.md) で行っているように、）文字列を数値に変換するなどの様々な方法でデータの質と量を向上させることができます。（[分類](../../../4-Classification/1-Introduction/README.md) で行っているように、）元のデータから新しいデータを生成することもできます。（[Webアプリ](../../../3-Web-App/README.md) の講義の前に行うように、）データをクリーニングしたり編集したりすることができます。最後に、学習の手法によっては、ランダムにしたりシャッフルしたりする必要もあるかもしれません。
+
+✅ データを集めて処理した後は、その形で意図した質問に対応できるかどうかを確認してみましょう。[クラスタリング](../../../5-Clustering/1-Visualize/README.md) の講義でわかるように、データは与えられたタスクに対して上手く機能しないかもしれません！
+
+### 機能とターゲット
+
+フィーチャは、データの測定可能なプロパティです。多くのデータセットでは、'日付' 'サイズ' や '色' のような列見出しとして表現されます。通常、コードでは `X` として表されるフィーチャ変数は、モデルのトレーニングに使用される入力変数を表します
+
+ターゲットは、予測しようとしているものです。ターゲットは通常、コードで`y`として表され、あなたのデータを尋ねようとしている質問に対する答えを表します:12月に、どの色のカボチャが最も安くなりますか?サンフランシスコでは、どの地域が最高の不動産価格を持つでしょうか?ターゲットはラベル属性とも呼ばれることもあります。
+
+### 特徴量の選択
+
+🎓 **特徴選択と特徴抽出** モデルを構築する際にどの変数を選ぶべきかは、どうすればわかるでしょうか？最も性能の高いモデルのためには、適した変数を選択する特徴選択や特徴抽出のプロセスをたどることになるでしょう。しかし、これらは同じものではありません。「特徴抽出は元の特徴の機能から新しい特徴を作成するのに対し、特徴選択は特徴の一部を返すものです。」 ([出典](https://wikipedia.org/wiki/Feature_selection))
+
+### データを可視化する
+
+データサイエンティストの道具に関する重要な側面は、Seaborn や MatPlotLib などの優れたライブラリを使ってデータを可視化する力です。データを視覚的に表現することで、隠れた相関関係を見つけて活用できるかもしれません。また、（[分類](../../../4-Classification/2-Classifiers-1/README.md) でわかるように、）視覚化することで、バイアスやバランシングされていないデータを見つけられるかもしれません。
+
+### データセットを分割する
+
+学習の前にデータセットを2つ以上に分割して、それぞれがデータを表すのに十分かつ不均等な大きさにする必要があります。
+
+- **学習**。データセットのこの部分は、モデルを学習するために適合させます。これは元のデータセットの大部分を占めます。
+- **テスト**。テストデータセットとは、構築したモデルの性能を確認するために使用する独立したデータグループのことで、多くの場合は元のデータから集められます。
+- **検証**。検証セットとは、さらに小さくて独立したサンプルの集合のことで、モデルを改善するためにハイパーパラメータや構造を調整する際に使用されます。（[時系列予測](../../../7-TimeSeries/1-Introduction/README.md) に記載しているように、）データの大きさや質問の内容によっては、この3つ目のセットを作る必要はありません。
+
+## モデルの構築
+
+訓練データと様々なアルゴリズムを使った **学習** によって、モデルもしくはデータの統計的な表現を構築することが目標です。モデルを学習することで、データを扱えるようになったり、発見、検証、肯定または否定したパターンに関する仮説を立てることができたりします。
+
+### 学習方法を決める
+
+質問の内容やデータの性質に応じて、モデルを学習する方法を選択します。このコースで使用する [Scikit-learn のドキュメント](https://scikit-learn.org/stable/user_guide.html) を見ると、モデルを学習する様々な方法を調べられます。経験次第では、最適なモデルを構築するためにいくつかの異なる方法を試す必要があるかもしれません。また、モデルが見たことのないデータを与えたり、質を下げている問題、精度、バイアスについて調べたり、タスクに対して最適な学習方法を選んだりすることで、データサイエンティストが行っている、モデルの性能を評価するプロセスを踏むことになるでしょう。
+
+### モデルを学習する
+
+トレーニングデータを使用して、モデルを作成するために「フィット」する準備が整いました。多くの ML ライブラリでは、コード 'model.fit' が見つかります - この時点で、値の配列 (通常は `X`) とターゲット変数 (通常は `y`) として機能変数を送信します。
+
+### モデルを評価する
+
+（大きなモデルを学習するには多くの反復（エポック）が必要になりますが、）学習プロセスが完了したら、テストデータを使ってモデルの質を評価することができます。このデータは元のデータのうち、モデルがそれまでに分析していないものです。モデルの質を表す指標の表を出力することができます。
+
+🎓 **モデルフィッティング**
+
+機械学習におけるモデルフィッティングは、モデルがまだ知らないデータを分析する際の根本的な機能の精度を参照します。
+
+🎓 **未学習** と **過学習** はモデルの質を下げる一般的な問題で、モデルが十分に適合していないか、または適合しすぎています。これによってモデルは訓練データに近すぎたり遠すぎたりする予測を行います。過学習モデルは、データの詳細やノイズもよく学習しているため、訓練データを上手く予測しすぎてしまいます。未学習モデルは、訓練データやまだ「見たことのない」データを正確に分析することができないため、精度が高くないです。
+
+![過学習モデル](../images/overfitting.png)
+> [Jen Looper](https://twitter.com/jenlooper) さんによる解説画像
+
+## パラメータチューニング
+
+最初のトレーニングが完了したら、モデルの質を観察して、「ハイパーパラメータ」の調整によるモデルの改善を検討しましょう。このプロセスについては [ドキュメント](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa) を読んでください。
+
+## 予測
+
+全く新しいデータを使ってモデルの精度をテストする瞬間です。本番環境でモデルを使用するためにWebアセットを構築するよう「適用された」機械学習の設定においては、推論や評価のためにモデルに渡したり、変数を設定したりするためにユーザの入力（ボタンの押下など）を収集することがこのプロセスに含まれるかもしれません。
+
+この講義では、「フルスタック」の機械学習エンジニアになるための旅をしながら、準備・構築・テスト・評価・予測などのデータサイエンティストが行うすべてのステップの使い方を学びます。
+
+---
+
+## 🚀チャレンジ
+
+機械学習の学習者のステップを反映したフローチャートを描いてください。今の自分はこのプロセスのどこにいると思いますか？どこに困難があると予想しますか？あなたにとって簡単そうなことは何ですか？
+
+## [講義後の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ja)
+
+## 振り返りと自主学習
+
+データサイエンティストが日々の仕事について話しているインタビューをネットで検索してみましょう。ひとつは [これ](https://www.youtube.com/watch?v=Z3IjgbbCEfs) です。
+
+## 課題
+
+[データサイエンティストにインタビューする](assignment.ja.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ko.md b/1-Introduction/4-techniques-of-ML/translations/README.ko.md
new file mode 100644
index 000000000..8e78db853
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ko.md
@@ -0,0 +1,114 @@
+# 머신러닝의 기술
+
+머신러닝 모델과 이를 사용하는 데이터를 구축, 사용, 그리고 관리하는 프로세스는 많은 타 개발 워크플로우와 매우 다른 프로세스입니다. 이 강의에서, 프로세스를 이해하고, 알아야 할 주요 기술을 간단히 설명합니다:
+
+- 머신러닝을 받쳐주는 프로세스를 고수준에서 이해합니다.
+- 'models', 'predictions', 그리고 'training data'와 같은 기초 개념을 탐색합니다.
+  
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+
+## 소개
+
+고수준에서, 머신러닝 (ML) 프로세스를 만드는 기술은 여러 단계로 구성됩니다:
+
+1. **질문 결정하기**. 대부분 ML 프로세스는 간단 조건 프로그램 또는 룰-베이스 엔진으로 대답할 수 없는 질문을 하는 것으로 시작합니다. 이 질문은 가끔 데이터 셋을 기반으로 한 예측을 중심으로 진행됩니다.
+2. **데이터 수집 및 준비하기**. 질문에 대답하려면, 데이터가 필요합니다. 데이터의 품질과, 때때로, 양에 따라 초기 질문에 잘 대답할 수 있는 지 결정됩니다. 데이터 시각화는 이 측면에서 중요합니다. 이 단계에서 데이터를 훈련과 테스트 그룹으로 분할하여 모델을 구축하는 게 포함됩니다.
+3. **학습 방식 선택하기**. 질문과 데이터의 특성에 따라, 데이터를 가장 잘 반영하고 정확한 예측을 할 수 있게 훈련하는 방법을 선택해야 합니다. 특정 전문 지식과, 지속적으로, 많은 실험이 필요한 ML 프로세스의 일부분입니다.
+4. **모델 학습하기**. 학습 데이터로, 다양한 알고리즘을 사용하여 데이터의 패턴을 인식하게 모델을 학습시킵니다. 모델을 더 좋게 만들기 위하여 데이터의 특정 부분을 타 부분보다 먼저 하도록 조정할 수 있도록 내부 가중치를 활용할 수 있습니다.
+5. **모델 평가하기**. 수집한 셋에서 이전에 본 적 없는 데이터 (테스트 데이터)로 모델의 성능을 확인합니다.
+6. **파라미터 튜닝하기**. 모델의 성능을 기반해서, 모델 학습으로 알고리즘의 동작을 컨트롤하는 다른 파라미터, 또는 변수를, 사용해서 프로세스를 다시 실행할 수 있습니다.
+7. **예측하기**. 모델의 정확성을 새로운 입력으로 테스트합니다.
+
+## 물어볼 질문하기
+
+컴퓨터는 데이터에서 숨겨진 패턴 찾는 것을 잘합니다. 유틸리티는 조건-기반 룰 엔진을 만들어서 쉽게 답할 수 없는 도메인에 대해 질문하는 연구원에게 매우 도움이 됩니다. 예를 들어서, actuarial 작업이 주어지면, 데이터 사이언티스트는 흡연자와 비흡연자의 사망률에 대하여 수작업 룰을 작성할 수 있습니다.
+
+많은 다른 변수가 방정식에 포함되면, ML 모델이 과거 건강기록을 기반으로 미래 사망률을 예측하는 데에 효율적이라고 검증할 수 있습니다. 유쾌한 예시로 위도, 경도, 기후 변화, proximity to the ocean, 제트 기류의 패턴을 포함한 데이터 기반으로 주어진 위치에서 4월의 날씨를 예측하는 것입니다.
+
+✅ 날씨 모델에 대한 [slide deck](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf)은 날씨 분석에서 ML을 사용한 역사적 관점을 제공합니다.
+
+## 작업 사전-구축하기
+
+모델을 만들기 전에, 완료해야 할 몇가지 작업이 더 있습니다. 질문을 테스트하고 모델 예측을 기반으로 가설 구성하려면, 여러 요소를 식별하고 구성해야 합니다.
+
+### 데이터
+
+어떠한 종류의 질문을 대답하려면, 올바른 타입의 데이터가 필요합니다. 이 포인트에서 필요한 두 가지가 있습니다:
+
+- **데이터 수집**. 데이터 분석의 공정도를 설명한 이전 강의를 기억하고, 데이터를 조심히 수집합니다. 데이터의 출처와, 내재적 편견을 알고, 출처를 문서화합니다.
+- **데이터 준비**. 데이터 준비 프로세스는 여러 단계가 있습니다. 데이터가 다양한 소스에서 제공되는 경우에는 정렬하고 노멀라이즈해야 할 수 있습니다. ([Clustering](../../../5-Clustering/1-Visualize/README.md)과 같이) 문자열을 숫자로 바꾸는 방식처럼 다양한 방식을 통하여 데이터의 품질과 양을 향상시킬 수 있습니다. ([Classification](../../../4-Classification/1-Introduction/README.md)과 같이) 원본 기반으로, 새로운 데이터를 생성할 수 있습니다. ([Web App](../../../3-Web-App/README.md) 강의 이전처럼) 데이터를 정리하고 변경할 수 있습니다. 마지막으로, 훈련하는 기술에 따라서, 무작위로 섞어야 할 수 있습니다.
+
+✅ 데이터를 수집하고 처리하면, 그 모양이 의도한 질문을 해결할 수 있는 지 잠시 봅니다. [Clustering](../../5-Clustering/1-Visualize/README.md) 강의에서 본 것처럼, 데이터가 주어진 작업에서 잘 수행하지 못할 수 있습니다!
+
+### Features와 타겟
+
+feature는 데이터의 측정할 수 있는 속성입니다. 많은 데이터셋에서 'date' 'size' 또는 'color'처럼 열 제목으로 표현합니다. 일반적으로 코드에서 X로 보여지는 feature 변수는, 모델을 훈련할 때 사용되는 입력 변수로 나타냅니다.
+
+타겟은 예측하려고 시도한 것입니다. 코드에서 X로 표시하는 보통 타겟은, 데이터에 물어보려는 질문의 대답을 나타냅니다: 12월에, 어떤 색의 호박이 가장 쌀까요? San Francisco 근처의 좋은 토지 실제 거래가는 어디인가요? 가끔은 타겟을 라벨 속성이라고 부르기도 합니다.
+
+### feature 변수 선택하기
+
+🎓 **Feature Selection과 Feature Extraction** 모델을 만들 때 선택할 변수를 어떻게 알 수 있을까요? 가장 성능이 좋은 모델에 올바른 변수를 선택하기 위하여 Feature Selection 또는 Feature Extraction 프로세스를 거치게 됩니다. 그러나, 같은 내용이 아닙니다: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
+
+### 데이터 시각화하기
+
+데이터 사이언티스트의 툴킷에서 중요한 측면은 Seaborn 또는 MatPlotLib과 같이 여러가지 뛰어난 라이브러리로 데이터 시각화하는 파워입니다. 데이터를 시각화로 보여주면 숨겨진 correlations를 찾아서 활용할 수 있습니다. ([Classification](../../../4-Classification/2-Classifiers-1/README.md)에서 발견한대로) 시각화는 편향적이거나 균형적이지 않은 데이터를 찾는 데 도움이 될 수 있습니다.
+
+### 데이터셋 나누기
+
+훈련하기 전, 데이터를 잘 나타낼 크기로 2개 이상의 데이터 셋을 나눌 필요가 있습니다.
+
+- **학습**. 데이터셋의 파트는 모델을 학습할 때 적당합니다. 이 셋은 본 데이터셋의 대부분을 차지합니다.
+- **테스트**. 테스트 데이터셋은 독립적인 데이터의 그룹이지만, 미리 만들어진 모델의 성능을 확인할 때에, 가끔 본 데이터에서도 수집됩니다.
+- **검증**. 검증 셋은 모델을 개선하며 모델의 hyperparameters, 또는 architecture를 튜닝할 때, 사용하는 작은 독립된 예시 그룹입니다. ([Time Series Forecasting](../../../7-TimeSeries/1-Introduction/README.md)에서 언급하듯) 데이터의 크기와 질문에 따라서 세번째 셋을 만들 이유가 없습니다.
+
+## 모델 구축하기
+
+훈련하고 있는 데이터를 사용하여, **학습**할 다양한 알고리즘으로, 모델 또는, 데이터의 통계적 표현을 만드는 게 목표입니다. 모델을 학습하면서 데이터에 노출되면 발견, 검증, 그리고 승인하거나 거부되는 perceived patterns에 대하여 가설을 세울 수 있습니다.
+
+### 학습 방식 결정하기
+
+질문과 데이터의 특성에 따라서, 어떻게 학습할 지 선택합니다. [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html)을 - 이 코스에서 - 단계별로 보면 모델이 학습하는 많은 방식을 찾을 수 있습니다. 숙련도에 따라서, 최고의 모델을 만들기 위하여 다른 방식을 해볼 수 있습니다. 데이터 사이언티스트가 볼 수 없는 데이터를 주고 정확도, 편향적, 품질-저하 이슈를 점검해서, 현재 작업에 가장 적당한 학습 방식을 선택하여 모델의 성능을 평가하는 프로세스를 거치게 될 예정입니다.
+
+### 모델 학습하기
+
+훈련 데이터로 감싸면, 모델을 만들 'fit'이 준비 되었습니다. 많은 ML 라이브러리에서 'model.fit' 코드를 찾을 수 있습니다. - 이 순간에 값의 배열 (보통 'X')과 feature 변수 (보통 'y')로 데이터를 보내게 됩니다.
+
+### 모델 평가하기
+
+훈련 프로세스가 완료되면 (큰 모델을 훈련하기 위해서 많이 반복하거나 'epochs'가 요구), 테스트 데이터로 모델의 성능을 측정해서 품질을 평가할 수 있습니다. 데이터는 모델이 이전에 분석하지 않았던 본 데이터의 서브셋입니다. 모델의 품질에 대한 지표 테이블을 출력할 수 있습니다.
+
+🎓 **모델 피팅**
+
+머신러닝의 컨텍스트에서, 모델 피팅은 친근하지 않은 데이터를 분석하려고 시도하는 순간에 모델 기본 기능의 정확도를 보입니다.
+
+🎓 **Underfitting** 과 **overfitting**은 모델 핏이 충분하지 않거나 너무 많을 때, 모델의 품질이 낮아지는 일반적인 이슈입니다. 이러한 이유는 모델이 훈련 데이터와 너무 근접하게 얼라인되거나 너무 느슨하게 얼라인된 예측을 합니다. overfit 모델은 데이터의 디테일과 노이즈를 너무 잘 배웠기에 훈련 데이터로 너무나 잘 예측합니다. underfit 모델은 훈련 데이터 또는 아직 볼 수 없던 데이터를 잘 분석할 수 없으므로 정확하지 않습니다.
+
+![overfitting model](../images/overfitting.png)
+> Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+## 파라미터 튜닝
+
+초반 훈련이 마무리 될 때, 모델의 품질을 살펴보고 'hyperparameters'를 트윅해서 개선하는 것을 고려합니다. [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa) 프로세스에 대하여 알아봅니다.
+
+## 예측
+
+완전히 새 데이터로 모델의 정확도를 테스트할 수 있는 순간입니다. 프로덕션에서 모델을 쓰기 위해서 웹 어셋을 만들며, '적용한' ML 세팅에, 프로세스는 변수를 설정하고 추론하거나, 평가하고자 사용자 입력(예를 들면, 버튼 입력)을 수집해 모델로 보낼 수 있습니다.
+
+이 강의에서는, 'full stack' ML 엔지니어가 되기 위하여 여행을 떠나는 과정이며, 이 단계에 - 데이터 사이언티스트의 모든 제스쳐가 있으며 준비, 빌드, 테스트, 평가와 예측 방식을 보게 됩니다.
+
+---
+
+## 🚀 도전
+
+ML 실무자의 단계를 반영한 플로우를 그려보세요. 프로세스에서 지금 어디에 있는 지 보이나요? 어려운 내용을 예상할 수 있나요? 어떤게 쉬울까요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+
+## 검토 & 자기주도 학습
+
+일상 업무를 이야기하는 데이터 사이언티스트 인터뷰를 온라인으로 검색합니다. 여기 [one](https://www.youtube.com/watch?v=Z3IjgbbCEfs) 있습니다.
+
+## 과제
+
+[Interview a data scientist](../assignment.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index d01d5bbfe..4c3d3454e 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
 - 在高层次上理解支持机器学习的过程。 
 - 探索基本概念，例如“模型”、“预测”和“训练数据”。 
   
-## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
 ## 介绍
 
 在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
@@ -40,9 +40,13 @@
 
 ✅ 在收集和处理你的数据后，花点时间看看它的形状是否能让你解决你的预期问题。正如我们在[聚类](../../../5-Clustering/1-Visualize/README.md)课程中发现的那样，数据可能在你的给定任务中表现不佳！
 
-### 选择特征变量
+### 功能和目标
+
+功能是数据的可测量属性。在许多数据集中，它表示为标题为"日期""大小"或"颜色"的列。您的功能变量（通常在代码中表示为 `X`）表示用于训练模型的输入变量。
 
-[特征](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数据的可衡量属性。在许多数据集中，它表示为列标题，如“日期”、“大小”或“颜色”。你的特征变量（通常在代码中表示为`y`）代表你试图对数据提出的问题的答案：在12月，哪种**颜色**的南瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
+目标就是你试图预测的事情。目标通常表示为代码中的 `y`，代表您试图询问数据的问题的答案：在 12 月，什么颜色的南瓜最便宜？在旧金山，哪些街区的房地产价格最好？有时目标也称为标签属性。
+
+### 选择特征变量
 
 🎓 **特征选择和特征提取** 构建模型时如何知道选择哪个变量？你可能会经历一个特征选择或特征提取的过程，以便为性能最好的模型选择正确的变量。然而，它们不是一回事：“特征提取是从基于原始特征的函数中创建新特征，而特征选择返回特征的一个子集。”（[来源](https://wikipedia.org/wiki/Feature_selection)）
 ### 可视化数据
@@ -54,7 +58,7 @@
 
 - **训练**。这部分数据集适合你的模型进行训练。这个集合构成了原始数据集的大部分。
 - **测试**。测试数据集是一组独立的数据，通常从原始数据中收集，用于确认构建模型的性能。
-- **验证**。验证集是一个较小的独立示例组，用于调整模型的超参数或架构，以改进模型。根据你的数据大小和你提出的问题，你可能不需要构建第三组（正如我们在[时间序列预测](../../7-TimeSeries/1-Introduction/README.md)中所述）。 
+- **验证**。验证集是一个较小的独立示例组，用于调整模型的超参数或架构，以改进模型。根据你的数据大小和你提出的问题，你可能不需要构建第三组（正如我们在[时间序列预测](../../../7-TimeSeries/1-Introduction/README.md)中所述）。 
 
 ## 建立模型 
 
@@ -66,20 +70,20 @@
 
 ### 训练模型
 
-有了你的训练数据，你就可以“拟合”它以创建模型。你会注意到，在许多ML库中，你会找到代码'model.fit'——此时你将数据作为值数组（通常为'X'）和特征变量（通常为'y'）发送）。 
+有了您的培训数据，您就可以"适应"它来创建模型。您会注意到，在许多 ML 库中，您会发现代码"model.fit"-此时，您将功能变量作为一系列值（通常是`X`）和目标变量（通常是`y`）发送。 
 
 ### 评估模型 
 
 训练过程完成后（训练大型模型可能需要多次迭代或“时期”），你将能够通过使用测试数据来衡量模型的性能来评估模型的质量。此数据是模型先前未分析的原始数据的子集。 你可以打印出有关模型质量的指标表。 
 
-🎓 **模型拟合 **
+🎓 **模型拟合**
 
 在机器学习的背景下，模型拟合是指模型在尝试分析不熟悉的数据时其底层功能的准确性。 
 
 🎓 **欠拟合**和**过拟合**是降低模型质量的常见问题，因为模型拟合得不够好或太好。这会导致模型做出与其训练数据过于紧密对齐或过于松散对齐的预测。 过拟合模型对训练数据的预测太好，因为它已经很好地了解了数据的细节和噪声。欠拟合模型并不准确，因为它既不能准确分析其训练数据，也不能准确分析尚未“看到”的数据。
 
 ![过拟合模型 ](../images/overfitting.png)
-> 作者[Jen Looper](https://twitter.com/jenlooper)
+> 作者 [Jen Looper](https://twitter.com/jenlooper)
 
 ## 参数调优 
 
@@ -97,7 +101,7 @@
 
 画一个流程图，反映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会遇到困难？什么对你来说很容易？ 
 
-## [阅读后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## 复习与自学 
 
@@ -105,4 +109,4 @@
 
 ## 任务
 
-[采访一名数据科学家](../assignment.md)
+[采访一名数据科学家](assignment.zh-cn.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.id.md b/1-Introduction/4-techniques-of-ML/translations/assignment.id.md
new file mode 100644
index 000000000..9f7b23be7
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.id.md
@@ -0,0 +1,11 @@
+# Wawancara seorang data scientist
+
+## Instruksi
+
+Di perusahaan Kamu, dalam user group, atau di antara teman atau sesama siswa, berbicaralah dengan seseorang yang bekerja secara profesional sebagai data scientist. Tulis makalah singkat (500 kata) tentang pekerjaan sehari-hari mereka. Apakah mereka spesialis, atau apakah mereka bekerja 'full stack'? 
+
+## Rubrik
+
+| Kriteria | Sangat Bagus                                                                            | Cukup                                                           | Perlu Peningkatan     |
+| -------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------ | --------------------- |
+|          | Sebuah esai dengan panjang yang sesuai, dengan sumber yang dikaitkan, disajikan sebagai file .doc | Esai dikaitkan dengan buruk atau lebih pendek dari panjang yang dibutuhkan | Tidak ada esai yang disajikan |
diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.ja.md b/1-Introduction/4-techniques-of-ML/translations/assignment.ja.md
new file mode 100644
index 000000000..b3690e770
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# データサイエンティストにインタビューする
+
+## 指示
+
+会社・ユーザグループ・友人・学生仲間の中で、データサイエンティストとして専門的に働いている人に話を聞いてみましょう。その人の日々の仕事について短いレポート（500語）を書いてください。その人は専門家でしょうか？それとも「フルスタック」として働いているでしょうか？
+
+## 評価基準
+
+| 基準 | 模範的                                                                 | 十分                                                           | 要改善                     |
+| ---- | ---------------------------------------------------------------------- | -------------------------------------------------------------- | -------------------------- |
+|      | 出典が明記された適切な長さのレポートが.docファイルとして提示されている | レポートに出典が明記されていない、もしくは必要な長さよりも短い | レポートが提示されていない |
diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..ba28b5549
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 采访一位数据科学家
+
+## 说明
+
+在你的公司、你所在的社群、或者在你的朋友和同学中，找到一位从事数据科学专业工作的人，与他或她交流一下。写一篇关于他们工作日常的小短文（500字左右）。他们是专家，还是说他们是“全栈”开发者？
+
+## 评判标准
+
+| 标准 | 优秀                                                                            | 中规中矩                                                           | 仍需努力     |
+| -------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------ | --------------------- |
+|          | 提交一篇清晰描述了职业属性且字数符合规范的word文档 | 提交的文档职业属性描述得不清晰或者字数不合规范 | 啥都没有交 |
diff --git a/1-Introduction/translations/README.fr.md b/1-Introduction/translations/README.fr.md
index c27f9bef1..462dea70e 100644
--- a/1-Introduction/translations/README.fr.md
+++ b/1-Introduction/translations/README.fr.md
@@ -7,10 +7,10 @@ Dans cette section du programme, vous découvrirez les concepts de base sous-jac
   
 ### Leçons
 
-1. [Introduction au machine learning](1-intro-to-ML/README.md)
-1. [L’histoire du machine learning et de l’IA](2-history-of-ML/README.md)
-1. [Équité et machine learning](3-équité/README.md)
-1. [Techniques de machine learning](4-techniques-of-ML/README.md)
+1. [Introduction au machine learning](../1-intro-to-ML/translations/README.fr.md)
+1. [L’histoire du machine learning et de l’IA](../2-history-of-ML/translations/README.fr.md)
+1. [Équité et machine learning](../3-fairness/translations/README.fr.md)
+1. [Techniques de machine learning](../4-techniques-of-ML/translations/README.fr.md)
 ### Crédits
 
 "Introduction au machine learning" a été écrit avec ♥️ par une équipe de personnes comprenant [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) et [Jen Looper](https://twitter.com/jenlooper)
diff --git a/1-Introduction/translations/README.id.md b/1-Introduction/translations/README.id.md
new file mode 100644
index 000000000..0e6cc5575
--- /dev/null
+++ b/1-Introduction/translations/README.id.md
@@ -0,0 +1,23 @@
+# Pengantar Machine Learning
+
+Di bagian kurikulum ini, Kamu akan berkenalan dengan konsep yang mendasari bidang Machine Learning, apa itu Machine Learning, dan belajar mengenai
+sejarah serta teknik-teknik yang digunakan oleh para peneliti. Ayo jelajahi dunia baru Machine Learning bersama! 
+
+![bola dunia](../images/globe.jpg)
+> Foto oleh <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> di <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+### Pelajaran
+
+1. [Pengantar Machine Learning](../1-intro-to-ML/translations/README.id.md)
+1. [Sejarah dari Machine Learning dan AI](../2-history-of-ML/translations/README.id.md)
+1. [Keadilan dan Machine Learning](../3-fairness/translations/README.id.md)
+1. [Teknik-Teknik Machine Learning](../4-techniques-of-ML/translations/README.id.md)
+### Penghargaan
+
+"Pengantar Machine Learning" ditulis dengan ♥️ oleh sebuah tim yang terdiri dari [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) dan [Jen Looper](https://twitter.com/jenlooper)
+
+"Sejarah dari Machine Learning dan AI" ditulis dengan ♥️ oleh [Jen Looper](https://twitter.com/jenlooper) dan [Amy Boyd](https://twitter.com/AmyKateNicho)
+
+"Keadilan dan Machine Learning" ditulis dengan ♥️ oleh [Tomomi Imura](https://twitter.com/girliemac) 
+
+"Teknik-Teknik Machine Learning" ditulis dengan ♥️ oleh [Jen Looper](https://twitter.com/jenlooper) dan [Chris Noring](https://twitter.com/softchris) 
diff --git a/1-Introduction/translations/README.ko.md b/1-Introduction/translations/README.ko.md
new file mode 100644
index 000000000..4a5147a6f
--- /dev/null
+++ b/1-Introduction/translations/README.ko.md
@@ -0,0 +1,23 @@
+# 머신러닝 소개하기
+
+커리큘럼의 이 섹션에서, 머신러닝 필드의 기초가 될 기본 개념, 의미, 역사와 연구자가 이용하는 기술을 배울 예정입니다. 새로운 ML의 세계로 같이 모험을 떠납시다!
+
+![globe](../images/globe.jpg)
+> Photo by <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> on <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+### 강의
+
+1. [머신러닝 소개하기](../1-intro-to-ML/translations/README.ko.md)
+1. [머신러닝과 AI의 역사](../2-history-of-ML/translations/README.ko.md)
+1. [공정성과 머신러닝](../3-fairness/translations/README.ko.md)
+1. [머신러닝의 기술](../4-techniques-of-ML/translations/README.ko.md)
+
+### 크레딧
+
+"Introduction to Machine Learning" was written with ♥️ by a team of folks including [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) and [Jen Looper](https://twitter.com/jenlooper)
+
+"The History of Machine Learning" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper) and [Amy Boyd](https://twitter.com/AmyKateNicho)
+
+"Fairness and Machine Learning" was written with ♥️ by [Tomomi Imura](https://twitter.com/girliemac) 
+
+"Techniques of Machine Learning" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper) and [Chris Noring](https://twitter.com/softchris) 
\ No newline at end of file
diff --git a/1-Introduction/translations/README.zh-cn.md b/1-Introduction/translations/README.zh-cn.md
index f1ad8e1eb..507856461 100644
--- a/1-Introduction/translations/README.zh-cn.md
+++ b/1-Introduction/translations/README.zh-cn.md
@@ -1,6 +1,6 @@
 # 机器学习入门
 
-课程的本章节将为您介绍机器学习领域背后的基本概念、什么是机器学习，并学习它的历史以及曾为此做出贡献的技术研究者门。让我们一起开始探索机器学习的全新世界吧！
+课程的本章节将为您介绍机器学习领域背后的基本概念、什么是机器学习，并学习它的历史以及曾为此做出贡献的技术研究者们。让我们一起开始探索机器学习的全新世界吧！
 
 ![globe](../images/globe.jpg)
 > 图片由 <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a>提供，来自 <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index e36c34fe6..d5ca6d124 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -4,7 +4,10 @@
 
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+
+> ### [This lesson is available in R!](./solution/R/lesson_1-R.ipynb)
+
 ## Introduction
 
 In these four lessons, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!
@@ -18,7 +21,7 @@ In this lesson, you will learn how to:
 
 ## Installations and configurations
 
-[![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
+[![Setup Python with Visual Studio Code](https://img.youtube.com/vi/yyQM70vi7V8/0.jpg)](https://youtu.be/yyQM70vi7V8 "Setup Python with Visual Studio Code")
 
 > 🎥 Click the image above for a video: using Python within VS Code.
 
@@ -32,7 +35,7 @@ In this lesson, you will learn how to:
 
 3. **Install Scikit-learn**, by following [these instructions](https://scikit-learn.org/stable/install.html). Since you need to ensure that you use Python 3, it's recommended that you use a virtual environment. Note, if you are installing this library on a M1 Mac, there are special instructions on the page linked above.
 
-1. **Install Jupyter Notebook**. You will need to [install the Jupyter package](https://pypi.org/project/jupyter/). 
+1. **Install Jupyter Notebook**. You will need to [install the Jupyter package](https://pypi.org/project/jupyter/).
 
 ## Your ML authoring environment
 
@@ -42,7 +45,7 @@ Notebooks are an interactive environment that allow the developer to both code a
 
 ### Exercise - work with a notebook
 
-In this folder, you will find the file _notebook.ipynb_. 
+In this folder, you will find the file _notebook.ipynb_.
 
 1. Open _notebook.ipynb_ in Visual Studio Code.
 
@@ -50,7 +53,7 @@ In this folder, you will find the file _notebook.ipynb_.
 
 1. Select the `md` icon and add a bit of markdown, and the following text **# Welcome to your notebook**.
 
-   Next, add some Python code. 
+   Next, add some Python code.
 
 1. Type **print('hello notebook')** in the code block.
 1. Select the arrow to run the code.
@@ -73,7 +76,7 @@ Now that Python is set up in your local environment, and you are comfortable wit
 
 According to their [website](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
 
-In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
+In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum.
 
 Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.
 
@@ -95,7 +98,7 @@ For this task we will import some libraries:
 
 - **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
 - **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
-- **sklearn**. This is the Scikit-learn library.
+- **sklearn**. This is the [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) library.
 
 Import some libraries to help with your tasks.
 
@@ -113,10 +116,10 @@ Import some libraries to help with your tasks.
 
 The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
 
-age: age in years
-bmi: body mass index
-bp: average blood pressure
-s1 tc: T-Cells (a type of white blood cells)
+- age: age in years
+- bmi: body mass index
+- bp: average blood pressure
+- s1 tc: T-Cells (a type of white blood cells)
 
 ✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
 
@@ -124,7 +127,7 @@ Now, load up the X and y data.
 
 > 🎓 Remember, this is supervised learning, and we need a named 'y' target.
 
-In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target. 
+In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target.
 
 1. Add some print commands to show the shape of the data matrix and its first element:
 
@@ -180,6 +183,9 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     ```python
     plt.scatter(X_test, y_test,  color='black')
     plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.xlabel('Scaled BMIs')
+    plt.ylabel('Disease Progression')
+    plt.title('A Graph Plot Showing Diabetes Progression Against BMI')
     plt.show()
     ```
 
@@ -193,7 +199,7 @@ Congratulations, you built your first linear regression model, created a predict
 ## 🚀Challenge
 
 Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## Review & Self Study
 
@@ -201,6 +207,6 @@ In this tutorial, you worked with simple linear regression, rather than univaria
 
 Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding.
 
-## Assignment 
+## Assignment
 
 [A different dataset](assignment.md)
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diff --git a/2-Regression/1-Tools/solution/Julia/README.md b/2-Regression/1-Tools/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/2-Regression/1-Tools/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
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index 000000000..335ee5e79
--- /dev/null
+++ b/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,441 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "lesson_1-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: Get started with R and Tidymodels for regression models"
+      ],
+      "metadata": {
+        "id": "YJUHCXqK57yz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Introduction to Regression - Lesson 1\n",
+        "\n",
+        "#### Putting it into perspective\n",
+        "\n",
+        "✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.\n",
+        "\n",
+        "In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.\n",
+        "\n",
+        "That said, let's get started on this task!\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/encouRage.jpg\"\n",
+        "   width=\"630\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "<!--![Artwork by \\@allison_horst](../../images/encouRage.jpg)<br>Artwork by @allison_horst-->"
+      ],
+      "metadata": {
+        "id": "LWNNzfqd6feZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Loading up our tool set\n",
+        "\n",
+        "For this task, we'll require the following packages:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+        "\n",
+        "The script below checks whether you have the packages required to complete this module and installs them for you in case some are missing."
+      ],
+      "metadata": {
+        "id": "FIo2YhO26wI9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "source": [
+        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+        "pacman::p_load(tidyverse, tidymodels)"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Loading required package: pacman\n",
+            "\n"
+          ]
+        }
+      ],
+      "metadata": {
+        "id": "cIA9fz9v7Dss",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's load these awesome packages and make them available in our current R session.(This is for mere illustration, `pacman::p_load()` already did that for you)"
+      ],
+      "metadata": {
+        "id": "gpO_P_6f9WUG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# load the core Tidyverse packages\r\n",
+        "library(tidyverse)\r\n",
+        "\r\n",
+        "# load the core Tidymodels packages\r\n",
+        "library(tidymodels)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "NLMycgG-9ezO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. The diabetes dataset\n",
+        "\n",
+        "In this exercise, we'll put our regression skills into display by making predictions on a diabetes dataset. The [diabetes dataset](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) includes `442 samples` of data around diabetes, with 10 predictor feature variables, `age`, `sex`, `body mass index`, `average blood pressure`, and `six blood serum measurements` as well as an outcome variable `y`: a quantitative measure of disease progression one year after baseline.\n",
+        "\n",
+        "|Number of observations|442|\n",
+        "|----------------------|:---|\n",
+        "|Number of predictors|First 10 columns are numeric predictive|\n",
+        "|Outcome/Target|Column 11 is a quantitative measure of disease progression one year after baseline|\n",
+        "|Predictor Information|- age in years\n",
+        "||- sex\n",
+        "||- bmi body mass index\n",
+        "||- bp average blood pressure\n",
+        "||- s1 tc, total serum cholesterol\n",
+        "||- s2 ldl, low-density lipoproteins\n",
+        "||- s3 hdl, high-density lipoproteins\n",
+        "||- s4 tch, total cholesterol / HDL\n",
+        "||- s5 ltg, possibly log of serum triglycerides level\n",
+        "||- s6 glu, blood sugar level|\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "> 🎓 Remember, this is supervised learning, and we need a named 'y' target.\n",
+        "\n",
+        "Before you can manipulate data with R, you need to import the data into R's memory, or build a connection to the data that R can use to access the data remotely.\n",
+        "\n",
+        "> The [readr](https://readr.tidyverse.org/) package, which is part of the Tidyverse, provides a fast and friendly way to read rectangular data into R.\n",
+        "\n",
+        "Now, let's load the diabetes dataset provided in this source URL: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+        "\n",
+        "Also, we'll perform a sanity check on our data using `glimpse()` and dsiplay the first 5 rows using `slice()`.\n",
+        "\n",
+        "Before going any further, let's also introduce something you will encounter often in R code 🥁🥁: the pipe operator `%>%`\n",
+        "\n",
+        "The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying \"and then\" in your code."
+      ],
+      "metadata": {
+        "id": "KM6iXLH996Cl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Import the data set\r\n",
+        "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Get a glimpse and dimensions of the data\r\n",
+        "glimpse(diabetes)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Select the first 5 rows of the data\r\n",
+        "diabetes %>% \r\n",
+        "  slice(1:5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Z1geAMhM-bSP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "`glimpse()` shows us that this data has 442 rows and 11 columns with all the columns being of data type `double` \n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "\n",
+        "\n",
+        "> glimpse() and slice() are functions in [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, part of the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "Now that we have the data, let's narrow down to one feature (`bmi`) to target for this exercise. This will require us to select the desired columns. So, how do we do this?\n",
+        "\n",
+        "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) allows us to *select* (and optionally rename) columns in a data frame."
+      ],
+      "metadata": {
+        "id": "UwjVT1Hz-c3Z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Select predictor feature `bmi` and outcome `y`\r\n",
+        "diabetes_select <- diabetes %>% \r\n",
+        "  select(c(bmi, y))\r\n",
+        "\r\n",
+        "# Print the first 5 rows\r\n",
+        "diabetes_select %>% \r\n",
+        "  slice(1:10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "RDY1oAKI-m80"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Training and Testing data\n",
+        "\n",
+        "It's common practice in supervised learning to *split* the data into two subsets; a (typically larger) set with which to train the model, and a smaller \"hold-back\" set with which to see how the model performed.\n",
+        "\n",
+        "Now that we have data ready, we can see if a machine can help determine a logical split between the numbers in this dataset. We can use the [rsample](https://tidymodels.github.io/rsample/) package, which is part of the Tidymodels framework, to create an object that contains the information on *how* to split the data, and then two more rsample functions to extract the created training and testing sets:\n"
+      ],
+      "metadata": {
+        "id": "SDk668xK-tc3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "set.seed(2056)\r\n",
+        "# Split 67% of the data for training and the rest for tesing\r\n",
+        "diabetes_split <- diabetes_select %>% \r\n",
+        "  initial_split(prop = 0.67)\r\n",
+        "\r\n",
+        "# Extract the resulting train and test sets\r\n",
+        "diabetes_train <- training(diabetes_split)\r\n",
+        "diabetes_test <- testing(diabetes_split)\r\n",
+        "\r\n",
+        "# Print the first 3 rows of the training set\r\n",
+        "diabetes_train %>% \r\n",
+        "  slice(1:10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "EqtHx129-1h-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Train a linear regression model with Tidymodels\n",
+        "\n",
+        "Now we are ready to train our model!\n",
+        "\n",
+        "In Tidymodels, you specify models using `parsnip()` by specifying three concepts:\n",
+        "\n",
+        "-   Model **type** differentiates models such as linear regression, logistic regression, decision tree models, and so forth.\n",
+        "\n",
+        "-   Model **mode** includes common options like regression and classification; some model types support either of these while some only have one mode.\n",
+        "\n",
+        "-   Model **engine** is the computational tool which will be used to fit the model. Often these are R packages, such as **`\"lm\"`** or **`\"ranger\"`**\n",
+        "\n",
+        "This modeling information is captured in a model specification, so let's build one!"
+      ],
+      "metadata": {
+        "id": "sBOS-XhB-6v7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Build a linear model specification\r\n",
+        "lm_spec <- \r\n",
+        "  # Type\r\n",
+        "  linear_reg() %>% \r\n",
+        "  # Engine\r\n",
+        "  set_engine(\"lm\") %>% \r\n",
+        "  # Mode\r\n",
+        "  set_mode(\"regression\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the model specification\r\n",
+        "lm_spec"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "20OwEw20--t3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "After a model has been *specified*, the model can be `estimated` or `trained` using the [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) function, typically using a formula and some data.\n",
+        "\n",
+        "`y ~ .` means we'll fit `y` as the predicted quantity/target, explained by all the predictors/features ie, `.` (in this case, we only have one predictor: `bmi` )"
+      ],
+      "metadata": {
+        "id": "_oDHs89k_CJj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Build a linear model specification\r\n",
+        "lm_spec <- linear_reg() %>% \r\n",
+        "  set_engine(\"lm\") %>%\r\n",
+        "  set_mode(\"regression\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Train a linear regression model\r\n",
+        "lm_mod <- lm_spec %>% \r\n",
+        "  fit(y ~ ., data = diabetes_train)\r\n",
+        "\r\n",
+        "# Print the model\r\n",
+        "lm_mod"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "YlsHqd-q_GJQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.\n",
+        "<br>\n",
+        "\n",
+        "## 5. Make predictions on the test set\n",
+        "\n",
+        "Now that we've trained a model, we can use it to predict the disease progression y for the test dataset using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). This will be used to draw the line between data groups."
+      ],
+      "metadata": {
+        "id": "kGZ22RQj_Olu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make predictions for the test set\r\n",
+        "predictions <- lm_mod %>% \r\n",
+        "  predict(new_data = diabetes_test)\r\n",
+        "\r\n",
+        "# Print out some of the predictions\r\n",
+        "predictions %>% \r\n",
+        "  slice(1:5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "nXHbY7M2_aao"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Woohoo! 💃🕺 We just trained a model and used it to make predictions!\n",
+        "\n",
+        "When making predictions, the tidymodels convention is to always produce a tibble/data frame of results with standardized column names. This makes it easy to combine the original data and the predictions in a usable format for subsequent operations such as plotting.\n",
+        "\n",
+        "`dplyr::bind_cols()` efficiently binds multiple data frames column."
+      ],
+      "metadata": {
+        "id": "R_JstwUY_bIs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Combine the predictions and the original test set\r\n",
+        "results <- diabetes_test %>% \r\n",
+        "  bind_cols(predictions)\r\n",
+        "\r\n",
+        "\r\n",
+        "results %>% \r\n",
+        "  slice(1:5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "RybsMJR7_iI8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 6. Plot modelling results\n",
+        "\n",
+        "Now, its time to see this visually 📈. We'll create a scatter plot of all the `y` and `bmi` values of the test set, then use the predictions to draw a line in the most appropriate place, between the model's data groupings.\n",
+        "\n",
+        "R has several systems for making graphs, but `ggplot2` is one of the most elegant and most versatile. This allows you to compose graphs by **combining independent components**."
+      ],
+      "metadata": {
+        "id": "XJbYbMZW_n_s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Set a theme for the plot\r\n",
+        "theme_set(theme_light())\r\n",
+        "# Create a scatter plot\r\n",
+        "results %>% \r\n",
+        "  ggplot(aes(x = bmi)) +\r\n",
+        "  # Add a scatter plot\r\n",
+        "  geom_point(aes(y = y), size = 1.6) +\r\n",
+        "  # Add a line plot\r\n",
+        "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "R9tYp3VW_sTn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.\n",
+        "\n",
+        "Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!\n"
+      ],
+      "metadata": {
+        "id": "zrPtHIxx_tNI"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/1-Tools/solution/R/lesson_1.Rmd b/2-Regression/1-Tools/solution/R/lesson_1.Rmd
new file mode 100644
index 000000000..cd2afebff
--- /dev/null
+++ b/2-Regression/1-Tools/solution/R/lesson_1.Rmd
@@ -0,0 +1,250 @@
+---
+title: 'Build a regression model: Get started with R and Tidymodels for regression models'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Introduction to Regression - Lesson 1
+
+#### Putting it into perspective
+
+✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+
+In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+
+That said, let's get started on this task!
+
+![Artwork by \@allison_horst](../../images/encouRage.jpg){width="630"}
+
+## 1. Loading up our tool set
+
+For this task, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels"))`
+
+The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+if (!require("pacman")) install.packages("pacman")
+pacman::p_load(tidyverse, tidymodels)
+```
+
+Now, let's load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)
+
+```{r load_tidy_verse_models, message=F, warning=F}
+# load the core Tidyverse packages
+library(tidyverse)
+
+# load the core Tidymodels packages
+library(tidymodels)
+
+
+```
+
+## 2. The diabetes dataset
+
+In this exercise, we'll put our regression skills into display by making predictions on a diabetes dataset. The [diabetes dataset](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) includes `442 samples` of data around diabetes, with 10 predictor feature variables, `age`, `sex`, `body mass index`, `average blood pressure`, and `six blood serum measurements` as well as an outcome variable `y`: a quantitative measure of disease progression one year after baseline.
+
++----------------------------+------------------------------------------------------------------------------------+
+| **Number of observations** | **442**                                                                            |
++============================+====================================================================================+
+| **Number of predictors**   | First 10 columns are numeric predictive values                                     |
++----------------------------+------------------------------------------------------------------------------------+
+| **Outcome/Target**         | Column 11 is a quantitative measure of disease progression one year after baseline |
++----------------------------+------------------------------------------------------------------------------------+
+| **Predictor Information**  | -   age age in years                                                               |
+|                            | -   sex                                                                            |
+|                            | -   bmi body mass index                                                            |
+|                            | -   bp average blood pressure                                                      |
+|                            | -   s1 tc, total serum cholesterol                                                 |
+|                            | -   s2 ldl, low-density lipoproteins                                               |
+|                            | -   s3 hdl, high-density lipoproteins                                              |
+|                            | -   s4 tch, total cholesterol / HDL                                                |
+|                            | -   s5 ltg, possibly log of serum triglycerides level                              |
+|                            | -   s6 glu, blood sugar level                                                      |
++----------------------------+------------------------------------------------------------------------------------+
+
+> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+
+Before you can manipulate data with R, you need to import the data into R's memory, or build a connection to the data that R can use to access the data remotely.\
+
+> The [readr](https://readr.tidyverse.org/) package, which is part of the Tidyverse, provides a fast and friendly way to read rectangular data into R.
+
+Now, let's load the diabetes dataset provided in this source URL: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>
+
+Also, we'll perform a sanity check on our data using `glimpse()` and dsiplay the first 5 rows using `slice()`.
+
+Before going any further, let's introduce something you will encounter quite often in R code: the pipe operator `%>%`
+
+The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying "and then" in your code.\
+
+```{r load_dataset, message=F, warning=F}
+# Import the data set
+diabetes <- read_table2(file = "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt")
+
+
+# Get a glimpse and dimensions of the data
+glimpse(diabetes)
+
+
+# Select the first 5 rows of the data
+diabetes %>% 
+  slice(1:5)
+
+```
+
+`glimpse()` shows us that this data has 442 rows and 11 columns with all the columns being of data type `double`
+
+> glimpse() and slice() are functions in [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, part of the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges
+
+Now that we have the data, let's narrow down to one feature (`bmi`) to target for this exercise. This will require us to select the desired columns. So, how do we do this?
+
+[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) allows us to *select* (and optionally rename) columns in a data frame.
+
+```{r select, message=F, warning=F}
+# Select predictor feature `bmi` and outcome `y`
+diabetes_select <- diabetes %>% 
+  select(c(bmi, y))
+
+# Print the first 5 rows
+diabetes_select %>% 
+  slice(1:5)
+```
+
+## 3. Training and Testing data
+
+It's common practice in supervised learning to *split* the data into two subsets; a (typically larger) set with which to train the model, and a smaller "hold-back" set with which to see how the model performed.
+
+Now that we have data ready, we can see if a machine can help determine a logical split between the numbers in this dataset. We can use the [rsample](https://tidymodels.github.io/rsample/) package, which is part of the Tidymodels framework, to create an object that contains the information on *how* to split the data, and then two more rsample functions to extract the created training and testing sets:
+
+```{r split, message=F, warning=F}
+set.seed(2056)
+# Split 67% of the data for training and the rest for tesing
+diabetes_split <- diabetes_select %>% 
+  initial_split(prop = 0.67)
+
+# Extract the resulting train and test sets
+diabetes_train <- training(diabetes_split)
+diabetes_test <- testing(diabetes_split)
+
+# Print the first 3 rows of the training set
+diabetes_train %>% 
+  slice(1:3)
+
+```
+
+## 4. Train a linear regression model with Tidymodels
+
+Now we are ready to train our model!
+
+In Tidymodels, you specify models using `parsnip()` by specifying three concepts:
+
+-   Model **type** differentiates models such as linear regression, logistic regression, decision tree models, and so forth.
+
+-   Model **mode** includes common options like regression and classification; some model types support either of these while some only have one mode.
+
+-   Model **engine** is the computational tool which will be used to fit the model. Often these are R packages, such as **`"lm"`** or **`"ranger"`**
+
+This modeling information is captured in a model specification, so let's build one!
+
+```{r lm_model_spec, message=F, warning=F}
+# Build a linear model specification
+lm_spec <- 
+  # Type
+  linear_reg() %>% 
+  # Engine
+  set_engine("lm") %>% 
+  # Mode
+  set_mode("regression")
+
+
+# Print the model specification
+lm_spec
+
+```
+
+After a model has been *specified*, the model can be `estimated` or `trained` using the [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) function, typically using a formula and some data.
+
+`y ~ .` means we'll fit `y` as the predicted quantity/target, explained by all the predictors/features ie, `.` (in this case, we only have one predictor: `bmi` )
+
+```{r train, message=F, warning=F}
+# Build a linear model specification
+lm_spec <- linear_reg() %>% 
+  set_engine("lm") %>%
+  set_mode("regression")
+
+
+# Train a linear regression model
+lm_mod <- lm_spec %>% 
+  fit(y ~ ., data = diabetes_train)
+
+# Print the model
+lm_mod
+```
+
+From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.
+
+## 5. Make predictions on the test set
+
+Now that we've trained a model, we can use it to predict the disease progression y for the test dataset using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). This will be used to draw the line between data groups.
+
+```{r test, message=F, warning=F}
+# Make predictions for the test set
+predictions <- lm_mod %>% 
+  predict(new_data = diabetes_test)
+
+# Print out some of the predictions
+predictions %>% 
+  slice(1:5)
+```
+
+Woohoo! 💃🕺 We just trained a model and used it to make predictions!
+
+When making predictions, the tidymodels convention is to always produce a tibble/data frame of results with standardized column names. This makes it easy to combine the original data and the predictions in a usable format for subsequent operations such as plotting.
+
+`dplyr::bind_cols()` efficiently binds multiple data frames column.
+
+```{r test_pred, message=F, warning=F}
+# Combine the predictions and the original test set
+results <- diabetes_test %>% 
+  bind_cols(predictions)
+
+
+results %>% 
+  slice(1:5)
+```
+
+## 6. Plot modelling results
+
+Now, its time to see this visually 📈. We'll create a scatter plot of all the `y` and `bmi` values of the test set, then use the predictions to draw a line in the most appropriate place, between the model's data groupings.
+
+R has several systems for making graphs, but `ggplot2` is one of the most elegant and most versatile. This allows you to compose graphs by **combining independent components**.
+
+```{r plot_pred, message=F, warning=F}
+# Set a theme for the plot
+theme_set(theme_light())
+# Create a scatter plot
+results %>% 
+  ggplot(aes(x = bmi)) +
+  # Add a scatter plot
+  geom_point(aes(y = y), size = 1.6) +
+  # Add a line plot
+  geom_line(aes(y = .pred), color = "blue", size = 1.5)
+  
+```
+
+> ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+
+Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
diff --git a/2-Regression/1-Tools/solution/notebook.ipynb b/2-Regression/1-Tools/solution/notebook.ipynb
index e7d80492a..ceb81b9c7 100644
--- a/2-Regression/1-Tools/solution/notebook.ipynb
+++ b/2-Regression/1-Tools/solution/notebook.ipynb
@@ -28,7 +28,7 @@
  "cells": [
   {
    "source": [
-    "## Linear Regression for North American Pumpkins - Lesson 1"
+    "## Linear Regression for Diabetes dataset - Lesson 1"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -182,13 +182,6 @@
     "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/2-Regression/1-Tools/translations/README.es.md b/2-Regression/1-Tools/translations/README.es.md
old mode 100644
new mode 100755
index e69de29bb..e6de7a29c
--- a/2-Regression/1-Tools/translations/README.es.md
+++ b/2-Regression/1-Tools/translations/README.es.md
@@ -0,0 +1,206 @@
+# Comience con Python y Scikit-learn para modelos de regresión
+
+![Resumen de regresiones en un boceto](../../sketchnotes/ml-regression.png)
+
+> Boceto de [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Cuestionario previo](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## Introducción
+
+En estas cuatro lecciones, descubrirá como crear modelos de regresión. Discutiremos para que sirven estos en breve. Pero antes de hacer cualquier coasa, asegúrese de tener las herramientas adecuadas para comenzar el proceso!
+
+En esta lección, aprenderá a:
+
+- Configurar su computadora para tares locales de machine learning.
+- Trabajar con cuadernos Jupyter.
+- Usar Scikit-learn, incluida la instalación.
+- Explorar la regressión lineal con un ejercicio práctico.
+
+## Instalaciones y configuraciones.
+
+[![Uso de Python con Visual Studio Code](https://img.youtube.com/vi/yyQM70vi7V8/0.jpg)](https://youtu.be/yyQM70vi7V8 "Uso de Python con Visual Studio Code")
+
+> 🎥 Haga click en la imagen de arriba para ver un video: usando Python dentro de VS Code.
+
+1. **Instale Python**. Asegúrese de que [Python](https://www.python.org/downloads/) esté instalado en su computadora. Utilizará Python para muchas tareas de ciencia de datos y machine learning. La mayoría de los sistemas informáticos ya incluyen una instalación de Python. También hay disponibles [paquetes de código de Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) útiles para facilitar la configuración a algunos usuarios.
+
+   Sin embargo algunos usos de Python requieren una versión del software, mientras otros requieren una versión diferente. Por esta razón, es útil trabajar dentro de un [entorno virtual](https://docs.python.org/3/library/venv.html).
+
+2. **Instale Visual Studio Code**. Asegúrese de tener Visual Studio Code instalado en su computadora. Siga estas instrucciones para [instalar Visual Studio Code](https://code.visualstudio.com/) para la instalación básica. Va a utilizar Python en Visual Studio Code en este curso, por lo que es posible que desee repasar cómo [configurar Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) para el desarrollo en Python.
+
+   > Siéntase cómodo con Python trabajando con esta colección de [módulos de aprendizaje](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+
+3. **Instale Scikit-learn**, siguiendo [estas instrucciones](https://scikit-learn.org/stable/install.html). Dado que debe asegurarse de usar Python3, se recomienda que use un entorno virtual. Tenga en cuenta que si está instalando esta biblioteca en una Mac M1, hay instrucciones especiales en la página vinculada arriba.
+
+1. **Instale Jupyter Notebook**. Deberá [instalar el paquete de Jupyter](https://pypi.org/project/jupyter/). 
+
+## El entorno de creación de ML
+
+Utilizará **cuadernos** para desarrollar su código en Python y crear modelos de machine learning. Este tipo de archivos es una herramienta común para científicos de datos, y se pueden identificar por su sufijo o extensión `.ipynb`.
+
+Los cuadernos son un entorno interactivo que permiten al desarrollador codificar y agregar notas y escribir documentación sobre el código lo cual es bastante útil para proyectos experimentales u orientados a la investigación.
+### Ejercicio - trabajar con un cuaderno
+
+En esta carpeta, encontrará el archivo _notebook.ipynb_. 
+
+1. Abra _notebook.ipynb_ en Visual Studio Code.
+
+Un servidor de Jupyter comenzará con Python 3+ iniciado. Encontrará áreas del cuaderno que se pueden ejecutar, fragmentos de código. Puede ejecutar un bloque de código seleccionando el icono que parece un botón de reproducción.
+
+1. Seleccione el icono `md` y agrege un poco de _markdown_, y el siguiente texto **# Welcome to your notebook**.
+
+   A continuación, agrege algo de código Python. 
+
+1. Escriba **print('hello notebook')** en el bloque de código.
+1. Seleccione la flecha para ejecutar el código.
+
+   Debería ver impresa la declaración:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code con un cuaderno abierto](images/notebook.png)
+
+Puede intercalar su código con comentarios para autodocumentar el cuaderno.
+
+✅ Piense por un minuto en cuán diferente es el entorno de trabajo de un desarrollador web en comparación con el de un científico de datos.
+
+## En funcionamiento con Scikit-learn
+
+Ahora que Python está configurado en un entorno local, y se siente cómo con los cuadernos de Jupyter, vamos a sentirnos igualmente cómodos con Scikit-learn (pronuncie `sci` como en `science`). Scikit-learn proporciona una [API extensa](https://scikit-learn.org/stable/modules/classes.html#api-ref) para ayudarlo a realizar tares de ML.
+
+Según su [sitio web](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn es una biblioteca de machine learning de código abierto que admite el aprendizaje supervisado y no supervisado. También proporciona varias herramientas para el ajuste de modelos, preprocesamiento de datos, selección y evaluación de modelos, y muchas otras utilidades."
+
+En este curso, utilizará Scikit-learn y otras herramientas para crear modelos de machine learning para realizar lo que llamamos tareas de 'machine leraning tradicional'. Hemos evitado deliberadamente las redes neuronales y el _deep learning_, ya que se tratartán mejor en nuestro próximo plan de estudios 'IA para principiantes'. 
+
+Scikit-learn hace que sea sencillo construir modelos y evaluarlos para su uso. Se centra principalmente en el uso de datos numéricos y contiene varios conjuntos de datos listos para usar como herramientas de aprendizaje. También incluye modelos prediseñados para que los estudiantes lo prueben. Exploremos el proceso de cargar datos preempaquetados y el uso de un primer modelo de estimador integrado con Scikit-learn con algunos datos básicos.
+
+## Ejercicio - su primer cuaderno de Scikit-learn
+
+> Este tutorial se insipiró en el [ejemplo de regresión lineal](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) en el sitio web de Scikit-learn's.
+
+En el archivo _notebook.ipynb_ asociado a esta lección, borre todas las celdas presionando el icono 'papelera'.
+
+En esta sección, trabajará con un pequeño conjunto de datos sobre la diabetes que está integrado con Scikit-learn con fines de aprendizaje. Imagínese que quisiera probar un tratamiento para pacientes diabéticos. Los modelos de Machine Learning, pueden ayudarlo a determinar que pacientes responderían mejor al tratamiento, en función  de combinanciones de varibales. Incluso un modelo de regresión muy básico, cuando se visualiza, puede mostrar información sobre variables que le ayudarían en sus ensayos clínicos teóricos.
+
+✅ Hay muchos tipos de métodos de regresión y el que elija dependerá de las respuestas que esté buscando. Si desea predecir la altura probable de una persona de una edad determinada, utlizaría la regresión lineal, ya que busca un **valor numérico**. Si está interesado en descubrir si un tipo de cocina puede considerarse vegano o no, está buscando una **asignación de categoría**, por lo que utlilizaría la regresión logística. Más adelante aprenderá más sobre la regresión logística. Piense un poco en algunas preguntas que puede puede hacer a los datos y cuáles de estos métodos sería más apropiado.
+
+Comencemos con esta tarea.
+
+### Importar bibliotecas
+
+Para esta tarea importaremos algunas librerías:
+
+- **matplotlib**. Es una [herramienta gráfica](https://matplotlib.org/)  útil y la usaremos para crear un diagrama de líneas.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) es una librería útil para manejar datos numéricos en Python.
+- **sklearn**. Esta es la librería Scikit-learn.
+
+Importar algunas librerías para ayudarte con tus tareas.
+
+1. Agrege importaciones escribiendo el siguiente código:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+Arriba estás importando `matplottlib`, `numpy` y estás importando `datasets`, `linear_model` y `model_selection` de `sklearn`. `model_selection` se usa para dividir datos en conjuntos de entrenamiento y de prueba.
+
+### El conjunto de datos de diabetes
+
+El [conjunto de datos de diabetes](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) incluye 442 muestras de datos sobre la diabetes, con 10 variables de características, algunas de las cuales incluyen:
+
+edad: edad en años.
+bmi: índice de masa corporal.
+bp: presión arterial promedio.
+s1 tc: Células-T (un tipo de glóbulos blancos).
+
+✅ Este conjunto de datos incluye el concepto de sexo como una variable característica importante para la investigación sobre la diabetes. Piense un poco en cómo categorizaciones como esta podrían excluir a ciertas partes de una población de los tratamientos.
+
+Ahora cargue los datos X e y.
+
+> 🎓 Recuerde, esto es aprendizeje supervisado, y necesitamos un objetivo llamado 'y'.
+
+En una nueva celda de código, cargue el conjunto de datos de diabetes llamando `load_diabetes()`. La entrada `return_X_y=True` indica que `X` será una matriz de datos, y `y` será el objetivo de regresión. 
+
+1. Agregue algunos comandos de impresión para mostrar la forma de la matriz de datos y su primer elemento:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    Lo que recibe como respuesta es una tupla. Lo que está haciendo es asignar los dos primeros valores de la tupla a `X` y `y` respectivamente. Más información [sobre tuplas](https://wikipedia.org/wiki/Tuple).
+
+    Puede ver que estos datos tienen 442 elementos en forma de matrices de 10 elementos:
+    
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ Piense un poco en la relación entre los datos y el objetivo de la regresión. La regersión lineal predice relaciones entre la característica X y la variable objetivo y. ¿Puede encontrar el [objetivo](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) para el cojunto de datos de diabetes en la documentación? ¿Qué está demostrando este conjunto de datos dado ese objetivo?
+
+2. A continuación, seleccione una parte de este conjunto de datos para graficarlos colocándolos en una nueva matriz utilizando la función `newaxis` de _numpy_. Vamos utilizar una regresión lineal para generar una línea entre los valores de estos datos, según un patrón que determine.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ En cualquier momento, imprima los datos para comprobar su forma.
+
+3. Ahora que tiene los datos listos para graficarlos, puede ver si una máquina puede ayudar a determinar una división lógica entre los númnero en este conjunto de datos. Para hacer esto, necesita dividir los datos (X) y el objetivo (y) en conjunto de datos de prueba y entrenamiento. Scikit-learn tiene una forma sencilla de hacer esto; puede dividir sus datos de prueba en un punto determinado.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. Ahora está listo para entrenar su modelo! Cargue el modelo de regresión lineal y entrénelo con sus datos de entrenamiento X e y usando `model.fit()`:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` es una función que verá en muchas bibliotecas de ML como TensorFlow
+
+5. Luego, cree una predicción usando datos de prueba, usando la función `predict()`. Esto se utilizará para trazar la línea entre los grupos de datos.
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. Ahora es el momento de mostrar los datos en una gráfica. Matplotlib es una herramienta muy útil para esta tarea. Cree una gráfica de dispersión de todos los datos de prueba X e y, y use la prediccíón para dibujar una línea en el lugar más apropiado, entre las agrupaciones de datos del modelo.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![un diagrama de dispersión que muestra puntos de datos sobre la diabetes](./images/scatterplot.png)
+
+   ✅ Piense un poco sobre lo que está pasando aquí. Una línea recta atraviesa muchos pequeños puntos de datos, pero ¿qué está haciendo excactamente? ¿Puede ver cómo debería poder usar esta línea para predecir dónde debe encajar un punto de datos nuevo y no visto en relación con el eje y del gráfico? Intente poner en palabras el uso práctico de este modelo.
+
+Felicitaciones, construiste tu primer modelo de regresión lineal, creaste una predicción con él y lo mostraste en una gráfica!
+
+---
+## Desafío
+
+Grafique una variable diferente de este conjunto de datos. Sugerencia: edite esta linea: `X = X[:, np.newaxis, 2]`. Dado el objetivo de este conjunto de datos,¿qué puede descubrir sobre la progresión de la diabetes?
+## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## Revisión y autoestudio
+
+En este tutorial, trabajó con regresión lineal simple, en lugar de regresión lineal univariante o múltiple. Lea un poco sobre las diferencias entre estos métodos o eche un vistazo a [este video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+
+Lea más sobre el concepto de regresión lineal y piense que tipo de preguntas se pueden responder con esta técnica.Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) para profundizar su comprensión.
+
+## Asignación 
+
+[Un conjunto de datos diferentes](assignment.md)
diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index b7ab4bf8c..87b2be53b 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -4,7 +4,7 @@
 
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Kuis Pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Kuis Pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 ## Pembukaan
 
 Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai!
@@ -195,7 +195,7 @@ Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah pre
 ## Tantangan
 
 Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini: `X = X[:, np.newaxis, 2]`. Mengetahui target *dataset* ini, apa yang kamu bisa menemukan tentang kemajuan diabetes sebagai sebuah penyakit?
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## Review & Pembelajaran Mandiri
 
diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md
new file mode 100644
index 000000000..c7516e5be
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.it.md
@@ -0,0 +1,211 @@
+# Iniziare con Python e Scikit-learn per i modelli di regressione
+
+![Sommario delle regressioni in uno sketchnote](../../../sketchnotes/ml-regression.png)
+
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Qui Pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/?loc=it)
+
+## Introduzione
+
+In queste quattro lezioni, si scoprirà come costruire modelli di regressione. Si discuterà di cosa siano fra breve.
+Prima di tutto, ci si deve assicurare di avere a disposizione gli strumenti adatti per far partire il processo!
+
+In questa lezione, si imparerà come:
+
+- Configurare il proprio computer per attività locali di machine learning.
+- Lavorare con i Jupyter notebook.
+- Usare Scikit-learn, compresa l'installazione.
+- Esplorare la regressione lineare con un esercizio pratico.
+
+## Installazioni e configurazioni
+
+[![Usare Python con Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
+
+> 🎥 Fare click sull'immagine qui sopra per un video: usare Python all'interno di VS Code.
+
+1. **Installare Python**. Assicurarsi che [Python](https://www.python.org/downloads/) sia installato nel proprio computer. Si userà Python for per molte attività di data science e machine learning. La maggior parte dei sistemi già include una installazione di Python. Ci sono anche utili [Pacchetti di Codice Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponbili, per facilitare l'installazione per alcuni utenti.
+
+   Alcuni utilizzi di Python, tuttavia, richiedono una versione del software, laddove altri ne richiedono un'altra differente. Per questa ragione, è utile lavorare con un [ambiente virtuale](https://docs.python.org/3/library/venv.html).
+
+2. **Installare Visual Studio Code**. Assicurarsi di avere installato Visual Studio Code sul proprio computer. Si seguano queste istruzioni per [installare Visual Studio Code](https://code.visualstudio.com/) per l'installazione basica. Si userà Python in Visual Studio Code in questo corso, quindi meglio rinfrescarsi le idee su come [configurare Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) per lo sviluppo in Python.
+
+   > Si prenda confidenza con Python tramite questa collezione di [moduli di apprendimento](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+
+3. **Installare Scikit-learn**, seguendo [queste istruzioni](https://scikit-learn.org/stable/install.html). Visto che ci si deve assicurare di usare Python 3, ci si raccomanda di usare un ambiente virtuale. Si noti che se si installa questa libreria in un M1 Mac, ci sono istruzioni speciali nella pagina di cui al riferimento qui sopra.
+
+1. **Installare Jupyter Notebook**. Servirà [installare il pacchetto Jupyter](https://pypi.org/project/jupyter/). 
+
+## Ambiente di creazione ML
+
+Si useranno **notebook** per sviluppare il codice Python e creare modelli di machine learning. Questo tipo di file è uno strumento comune per i data scientist, e viene identificato dal suffisso o estensione `.ipynb`.
+
+I notebook sono un ambiente interattivo che consente allo sviluppatore di scrivere codice, aggiungere note e scrivere documentazione attorno al codice il che è particolarmente utile per progetti sperimentali o orientati alla ricerca.
+
+### Esercizio - lavorare con un notebook
+
+In questa cartella, si troverà il file _notebook.ipynb_. 
+
+1. Aprire _notebook.ipynb_ in Visual Studio Code.
+
+   Un server Jupyter verrà lanciato con Python 3+. Si troveranno aree del notebook che possono essere `eseguite`, pezzi di codice. Si può eseguire un blocco di codice selezionando l'icona che assomiglia a un bottone di riproduzione.
+
+1. Selezionare l'icona `md` e aggiungere un  poco di markdown, e il seguente testo **# Benvenuto nel tuo notebook**.
+
+   Poi, aggiungere un blocco di codice Python. 
+
+1. Digitare **print('hello notebook')** nell'area riservata al codice.
+1. Selezionare la freccia per eseguire il codice.
+
+   Si dovrebbe vedere stampata la seguente frase:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code con un notebook aperto](../images/notebook.png)
+
+Si può inframezzare il codice con commenti per auto documentare il notebook.
+
+✅ Si pensi per un minuto all'ambiente di lavoro di uno sviluppatore web rispetto a quello di un data scientist.
+
+## Scikit-learn installato e funzionante
+
+Adesso che Python è impostato nel proprio ambiente locale, e si è familiari con i notebook Jupyter, si acquisterà ora confidenza con Scikit-learn (si pronuncia con la `si` della parola inglese `science`). Scikit-learn fornisce una [API estensiva](https://scikit-learn.org/stable/modules/classes.html#api-ref) che aiuta a eseguire attività ML.
+
+Stando al loro [sito web](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn è una libreria di machine learning open source che supporta l'apprendimento assistito (supervised learning) e non assistito (unsuperivised learnin). Fornisce anche strumenti vari per l'adattamento del modello, la pre-elaborazione dei dati, la selezione e la valutazione dei modelli e molte altre utilità."
+
+In questo corso, si userà Scikit-learn e altri strumenti per costruire modelli di machine learning per eseguire quelle che vengono chiamate attività di 'machine learning tradizionale'. Si sono deliberamente evitate le reti neurali e il deep learning visto che saranno meglio trattati nel prossimo programma di studi 'AI per Principianti'.
+
+Scikit-learn rende semplice costruire modelli e valutarli per l'uso. Si concentra principalmente sull'utilizzo di dati numerici e contiene diversi insiemi di dati già pronti per l'uso come strumenti di apprendimento. Include anche modelli pre-costruiti per gli studenti da provare. Si esplora ora il processo di caricamento dei dati preconfezionati, e, utilizzando un modello di stimatore incorporato, un primo modello ML con Scikit-Learn con alcuni dati di base.
+
+## Esercizio - Il Primo notebook Scikit-learn
+
+> Questo tutorial è stato ispirato dall'[esempio di regressione lineare](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) nel sito web di Scikit-learn.
+
+Nel file _notebook.ipynb_ associato a questa lezione, svuotare tutte le celle usando l'icona cestino ('trash can').
+
+In questa sezione, di lavorerà con un piccolo insieme di dati sul diabete che è incorporato in Scikit-learn per scopi di apprendimento. Si immagini di voler testare un trattamento per i pazienti diabetici. I modelli di machine learning potrebbero essere di aiuto nel determinare quali pazienti risponderebbero meglio al trattamento, in base a combinazioni di variabili. Anche un modello di regressione molto semplice, quando visualizzato, potrebbe mostrare informazioni sulle variabili che aiuteranno a organizzare le sperimentazioni cliniche teoriche.
+
+✅ Esistono molti tipi di metodi di regressione e quale scegliere dipende dalla risposta che si sta cercando. Se si vuole prevedere l'altezza probabile per una persona di una data età, si dovrebbe usare la regressione lineare, visto che si sta cercando un **valore numerico**. Se si è interessati a scoprire se un tipo di cucina dovrebbe essere considerato vegano o no, si sta cercando un'**assegnazione di categoria** quindi si dovrebbe usare la regressione logistica. Si imparerà di più sulla regressione logistica in seguito. Si pensi ad alcune domande che si possono chiedere ai dati e quale di questi metodi sarebbe più appropriato.
+
+Si inizia con questa attività.
+
+### Importare le librerie
+
+Per questo compito verranno importate alcune librerie:
+
+- **matplotlib**. E' un utile [strumento grafico](https://matplotlib.org/) e verrà usato per creare una trama a linee.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) è una libreira utile per gestire i dati numerici in Python.
+- **sklearn**. Questa è la libreria Scikit-learn.
+
+Importare alcune librerie che saranno di aiuto per le proprie attività.
+
+1. Con il seguente codice si aggiungono le importazioni:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   Qui sopra vengono importati `matplottlib`, e `numpy`, da `sklearn` si importa `datasets`, `linear_model` e `model_selection`. `model_selection` viene usato per dividere i dati negli insiemi di addestramento e test.
+
+### L'insieme di dati riguardante il diabete
+
+L'[insieme dei dati sul diabete](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) include 442 campioni di dati sul diabete, con 10 variabili caratteristiche, alcune delle quali includono:
+
+- age (età): età in anni
+- bmi: indice di massa corporea (body mass index)
+- bp: media pressione sanguinea
+- s1 tc: Cellule T (un tipo di leucocito)
+
+✅ Questo insieme di dati include il concetto di "sesso" come caratteristica variabile importante per la ricerca sul diabete. Molti insiemi di dati medici includono questo tipo di classificazione binaria. Si rifletta su come categorizzazioni come questa potrebbe escludere alcune parti di una popolazione dai trattamenti.
+
+Ora si caricano i dati di X e y.
+
+> 🎓 Si ricordi, questo è apprendimento supervisionato (supervised learning), e serve dare un nome all'obiettivo 'y'.
+
+In una nuova cella di codice, caricare l'insieme di dati sul diabete chiamando `load_diabetes()`. Il parametro `return_X_y=True` segnala che `X` sarà una matrice di dati e `y` sarà l'obiettivo della regressione. 
+
+1. Si aggiungono alcuni comandi di stampa per msotrare la forma della matrice di dati e i suoi primi elementi:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    Quella che viene ritornata è una tuple. Quello che si sta facento è assegnare i primi due valori della tupla a  `X` e `y` rispettivamente. Per saperne di più sulle [tuples](https://wikipedia.org/wiki/Tuple).
+
+    Si può vedere che questi dati hanno 442 elementi divisi in array di 10 elementi:
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ Si rifletta sulla relazione tra i dati e l'obiettivo di regressione. La regressione lineare prevede le relazioni tra la caratteristica X e la variabile di destinazione y. Si può trovare l'[obiettivo](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) per l'insieme di dati sul diabete nella documentazione? Cosa dimostra questo insieme di dati, dato quell'obiettivo?
+
+2. Successivamente, selezionare una porzione di questo insieme di dati da tracciare sistemandola in un nuovo array usando la funzione di numpy's `newaxis`. Verrà usata la regressione lineare per generare una linea tra i valori in questi dati secondo il modello che determina.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ A piacere, stampare i dati per verificarne la forma.
+
+3. Ora che si hanno dei dati pronti per essere tracciati, è possibile vedere se una macchina può aiutare a determinare una divisione logica tra i numeri in questo insieme di dati. Per fare ciò, è necessario dividere sia i dati (X) che l'obiettivo (y) in insiemi di test e addestamento. Scikit-learn ha un modo semplice per farlo; si possono dividere i  dati di prova in un determinato punto.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. Ora si è pronti ad addestare il modello! Caricare il modello di regressione lineare e addestrarlo con i propri insiemi di addestramento X e y usando `model.fit()`:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` è una funzione che si vedrà in molte librerie ML tipo TensorFlow
+
+5. Successivamente creare una previsione usando i dati di test, con la funzione `predict()`. Questo servirà per tracciare la linea tra i gruppi di dati
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. Ora è il momento di mostrare i dati in un tracciato. Matplotlib è uno strumento molto utile per questo compito. Si crei un grafico a dispersione (scatterplot) di tutti i dati del test X e y e si utilizzi la previsione per disegnare una linea nel luogo più appropriato, tra i raggruppamenti dei dati del modello.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![un grafico a dispersione che mostra i punti dati sul diabete](../images/scatterplot.png)
+
+   ✅ Si pensi a cosa sta succedendo qui. Una linea retta scorre attraverso molti piccoli punti dati, ma cosa sta facendo esattamente? Si può capire come si dovrebbe utilizzare questa linea per prevedere dove un nuovo punto di dati non noto dovrebbe adattarsi alla relazione con l'asse y del tracciato? Si cerchi di mettere in parole l'uso pratico di questo modello.
+
+Congratulazioni, si è costruito il primo modello di regressione lineare, creato una previsione con esso, e visualizzata in una tracciato!
+
+---
+
+## 🚀Sfida
+
+Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modificare questa riga: `X = X[:, np.newaxis, 2]`. Dato l'obiettivo di questo insieme di dati, cosa si potrebbe riuscire a scoprire circa la progressione del diabete come matattia?
+
+## [Qui post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/?loc=it)
+
+## Riepilogo e Auto Apprendimento
+
+In questo tutorial, si è lavorato con una semplice regressione lineare, piuttosto che una regressione univariata o multipla. Ci so informi circa le differenze tra questi metodi oppure si dia uno sguardo a [questo video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+
+Si legga di più sul concetto di regressione e si pensi a quale tipo di domande potrebbero trovare risposta con questa tecnica. Seguire questo [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) per approfondire la propria conoscenza.
+
+## Compito
+
+[Un insieme di dati diverso](assignment.it.md)
+
diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index 0bebf16d0..25b86f0e7 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -4,7 +4,7 @@
 
 > [Tomomi Imura](https://www.twitter.com/girlie_mac) によって制作されたスケッチノート
 
-## [講義前クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [講義前クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ja)
 
 ## イントロダクション
 
@@ -121,10 +121,10 @@ Scikit-learnは、モデルを構築し、評価を行って実際に利用す
 
 組み込みの [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) には、糖尿病に関する442サンプルのデータが含まれており、10個の変数が含まれています。
 
-age: 年齢
-bmi: ボディマス指数
-bp: 平均血圧
-s1 tc: T細胞（白血球の一種）
+- age: 年齢
+- bmi: ボディマス指数
+- bp: 平均血圧
+- s1 tc: T細胞（白血球の一種）
 
 ✅ このデータセットには、糖尿病に関する研究に重要な変数として「性別」の概念が含まれています。多くの医療データセットには、このようなバイナリ分類が含まれています。このような分類が、人口のある部分を治療から排除する可能性があることについて、少し考えてみましょう。
 
@@ -205,7 +205,7 @@ s1 tc: T細胞（白血球の一種）
 ## 🚀チャレンジ
 
 このデータセットから別の変数を選択してプロットしてください。ヒント： `X = X[:, np.newaxis, 2]` の行を編集する。今回のデータセットのターゲットである、糖尿病という病気の進行について、どのような発見があるのでしょうか？
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ja)
 
 ## レビュー & 自主学習
 
@@ -215,4 +215,4 @@ s1 tc: T細胞（白血球の一種）
 
 ## 課題
 
-[異なるデータセット](assignment.md)
+[異なるデータセット](./assignment.ja.md)
diff --git a/2-Regression/1-Tools/translations/README.ko.md b/2-Regression/1-Tools/translations/README.ko.md
new file mode 100644
index 000000000..c1cbb4b74
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.ko.md
@@ -0,0 +1,213 @@
+# Regression 모델을 위한 Python과 Scikit-learn 시작하기
+
+![Summary of regressions in a sketchnote](../../../sketchnotes/ml-regression.png)
+
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+
+## 소개
+
+4개의 강의에서, regression 모델을 어떻게 만드는 지에 대하여 탐험합니다. 무엇인지 곧 이야기할 것 입니다. 하지만 모든 것을 하기 전에, 프로세스를 시작할 때 올바른 도구가 있는 지 확인합니다!
+
+이 강의에서는, 이와 같은 내용을 배웁니다:
+
+- 로컬 머신러닝 작업을 위해서 컴퓨터를 구성합니다.
+- Jupyter notebooks으로 작업합니다.
+- 설치 과정을 포함해서, Scikit-learn 사용합니다.
+- 직접 연습해보며 linear regression을 알아봅니다.
+
+## 설치하고 구성하기
+
+[![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
+
+> 🎥 영상 보려면 이미지 클릭: using Python within VS Code.
+
+1. **Python 설치하기**. [Python](https://www.python.org/downloads/)이 컴퓨터에 설치되었는 지 확인합니다. 많은 데이터 사이언스와 머신러닝 작업에서 Python을 사용하게 됩니다. 대부분 컴퓨터 시스템은 이미 Python 애플리케이션을 미리 포함하고 있습니다. 사용자가 설치를 쉽게하는, 유용한 [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)이 존재합니다.
+
+   그러나, 일부 Python만 사용하면, 소프트웨어의 하나의 버전만 요구하지만, 다른 건 다른 버전을 요구합니다. 이런 이유로, [virtual environment](https://docs.python.org/3/library/venv.html)에서 작업하는 것이 유용합니다.
+
+2. **Visual Studio Code 설치하기**. 컴퓨터에 Visual Studio Code가 설치되어 있는 지 확인합니다. 기본 설치로 [install Visual Studio Code](https://code.visualstudio.com/)를 따라합니다. Visual Studio Code에서 Python을 사용하므로 Python 개발을 위한 [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa)를 살펴봅니다.
+
+   > 이 [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)의 모음을 통하여 Python에 익숙해집시다.
+
+3. [these instructions](https://scikit-learn.org/stable/install.html)에 따라서,  **Scikit-learn 설치하기**. Python 3을  사용하는 지 확인할 필요가 있습니다. 가상 환경으로 사용하는 것을 추천합니다. 참고로, M1 Mac에서 라이브러리를 설치하려면, 링크된 페이지에서 특별한 설치 방법을 따라합시다.
+
+1. **Jupyter Notebook 설치하기**. [install the Jupyter package](https://pypi.org/project/jupyter/)가 필요합니다. 
+
+
+## ML 작성 환경
+
+**notebooks**으로 Python 코드를 개발하고 머신러닝 모델도 만드려고 합니다. 이 타입 파일은 데이터 사이언티스트의 일반적인 도구이고, 접미사 또는 확장자 `.ipynb`로 찾을 수 있습니다.
+
+노트북은 개발자가 코드를 작성하고 노트를 추가하며 코드 사이에 문서를 작성할 수 있는 대화형 환경으로서 실험적이거나 연구-중심 프로젝트에 매우 도움이 됩니다.
+
+### 연습 - notebook으로 작업하기
+
+이 폴더에서, _notebook.ipynb_ 파일을 찾을 수 있습니다.
+
+1. Visual Studio Code에서 _notebook.ipynb_ 엽니다.
+
+   Jupyter 서버는 Python 3+ 이상에서 시작됩니다. 코드 조각에서, `run` 할 수 있는 노트북 영역을 찾습니다. 재생 버튼처럼 보이는 아이콘을 선택해서, 코드 블록을 실핼할 수 있습니다.
+
+1. `md` 아이콘을 선택하고 markdown을 살짝 추가합니다, 그리고 **# Welcome to your notebook** 텍스트를 넣습니다.
+
+   다음으로, 약간의 Python 코드를 추가합니다. 
+
+1. 코드 블록에서 **print('hello notebook')** 입력합니다.
+1. 코드를 실행하려면 화살표를 선택합니다.
+
+   출력된 구문이 보여야 합니다:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code with a notebook open](../images/notebook.png)
+
+코드에 주석을 넣어서 노트북이 자체적으로 문서화 할 수 있게 할 수 있습니다.
+
+✅ 웹 개발자의 작업 환경이 데이터 사이언티스트와 어떻게 다른 지 잠시 알아보세요.
+
+## Scikit-learn으로 시작하고 실행하기
+
+이제 로컬 환경에 Python이 설정되었고, 그리고 Jupyter notebooks에 익숙해진 상태에서, Scikit-learn (`science`에서는 `sci`로 발음)도 익숙하게 하겠습니다. Scikit-learn은 ML 작업을 돕는 [extensive API](https://scikit-learn.org/stable/modules/classes.html#api-ref)가 제공됩니다.
+
+[website](https://scikit-learn.org/stable/getting_started.html)에 따르면, "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities." 라고 언급되고 있습니다.
+
+이 코스에서, Scikit-learn과 다른 도구를 사용하여 머신러닝 모델을 만들면서 'traditional machine learning' 작업이 진행됩니다. 곧 다가올 'AI for Beginners' 커리큘럼에서 더 잘 커버될 것이기 때문에, 신경망과 딥러닝은 제외했습니다.
+
+Scikit-learn 사용하면 올바르게 모델을 만들고 사용하기 위해 평가할 수 있습니다. 주로 숫자 데이터에 포커스를 맞추고 학습 도구로 사용하기 위한 여러 ready-made 데이터셋이 포함됩니다. 또 학생들이 시도해볼 수 있도록 사전-제작된 모델을 포함합니다. 패키징된 데이터를 불러오고 기초 데이터와 Scikit-learn이 같이 있는 estimator first ML 모델로 프로세스를 찾아봅니다. 
+
+## 연습 - 첫 Scikit-learn notebook
+
+> 이 튜토리얼은 Scikit-learn 웹사이트에 있는 [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)에서 영감 받았습니다.
+
+이 강의에서 관련있는 _notebook.ipynb_ 파일에서, 'trash can' 아이콘을 누르면 모든 셀이 지워집니다.
+
+이 세션에서, 학습 목적의 Scikit-learn에서 만든 작은 당뇨 데이터셋으로 다룹니다. 당뇨 환자를 위한 치료 방법을 테스트하고 싶다고 생각해보세요. 머신러닝 모델은 변수 조합을 기반으로, 어떤 환자가 더 잘 치료될 지 결정할 때 도울 수 있습니다. 매우 기초적인 regression 모델도, 시각화하면, 이론적인 임상 시험을 구성하는 데에 도움이 될 변수 정보를 보여줄 수 있습니다.
+
+✅ Regression 방식에는 많은 타입이 있고, 어떤 것을 선택하는 지에 따라 다릅니다. 만약 주어진 나이의 사람이 클 수 있는 키에 대하여 예측하려고, **numeric value**를 구할 때, linear regression을 사용합니다. 만약 어떤 타입의 요리를 비건으로 분류해야 하는 지 알고 싶다면, logistic regression으로 **category assignment**을 찾습니다. logistic regression은 나중에 자세히 알아봅시다. 데이터에 대하여 물어볼 수 있는 몇 가지 질문과, 이 방식 중 어느 것이 적당한 지 생각해봅니다.
+
+작업을 시작하겠습니다.
+
+### 라이브러리 Import
+
+작업을 하기 위하여 일부 라이브러리를 import 합니다:
+
+- **matplotlib**. 유용한 [graphing tool](https://matplotlib.org/)이며 line plot을 만들 때 사용합니다.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html)는 Python애서 숫자를 핸들링할 때 유용한 라이브러리입니다.
+- **sklearn**. [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) 라이브러리 입니다.
+
+작업을 도움받으려면 라이브러리를 Import 합니다.
+
+1. 다음 코드를 타이핑해서 imports를 추가합니다:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   `matplottlib`, `numpy`를 가져오고 `sklearn` 에서 `datasets`, `linear_model`과 `model_selection`을 가져옵니다. `model_selection`은 데이터를 학습하고 테스트 셋으로 나누기 위하여 사용합니다.
+
+### 당뇨 데이터셋
+
+빌트-인된 [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)은 당뇨에 대한 442개의 데이터 샘플이 있고, 10개의 feature 변수가 있으며, 그 일부는 아래와 같습니다:
+
+- age: age in years
+- bmi: body mass index
+- bp: average blood pressure
+- s1 tc: T-Cells (a type of white blood cells)
+
+✅ 이 데이터셋에는 당뇨를 연구할 때 중요한 feature 변수인 '성' 컨셉이 포함되어 있습니다. 많은 의학 데이터셋에는 binary classification의 타입이 포함됩니다. 이처럼 categorizations이 치료에서 인구의 특정 파트를 제외할 수 있는 방법에 대하여 조금 고민해보세요.
+
+이제, X 와 y 데이터를 불러옵니다.
+
+> 🎓 다시 언급하지만, 지도 학습이며, 이름이 붙은 'y' 타겟이 필요합니다.
+
+새로운 코드 셀에서, `load_diabetes()`를 호출하여 당뇨 데이터셋을 불러옵니다. 입력 `return_X_y=True`는 `X`를 data matrix, `y`를 regression 타겟으로 나타냅니다.
+
+
+1. data matrix와 첫 요소의 모양을 보여주는 출력 명령을 몇 개 추가합니다:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    응답하는 것은, tuple 입니다. 할 일은 tuple의 두 첫번째 값을 `X` 와 `y`에 각자 할당하는 것입니다. [about tuples](https://wikipedia.org/wiki/Tuple)을 봅시다.
+
+    데이터에 10개 요소의 배열로 이루어진 442개의 아이템이 보입니다:
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ 데이터와 regression 타겟의 관계를 잠시 생각해보세요. Linear regression은 feature X와 타겟 변수 y 사이 관계를 예측합니다. 문서에서 당뇨 데이터셋의 [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)을 찾을 수 있나요? 타겟이 고려하면, 데이터셋은 무엇을 보여주나요?
+
+2. 다음은, numpy의 `newaxis` 함수로 새로운 배열을 통해 플롯할 데이터셋의 일부를 선택합니다. 결정한 패턴에 맞춰서, 데이터의 값 사이에 라인을 생성하기 위하여 linear regression을 사용합니다.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ 언제나, 모양 확인 차 데이터를 출력할 수 있습니다.
+
+3. 이제 데이터를 그릴 준비가 되었으므로, 머신이 데이터셋의 숫자 사이에서 논리적으로 판단하며 나누는 것을 도와주고 있는 지 확인할 수 있습니다. 그러면, 데이터 (X) 와 타겟 (y)를 테스트와 훈련 셋으로 나눌 필요가 있습니다. Scikit-learn에서는 간단한 방식이 존재합니다; 주어진 포인트에서 테스트 데이터를 나눌 수 있습니다.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. 이제 모델을 훈련할 준비가 되었습니다! linear regression 모델을 부르고 `model.fit()` 사용하여 X 와 y 훈련 셋으로 훈련합니다:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()`은 TensorFlow 처럼 많은 ML 라이브러리에서 볼 수 있는 함수입니다
+
+5. 그러면, `predict()` 함수를 사용하여, 테스트 데이터로 prediction을 만듭니다. 데이터 그룹 사이에 라인을 그릴 때 사용합니다
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. 이제 plot으로 데이터를 나타낼 시간입니다. Matplotlib은 이 작업에서 매우 유용한 도구입니다. 모든 X 와 y 테스트 데이터의 scatterplot (산점도)를 만들고, prediction을 사용해서 모델의 데이터 그룹 사이, 가장 적절한 장소에 라인을 그립니다.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.xlabel('Scaled BMIs')
+    plt.ylabel('Disease Progression')
+    plt.title('A Graph Plot Showing Diabetes Progression Against BMI')
+    plt.show()
+    ```
+
+   ![a scatterplot showing datapoints around diabetes](.././images/scatterplot.png)
+
+   ✅ 여기에 어떤 일이 생기는 지 생각합니다. 직선은 많은 데이터의 점을 지나지만, 무엇을 하고있나요? 라인으로 보이지 않는 데이터 포인트가 plot y 축으로 연관해서, 새롭게 맞출 지 예측하는 방식을 알 수 있을까요? 모델의 실제 사용 사례를 말 해봅니다.
+
+축하드립니다. 첫 linear regression 모델을 만들고, 이를 통해서 prediction도 만들어서, plot에 보이게 했습니다!
+
+---
+## 🚀 도전
+
+이 데이터셋은 다른 변수를 Plot 합니다. 힌트: 이 라인을 수정합니다: `X = X[:, np.newaxis, 2]`. 이 데이터셋의 타겟이 주어질 때, 질병으로 당뇨가 진행되면 어떤 것을 탐색할 수 있나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+
+## 검토 & 자기주도 학습
+
+이 튜토리얼에서, univariate 또는 multiple linear regression이 아닌 simple linear regression으로 작업했습니다. 방식의 차이를 읽어보거나, [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)를 봅니다.
+
+regression의 개념에 대하여 더 읽고 기술로 답변할 수 있는 질문의 종류에 대하여 생각해봅니다. [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa)로 깊게 이해합니다.
+
+## 과제 
+
+[A different dataset](../assignment.md)
diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index 4dff27952..c578eb430 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -1,10 +1,10 @@
-# 开始使用Python和Scikit学习回归模型
+# 开始使用 Python 和 Scikit 学习回归模型
 
 ![回归](../../../sketchnotes/ml-regression.png)
 
-> 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
+> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 ## 介绍
 
 在这四节课中，你将了解如何构建回归模型。我们将很快讨论这些是什么。但在你做任何事情之前，请确保你有合适的工具来开始这个过程！
@@ -12,45 +12,45 @@
 在本课中，你将学习如何：
 
 - 为本地机器学习任务配置你的计算机。
-- 使用Jupyter notebooks。
-- 使用Scikit-learn，包括安装。
+- 使用 Jupyter notebooks。
+- 使用 Scikit-learn，包括安装。
 - 通过动手练习探索线性回归。 
 
 ## 安装和配置 
 
-[![在 Visual Studio Code中使用 Python](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "在 Visual Studio Code中使用 Python")
+[![在 Visual Studio Code 中使用 Python](https://img.youtube.com/vi/yyQM70vi7V8/0.jpg)](https://youtu.be/yyQM70vi7V8 "Setup Python with Visual Studio Code")
 
-> 🎥 单击上图观看视频：在VS Code中使用Python。 
+> 🎥 单击上图观看视频：在 VS Code 中使用 Python。 
 
-1. **安装 Python**。确保你的计算机上安装了[Python](https://www.python.org/downloads/)。你将在许多数据科学和机器学习任务中使用 Python。大多数计算机系统已经安装了Python。也有一些有用的[Python编码包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-15963-cxa)可用于简化某些用户的设置。 
+1. **安装 Python**。确保你的计算机上安装了 [Python](https://www.python.org/downloads/)。你将在许多数据科学和机器学习任务中使用 Python。大多数计算机系统已经安装了 Python。也有一些有用的 [Python 编码包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-15963-cxa) 可用于简化某些用户的设置。 
 
-   然而，Python的某些用法需要一个版本的软件，而其他用法则需要另一个不同的版本。 因此，在[虚拟环境](https://docs.python.org/3/library/venv.html)中工作很有用。
+   然而，Python 的某些用法需要一个版本的软件，而其他用法则需要另一个不同的版本。 因此，在 [虚拟环境](https://docs.python.org/3/library/venv.html) 中工作很有用。
 
-2. **安装 Visual Studio Code**。确保你的计算机上安装了Visual Studio Code。按照这些说明[安装 Visual Studio Code](https://code.visualstudio.com/)进行基本安装。在本课程中，你将在Visual Studio Code中使用Python，因此你可能想复习如何[配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa)用于Python开发。 
+2. **安装 Visual Studio Code**。确保你的计算机上安装了 Visual Studio Code。按照这些说明 [安装 Visual Studio Code](https://code.visualstudio.com/) 进行基本安装。在本课程中，你将在 Visual Studio Code 中使用 Python，因此你可能想复习如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) 用于 Python 开发。 
 
-   > 通过学习这一系列的 [学习模块](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)熟悉Python
+   > 通过学习这一系列的 [学习模块](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) 熟悉 Python
 
-3. **按照[这些说明]安装Scikit learn**(https://scikit-learn.org/stable/install.html)。由于你需要确保使用Python3，因此建议你使用虚拟环境。注意，如果你是在M1 Mac上安装这个库，在上面链接的页面上有特别的说明。
+3. **按照 [这些说明](https://scikit-learn.org/stable/install.html) 安装 Scikit learn**。由于你需要确保使用 Python3，因此建议你使用虚拟环境。注意，如果你是在 M1 Mac 上安装这个库，在上面链接的页面上有特别的说明。
 
-4. **安装Jupyter Notebook**。你需要[安装Jupyter包](https://pypi.org/project/jupyter/)。
+4. **安装 Jupyter Notebook**。你需要 [安装 Jupyter 包](https://pypi.org/project/jupyter/)。
 
-## 你的ML工作环境
+## 你的 ML 工作环境
 
-你将使用**notebooks**开发Python代码并创建机器学习模型。这种类型的文件是数据科学家的常用工具，可以通过后缀或扩展名`.ipynb`来识别它们。
+你将使用 **notebooks** 开发 Python 代码并创建机器学习模型。这种类型的文件是数据科学家的常用工具，可以通过后缀或扩展名 `.ipynb` 来识别它们。
 
-Notebooks是一个交互式环境，允许开发人员编写代码并添加注释并围绕代码编写文档，这对于实验或面向研究的项目非常有帮助。 
+Notebooks 是一个交互式环境，允许开发人员编写代码并添加注释并围绕代码编写文档，这对于实验或面向研究的项目非常有帮助。 
 
-### 练习 - 使用notebook
+### 练习 - 使用 notebook
 
-1. 在Visual Studio Code中打开_notebook.ipynb_。
+1. 在 Visual Studio Code 中打开 _notebook.ipynb_。
 
-    Jupyter服务器将以python3+启动。你会发现notebook可以“运行”的区域、代码块。你可以通过选择看起来像播放按钮的图标来运行代码块。
+    Jupyter 服务器将以 python3+启动。你会发现 notebook 可以“运行”的区域、代码块。你可以通过选择看起来像播放按钮的图标来运行代码块。
 
-2. 选择`md`图标并添加一点markdown，输入文字**#Welcome to your notebook**。 
+2. 选择 `md` 图标并添加一点 markdown，输入文字 **# Welcome to your notebook**。
 
-   接下来，添加一些Python代码。
+   接下来，添加一些 Python 代码。
 
-1. 在代码块中输入**print("hello notebook")**。
+1. 在代码块中输入 **print("hello notebook")**。
 
 2. 选择箭头运行代码。
 
@@ -60,43 +60,43 @@ Notebooks是一个交互式环境，允许开发人员编写代码并添加注
     hello notebook
     ```
 
-![打开notebook的VS Code](../images/notebook.png)
+![打开 notebook 的 VS Code](../images/notebook.png)
 
-你可以为你的代码添加注释，以便notebook可以自描述。
+你可以为你的代码添加注释，以便 notebook 可以自描述。
 
-✅ 想一想web开发人员的工作环境与数据科学家的工作环境有多大的不同。
+✅ 想一想 web 开发人员的工作环境与数据科学家的工作环境有多大的不同。
 
-## 启动并运行Scikit-learn 
+## 启动并运行 Scikit-learn
 
-现在Python已在你的本地环境中设置好，并且你对Jupyter notebook感到满意，让我们同样熟悉Scikit-learn（在“science”中发音为“sci”）。 Scikit-learn提供了[大量的API](https://scikit-learn.org/stable/modules/classes.html#api-ref)来帮助你执行ML任务。
+现在 Python 已在你的本地环境中设置好，并且你对 Jupyter notebook 感到满意，让我们同样熟悉 Scikit-learn（在“science”中发音为“sci”）。 Scikit-learn 提供了 [大量的 API](https://scikit-learn.org/stable/modules/classes.html#api-ref) 来帮助你执行 ML 任务。
 
-根据他们的[网站](https://scikit-learn.org/stable/getting_started.html)，“Scikit-learn是一个开源机器学习库，支持有监督和无监督学习。它还提供了各种模型拟合工具、数据预处理、模型选择和评估以及许多其他实用程序。”
+根据他们的 [网站](https://scikit-learn.org/stable/getting_started.html)，“Scikit-learn 是一个开源机器学习库，支持有监督和无监督学习。它还提供了各种模型拟合工具、数据预处理、模型选择和评估以及许多其他实用程序。”
 
-在本课程中，你将使用Scikit-learn和其他工具来构建机器学习模型，以执行我们所谓的“传统机器学习”任务。我们特意避免了神经网络和深度学习，因为它们在我们即将推出的“面向初学者的人工智能”课程中得到了更好的介绍。 
+在本课程中，你将使用 Scikit-learn 和其他工具来构建机器学习模型，以执行我们所谓的“传统机器学习”任务。我们特意避免了神经网络和深度学习，因为它们在我们即将推出的“面向初学者的人工智能”课程中得到了更好的介绍。 
 
-Scikit-learn使构建模型和评估它们的使用变得简单。它主要侧重于使用数字数据，并包含几个现成的数据集用作学习工具。它还包括供学生尝试的预建模型。让我们探索加载预先打包的数据和使用内置的estimator first ML模型和Scikit-learn以及一些基本数据的过程。 
+Scikit-learn 使构建模型和评估它们的使用变得简单。它主要侧重于使用数字数据，并包含几个现成的数据集用作学习工具。它还包括供学生尝试的预建模型。让我们探索加载预先打包的数据和使用内置的 estimator first ML 模型和 Scikit-learn 以及一些基本数据的过程。
 
-## 练习 - 你的第一个Scikit-learn notebook
+## 练习 - 你的第一个 Scikit-learn notebook
 
-> 本教程的灵感来自Scikit-learn网站上的[线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)。
+> 本教程的灵感来自 Scikit-learn 网站上的 [线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)。
 
-在与本课程相关的_notebook.ipynb_文件中，通过点击“垃圾桶”图标清除所有单元格。 
+在与本课程相关的 _notebook.ipynb_ 文件中，通过点击“垃圾桶”图标清除所有单元格。 
 
-在本节中，你将使用一个关于糖尿病的小数据集，该数据集内置于Scikit-learn中以用于学习目的。想象一下，你想为糖尿病患者测试一种治疗方法。机器学习模型可能会帮助你根据变量组合确定哪些患者对治疗反应更好。即使是非常基本的回归模型，在可视化时，也可能会显示有助于组织理论临床试验的变量信息。 
+在本节中，你将使用一个关于糖尿病的小数据集，该数据集内置于 Scikit-learn 中以用于学习目的。想象一下，你想为糖尿病患者测试一种治疗方法。机器学习模型可能会帮助你根据变量组合确定哪些患者对治疗反应更好。即使是非常基本的回归模型，在可视化时，也可能会显示有助于组织理论临床试验的变量信息。
 
-✅ 回归方法有很多种，你选择哪一种取决于你正在寻找的答案。如果你想预测给定年龄的人的可能身高，你可以使用线性回归，因为你正在寻找**数值**。如果你有兴趣了解某种菜肴是否应被视为素食主义者，那么你正在寻找**类别分配**，以便使用逻辑回归。稍后你将了解有关逻辑回归的更多信息。想一想你可以对数据提出的一些问题，以及这些方法中的哪一个更合适。 
+✅ 回归方法有很多种，你选择哪一种取决于你正在寻找的答案。如果你想预测给定年龄的人的可能身高，你可以使用线性回归，因为你正在寻找**数值**。如果你有兴趣了解某种菜肴是否应被视为素食主义者，那么你正在寻找**类别分配**，以便使用逻辑回归。稍后你将了解有关逻辑回归的更多信息。想一想你可以对数据提出的一些问题，以及这些方法中的哪一个更合适。
 
-让我们开始这项任务。 
+让我们开始这项任务。
 
 ### 导入库 
 
 对于此任务，我们将导入一些库：
 
-- **matplotlib**。这是一个有用的[绘图工具](https://matplotlib.org/)，我们将使用它来创建线图。
-- **numpy**。 [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html)是一个有用的库，用于在Python中处理数字数据。
-- **sklearn**。这是Scikit-learn库。
+- **matplotlib**。这是一个有用的 [绘图工具](https://matplotlib.org/)，我们将使用它来创建线图。
+- **numpy**。 [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) 是一个有用的库，用于在 Python 中处理数字数据。
+- **sklearn**。这是 Scikit-learn 库。
 
-导入一些库来帮助你完成任务。 
+导入一些库来帮助你完成任务。
 
 1. 通过输入以下代码添加导入：
 
@@ -106,24 +106,24 @@ Scikit-learn使构建模型和评估它们的使用变得简单。它主要侧
    from sklearn import datasets, linear_model, model_selection
    ```
 
-    在上面的代码中，你正在导入`matplottlib`、`numpy`，你正在从`sklearn`导入`datasets`、`linear_model`和`model_selection`。 `model_selection`用于将数据拆分为训练集和测试集。 
+    在上面的代码中，你正在导入 `matplottlib`、`numpy`，你正在从 `sklearn` 导入 `datasets`、`linear_model` 和 `model_selection`。 `model_selection` 用于将数据拆分为训练集和测试集。 
 
-### 糖尿病数据集 
+### 糖尿病数据集
 
-内置的[糖尿病数据集](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)包含442个围绕糖尿病的数据样本，具有10个特征变量，其中包括：
+内置的 [糖尿病数据集](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) 包含 442 个围绕糖尿病的数据样本，具有 10 个特征变量，其中包括：
 
-age：岁数
-bmi：体重指数
-bp：平均血压
-s1 tc：T细胞（一种白细胞） 
+- age：岁数
+- bmi：体重指数
+- bp：平均血压
+- s1 tc：T 细胞（一种白细胞）
 
-✅ 该数据集包括“性别”的概念，作为对糖尿病研究很重要的特征变量。许多医学数据集包括这种类型的二元分类。想一想诸如此类的分类如何将人群的某些部分排除在治疗之外。 
+✅ 该数据集包括“性别”的概念，作为对糖尿病研究很重要的特征变量。许多医学数据集包括这种类型的二元分类。想一想诸如此类的分类如何将人群的某些部分排除在治疗之外。
 
-现在，加载X和y数据。 
+现在，加载 X 和 y 数据。
 
-> 🎓 请记住，这是监督学习，我们需要一个命名为“y”的目标。 
+> 🎓 请记住，这是监督学习，我们需要一个命名为“y”的目标。
 
-在新的代码单元中，通过调用`load_diabetes()`加载糖尿病数据集。输入`return_X_y=True`表示`X`将是一个数据矩阵，而`y`将是回归目标。
+在新的代码单元中，通过调用 `load_diabetes()` 加载糖尿病数据集。输入 `return_X_y=True` 表示 `X` 将是一个数据矩阵，而`y`将是回归目标。
 
 1. 添加一些打印命令来显示数据矩阵的形状及其第一个元素： 
 
@@ -133,9 +133,9 @@ s1 tc：T细胞（一种白细胞）
     print(X[0])
     ```
 
-    作为响应返回的是一个元组。你正在做的是将元组的前两个值分别分配给`X`和`y`。了解更多 [关于元组](https://wikipedia.org/wiki/Tuple)。
+    作为响应返回的是一个元组。你正在做的是将元组的前两个值分别分配给 `X` 和 `y`。了解更多 [关于元组](https://wikipedia.org/wiki/Tuple)。
 
-    你可以看到这个数据有442个项目，组成了10个元素的数组：
+    你可以看到这个数据有 442 个项目，组成了 10 个元素的数组：
 
     ```text
     (442, 10)
@@ -143,38 +143,38 @@ s1 tc：T细胞（一种白细胞）
     -0.04340085 -0.00259226  0.01990842 -0.01764613]
     ```
 
-    ✅ 稍微思考一下数据和回归目标之间的关系。线性回归预测特征X和目标变量y之间的关系。你能在文档中找到糖尿病数据集的[目标](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)吗？鉴于该目标，该数据集展示了什么？
+    ✅ 稍微思考一下数据和回归目标之间的关系。线性回归预测特征 X 和目标变量 y 之间的关系。你能在文档中找到糖尿病数据集的 [目标](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) 吗？鉴于该目标，该数据集展示了什么？
 
-2. 接下来，通过使用numpy的`newaxis`函数将其排列到一个新数组中来选择要绘制的该数据集的一部分。我们将使用线性回归根据它确定的模式在此数据中的值之间生成一条线。
+2. 接下来，通过使用 numpy 的 `newaxis` 函数将数据集的一部分排列到一个新数组中。我们将使用线性回归根据它确定的模式在此数据中的值之间生成一条线。
 
    ```python
    X = X[:, np.newaxis, 2]
    ```
 
-   ✅ 随时打印数据以检查其形状。 
+   ✅ 随时打印数据以检查其形状。
 
-3. 现在你已准备好绘制数据，你可以查看机器是否可以帮助确定此数据集中数字之间的逻辑分割。为此你需要将数据(X)和目标(y)拆分为测试集和训练集。Scikit-learn有一个简单的方法来做到这一点；你可以在给定点拆分测试数据。
+3. 现在你已准备好绘制数据，你可以看到计算机是否可以帮助确定此数据集中数字之间的逻辑分割。为此你需要将数据 (X) 和目标 (y) 拆分为测试集和训练集。Scikit-learn 有一个简单的方法来做到这一点；你可以在给定点拆分测试数据。
 
    ```python
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
    ```
 
-4. 现在你已准备好训练你的模型！加载线性回归模型并使用`model.fit()`使用X和y训练集对其进行训练：
+4. 现在你已准备好训练你的模型！加载线性回归模型并使用 `model.fit()` 使用 X 和 y 训练集对其进行训练：
 
     ```python
     model = linear_model.LinearRegression()
     model.fit(X_train, y_train)
     ```
 
-    ✅ `model.fit()`是一个你会在许多机器学习库（例如 TensorFlow）中看到的函数 
+    ✅ `model.fit()` 是一个你会在许多机器学习库（例如 TensorFlow）中看到的函数
 
-5. 然后，使用函数`predict()`，使用测试数据创建预测。这将用于绘制数据组之间的线
+5. 然后，使用函数 `predict()`，使用测试数据创建预测。这将用于绘制数据组之间的线
 
     ```python
     y_pred = model.predict(X_test)
     ```
 
-6. 现在是时候在图中显示数据了。Matplotlib是完成此任务的非常有用的工具。创建所有X和y测试数据的散点图，并使用预测在模型的数据分组之间最合适的位置画一条线。
+6. 现在是时候在图中显示数据了。Matplotlib 是完成此任务的非常有用的工具。创建所有 X 和 y 测试数据的散点图，并使用预测在模型的数据分组之间最合适的位置画一条线。
 
     ```python
     plt.scatter(X_test, y_test,  color='black')
@@ -184,22 +184,24 @@ s1 tc：T细胞（一种白细胞）
 
    ![显示糖尿病周围数据点的散点图](../images/scatterplot.png)
 
-   ✅ 想一想这里发生了什么。一条直线穿过许多小数据点，但它到底在做什么？你能看到你应该如何使用这条线来预测一个新的、未见过的数据点对应的y轴值吗？尝试用语言描述该模型的实际用途。
+   ✅ 想一想这里发生了什么。一条直线穿过许多小数据点，但它到底在做什么？你能看到你应该如何使用这条线来预测一个新的、未见过的数据点对应的 y 轴值吗？尝试用语言描述该模型的实际用途。
 
-恭喜，你构建了第一个线性回归模型，使用它创建了预测，并将其显示在绘图中！ 
+恭喜，你构建了第一个线性回归模型，使用它创建了预测，并将其显示在绘图中！
 
 ---
+
 ## 🚀挑战
 
 从这个数据集中绘制一个不同的变量。提示：编辑这一行：`X = X[:, np.newaxis, 2]`。鉴于此数据集的目标，你能够发现糖尿病作为一种疾病的进展情况吗？
-## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## 复习与自学 
 
-在本教程中，你使用了简单线性回归，而不是单变量或多元线性回归。阅读一些关于这些方法之间差异的信息，或查看[此视频](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) 
+在本教程中，你使用了简单线性回归，而不是单变量或多元线性回归。阅读一些关于这些方法之间差异的信息，或查看 [此视频](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
 
-阅读有关回归概念的更多信息，并思考这种技术可以回答哪些类型的问题。用这个[教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa)加深你的理解。 
+阅读有关回归概念的更多信息，并思考这种技术可以回答哪些类型的问题。用这个 [教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) 加深你的理解。
 
 ## 任务  
 
-[不同的数据集](../assignment.md)
+[不同的数据集](./assignment.zh-cn.md)
diff --git a/2-Regression/1-Tools/translations/assignment.es.md b/2-Regression/1-Tools/translations/assignment.es.md
new file mode 100644
index 000000000..e51935977
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.es.md
@@ -0,0 +1,14 @@
+# Regresión con Scikit-learn
+
+## Instrucciones
+
+
+Eche un vistazo al [Linnerud _dataset_](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) en Scikit-learn. Este _dataset_ tiene varios [objetivos](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset): 'Consiste en tres ejercicios (datos) y tres variables fisiológicas (objetivos) recopiladas de veinte hombres de mediana edad en un gimnasio'.
+
+En sus palabras, describa como crear un modelo de regresión que trazaría la relación entre la cintura y la cantidad de abdominales que se realizan. Haga lo mismo con otros puntos de datos en el _dataset_.
+
+## Rúbrica
+
+| Criterios                       | Ejemplar                           | Adecuado              | Necesita Mejorar         |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| Envíe un párrafo descriptivo | Se envía un párrafo bien escrito | Se envían algunas frases | No se proporciona ninguna descripción |
diff --git a/2-Regression/1-Tools/translations/assignment.it.md b/2-Regression/1-Tools/translations/assignment.it.md
new file mode 100644
index 000000000..51fa1663c
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.it.md
@@ -0,0 +1,13 @@
+# Regressione con Scikit-learn
+
+## Istruzioni
+
+Dare un'occhiata all'[insieme di dati Linnerud](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) in Scikit-learn. Questo insieme di dati ha [obiettivi](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset) multipli: "Consiste di tre variabili di esercizio (dati) e tre variabili fisiologiche (obiettivo) raccolte da venti uomini di mezza età in un fitness club".
+
+Con parole proprie, descrivere come creare un modello di Regressione che tracci la relazione tra il punto vita e il numero di addominali realizzati. Fare lo stesso per gli altri punti dati in questo insieme di dati.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| Inviare un paragrafo descrittivo | Viene presentato un paragrafo ben scritto | Vengono inviate alcune frasi | Non viene fornita alcuna descrizione |
diff --git a/2-Regression/1-Tools/translations/assignment.ja.md b/2-Regression/1-Tools/translations/assignment.ja.md
new file mode 100644
index 000000000..6f7d9ef07
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.ja.md
@@ -0,0 +1,13 @@
+# Scikit-learnを用いた回帰
+
+## 課題の指示
+
+Scikit-learnで[Linnerud dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) を見てみましょう。このデータセットは複数の[ターゲット](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset) を持っています。フィットネスクラブで20人の中年男性から収集した3つの運動変数(data)と3つの生理変数(target)で構成されています。
+
+あなた自身の言葉で、ウエストラインと腹筋の回数との関係をプロットする回帰モデルの作成方法を説明してください。このデータセットの他のデータポイントについても同様に説明してみてください。
+
+## ルーブリック
+
+| 指標 | 模範的                           | 適切                      | 要改善          |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| 説明文を提出してください。 | よく書けた文章が提出されている。 | いくつかの文章が提出されている。 | 文章が提出されていません。 |
diff --git a/2-Regression/1-Tools/translations/assignment.zh-cn.md b/2-Regression/1-Tools/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..4efe3e110
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.zh-cn.md
@@ -0,0 +1,14 @@
+# 用 Scikit-learn 实现一次回归算法
+
+## 说明
+
+先看看 Scikit-learn 中的 [Linnerud 数据集](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud)
+这个数据集中有多个[目标变量（target）](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset)，其中包含了三种运动（训练数据）和三个生理指标（目标变量）组成，这些数据都是从一个健身俱乐部中的 20 名中年男子收集到的。
+
+之后用自己的方式，创建一个可以描述腰围和完成仰卧起坐个数关系的回归模型。用同样的方式对这个数据集中的其它数据也建立一下模型探究一下其中的关系。
+
+## 评判标准
+
+| 标准                       | 优秀                           | 中规中矩                      | 仍需努力          |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| 需要提交一段能描述数据集中关系的文字 | 很好的描述了数据集中的关系 | 只能描述少部分的关系 | 啥都没有提交 |
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 2c7f23adc..ba1888101 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -1,9 +1,12 @@
 # Build a regression model using Scikit-learn: prepare and visualize data
 
-> ![Data visualization infographic](./images/data-visualization.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+![Data visualization infographic](./images/data-visualization.png)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+
+> ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb)
 
 ## Introduction
 
@@ -14,6 +17,10 @@ In this lesson, you will learn:
 - How to prepare your data for model-building.
 - How to use Matplotlib for data visualization.
 
+[![Preparing and Visualizing data](https://img.youtube.com/vi/11AnOn_OAcE/0.jpg)](https://youtu.be/11AnOn_OAcE "Preparing and Visualizing data video - Click to Watch!")
+> 🎥 Click the image above for a video covering key aspects of this lesson
+
+
 ## Asking the right question of your data
 
 The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
@@ -34,7 +41,7 @@ This data is in the public domain. It can be downloaded in many separate files,
 
 What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.
 
-What question can you ask of this data, using a Regression technique? What about "Predict the price of a pumpkin for sale during a given month". Looking again at the data, there are some changes you need to make to create the data structure necessary for the task. 
+What question can you ask of this data, using a Regression technique? What about "Predict the price of a pumpkin for sale during a given month". Looking again at the data, there are some changes you need to make to create the data structure necessary for the task.
 ## Exercise - analyze the pumpkin data
 
 Let's use [Pandas](https://pandas.pydata.org/), (the name stands for `Python Data Analysis`) a tool very useful for shaping data, to analyze and prepare this pumpkin data.
@@ -66,7 +73,7 @@ Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet
 
     There is missing data, but maybe it won't matter for the task at hand.
 
-1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need: 
+1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need:
 
     ```python
     new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
@@ -83,9 +90,9 @@ Solution: take the average of the `Low Price` and `High Price` columns to popula
 
     ```python
     price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
-    
+
     month = pd.DatetimeIndex(pumpkins['Date']).month
-    
+
     ```
 
    ✅ Feel free to print any data you'd like to check using `print(month)`.
@@ -122,7 +129,7 @@ Did you notice that the bushel amount varies per row? You need to normalize the
 
     ```python
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
-    
+
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
     ```
 
@@ -134,7 +141,7 @@ Now, you can analyze the pricing per unit based on their bushel measurement. If
 
 ## Visualization Strategies
 
-Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover. 
+Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.
 
 Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
 
@@ -189,7 +196,7 @@ To get charts to display useful data, you usually need to group the data somehow
 
 Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/2-Data/images/dplyr_wrangling.png b/2-Regression/2-Data/images/dplyr_wrangling.png
new file mode 100644
index 000000000..06c50bb33
Binary files /dev/null and b/2-Regression/2-Data/images/dplyr_wrangling.png differ
diff --git a/2-Regression/2-Data/images/unruly_data.jpg b/2-Regression/2-Data/images/unruly_data.jpg
new file mode 100644
index 000000000..54943ca9f
Binary files /dev/null and b/2-Regression/2-Data/images/unruly_data.jpg differ
diff --git a/2-Regression/2-Data/solution/Julia/README.md b/2-Regression/2-Data/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/2-Regression/2-Data/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..959018751
--- /dev/null
+++ b/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,664 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "lesson_2-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: prepare and visualize data\n",
+        "\n",
+        "## **Linear Regression for Pumpkins - Lesson 2**\n",
+        "#### Introduction\n",
+        "\n",
+        "Now that you are set up with the tools you need to start tackling machine learning model building with Tidymodels and the Tidyverse, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.\n",
+        "\n",
+        "In this lesson, you will learn:\n",
+        "\n",
+        "-   How to prepare your data for model-building.\n",
+        "\n",
+        "-   How to use `ggplot2` for data visualization.\n",
+        "\n",
+        "The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.\n",
+        "\n",
+        "Let's see this by working through a practical exercise.\n",
+        "\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/unruly_data.jpg\"\n",
+        "   width=\"700\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Artwork by \\@allison_horst](../../images/unruly_data.jpg)<br>Artwork by \\@allison_horst-->"
+      ],
+      "metadata": {
+        "id": "Pg5aexcOPqAZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Importing pumpkins data and summoning the Tidyverse\n",
+        "\n",
+        "We'll require the following packages to slice and dice this lesson:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\"))`\n",
+        "\n",
+        "The script below checks whether you have the packages required to complete this module and installs them for you in case some are missing."
+      ],
+      "metadata": {
+        "id": "dc5WhyVdXAjR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+        "pacman::p_load(tidyverse)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "GqPYUZgfXOBt"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's fire up some packages and load the [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) provided for this lesson!"
+      ],
+      "metadata": {
+        "id": "kvjDTPDSXRr2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the core Tidyverse packages\n",
+        "library(tidyverse)\n",
+        "\n",
+        "# Import the pumpkins data\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+        "\n",
+        "\n",
+        "# Get a glimpse and dimensions of the data\n",
+        "glimpse(pumpkins)\n",
+        "\n",
+        "\n",
+        "# Print the first 50 rows of the data set\n",
+        "pumpkins %>% \n",
+        "  slice_head(n =50)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "VMri-t2zXqgD"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "A quick `glimpse()` immediately shows that there are blanks and a mix of strings (`chr`) and numeric data (`dbl`). The `Date` is of type character and there's also a strange column called `Package` where the data is a mix between `sacks`, `bins` and other values. The data, in fact, is a bit of a mess 😤.\n",
+        "\n",
+        "In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. But worry not, in this lesson, you will learn how to prepare a raw dataset using standard R libraries 🧑‍🔧. You will also learn various techniques to visualize the data.📈📊\n",
+        "<br>\n",
+        "\n",
+        "> A refresher: The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying \"and then\" in your code.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "REWcIv9yX29v"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Check for missing data\n",
+        "\n",
+        "One of the most common issues data scientists need to deal with is incomplete or missing data. R represents missing, or unknown values, with special sentinel value: `NA` (Not Available).\n",
+        "\n",
+        "So how would we know that the data frame contains missing values?\n",
+        "<br>\n",
+        "-   One straight forward way would be to use the base R function `anyNA` which returns the logical objects `TRUE` or `FALSE`"
+      ],
+      "metadata": {
+        "id": "Zxfb3AM5YbUe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "pumpkins %>% \n",
+        "  anyNA()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "G--DQutAYltj"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Great, there seems to be some missing data! That's a good place to start.\n",
+        "\n",
+        "-   Another way would be to use the function `is.na()` that indicates which individual column elements are missing with a logical `TRUE`."
+      ],
+      "metadata": {
+        "id": "mU-7-SB6YokF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "pumpkins %>% \n",
+        "  is.na() %>% \n",
+        "  head(n = 7)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "W-DxDOR4YxSW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Okay, got the job done but with a large data frame such as this, it would be inefficient and practically impossible to review all of the rows and columns individually😴.\n",
+        "\n",
+        "-   A more intuitive way would be to calculate the sum of the missing values for each column:"
+      ],
+      "metadata": {
+        "id": "xUWxipKYY0o7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "pumpkins %>% \n",
+        "  is.na() %>% \n",
+        "  colSums()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ZRBWV6P9ZArL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Much better! There is missing data, but maybe it won't matter for the task at hand. Let's see what further analysis brings forth.\n",
+        "\n",
+        "> Along with the awesome sets of packages and functions, R has a very good documentation. For instance, use `help(colSums)` or `?colSums` to find out more about the function."
+      ],
+      "metadata": {
+        "id": "9gv-crB6ZD1Y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Dplyr: A Grammar of Data Manipulation\n",
+        "\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+        "   width=\"569\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Artwork by \\@allison_horst](../../images/dplyr_wrangling.png)<br/>Artwork by \\@allison_horst-->"
+      ],
+      "metadata": {
+        "id": "o4jLY5-VZO2C"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[`dplyr`](https://dplyr.tidyverse.org/), a package in the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges. In this section, we'll explore some of dplyr's verbs!\n",
+        "<br>\n"
+      ],
+      "metadata": {
+        "id": "i5o33MQBZWWw"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### dplyr::select()\n",
+        "\n",
+        "`select()` is a function in the package `dplyr` which helps you pick columns to keep or exclude.\n",
+        "\n",
+        "To make your data frame easier to work with, drop several of its columns, using `select()`, keeping only the columns you need.\n",
+        "\n",
+        "For instance, in this exercise, our analysis will involve the columns `Package`, `Low Price`, `High Price` and `Date`. Let's select these columns."
+      ],
+      "metadata": {
+        "id": "x3VGMAGBZiUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Select desired columns\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  select(Package, `Low Price`, `High Price`, Date)\n",
+        "\n",
+        "\n",
+        "# Print data set\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "F_FgxQnVZnM0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### dplyr::mutate()\n",
+        "\n",
+        "`mutate()` is a function in the package `dplyr` which helps you create or modify columns, while keeping the existing columns.\n",
+        "\n",
+        "The general structure of mutate is:\n",
+        "\n",
+        "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+        "\n",
+        "Let's take `mutate` out for a spin using the `Date` column by doing the following operations:\n",
+        "\n",
+        "1.  Convert the dates (currently of type character) to a month format (these are US dates, so the format is `MM/DD/YYYY`).\n",
+        "\n",
+        "2.  Extract the month from the dates to a new column.\n",
+        "\n",
+        "In R, the package [lubridate](https://lubridate.tidyverse.org/) makes it easier to work with Date-time data. So, let's use `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` and see how to achieve the above objectives. We can drop the Date column since we won't be needing it again in subsequent operations."
+      ],
+      "metadata": {
+        "id": "2KKo0Ed9Z1VB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load lubridate\n",
+        "library(lubridate)\n",
+        "\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  # Convert the Date column to a date object\n",
+        "  mutate(Date = mdy(Date)) %>% \n",
+        "  # Extract month from Date\n",
+        "  mutate(Month = month(Date)) %>% \n",
+        "  # Drop Date column\n",
+        "  select(-Date)\n",
+        "\n",
+        "# View the first few rows\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 7)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "5joszIVSZ6xe"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Woohoo! 🤩\n",
+        "\n",
+        "Next, let's create a new column `Price`, which represents the average price of a pumpkin. Now, let's take the average of the `Low Price` and `High Price` columns to populate the new Price column.\n",
+        "<br>"
+      ],
+      "metadata": {
+        "id": "nIgLjNMCZ-6Y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create a new column Price\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+        "\n",
+        "# View the first few rows of the data\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Zo0BsqqtaJw2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Yeees!💪\n",
+        "\n",
+        "\"But wait!\", you'll say after skimming through the whole data set with `View(pumpkins)`, \"There's something odd here!\"🤔\n",
+        "\n",
+        "If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in `1 1/9 bushel` measures, and some in `1/2 bushel` measures, some per pumpkin, some per pound, and some in big boxes with varying widths.\n",
+        "\n",
+        "Let's verify this:"
+      ],
+      "metadata": {
+        "id": "p77WZr-9aQAR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Verify the distinct observations in Package column\n",
+        "pumpkins %>% \n",
+        "  distinct(Package)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "XISGfh0IaUy6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Amazing!👏\n",
+        "\n",
+        "Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string *bushel* in the `Package` column and put this in a new data frame `new_pumpkins`.\n",
+        "<br>"
+      ],
+      "metadata": {
+        "id": "7sMjiVujaZxY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### dplyr::filter() and stringr::str_detect()\n",
+        "\n",
+        "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): creates a subset of the data only containing **rows** that satisfy your conditions, in this case, pumpkins with the string *bushel* in the `Package` column.\n",
+        "\n",
+        "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): detects the presence or absence of a pattern in a string.\n",
+        "\n",
+        "The [`stringr`](https://github.com/tidyverse/stringr) package provides simple functions for common string operations."
+      ],
+      "metadata": {
+        "id": "L8Qfcs92ageF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Retain only pumpkins with \"bushel\"\n",
+        "new_pumpkins <- pumpkins %>% \n",
+        "       filter(str_detect(Package, \"bushel\"))\n",
+        "\n",
+        "# Get the dimensions of the new data\n",
+        "dim(new_pumpkins)\n",
+        "\n",
+        "# View a few rows of the new data\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "hy_SGYREampd"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that we have narrowed down to 415 or so rows of data containing pumpkins by the bushel.🤩\n",
+        "<br>"
+      ],
+      "metadata": {
+        "id": "VrDwF031avlR"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### dplyr::case_when()\n",
+        "\n",
+        "**But wait! There's one more thing to do**\n",
+        "\n",
+        "Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel. Time to do some math to standardize it.\n",
+        "\n",
+        "We'll use the function [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) to *mutate* the Price column depending on some conditions. `case_when` allows you to vectorise multiple `if_else()`statements.\n"
+      ],
+      "metadata": {
+        "id": "mLpw2jH4a0tx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Convert the price if the Package contains fractional bushel values\n",
+        "new_pumpkins <- new_pumpkins %>% \n",
+        "  mutate(Price = case_when(\n",
+        "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+        "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+        "    TRUE ~ Price))\n",
+        "\n",
+        "# View the first few rows of the data\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 30)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "P68kLVQmbM6I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, we can analyze the pricing per unit based on their bushel measurement. All this study of bushels of pumpkins, however, goes to show how very `important` it is to `understand the nature of your data`!\n",
+        "\n",
+        "> ✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. \"A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds.\" It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!\n",
+        ">\n",
+        "> ✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.\n",
+        "<br>\n"
+      ],
+      "metadata": {
+        "id": "pS2GNPagbSdb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now lastly, for the sheer sake of adventure 💁‍♀️, let's also move the Month column to the first position i.e `before` column `Package`.\n",
+        "\n",
+        "`dplyr::relocate()` is used to change column positions."
+      ],
+      "metadata": {
+        "id": "qql1SowfbdnP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create a new data frame new_pumpkins\n",
+        "new_pumpkins <- new_pumpkins %>% \n",
+        "  relocate(Month, .before = Package)\n",
+        "\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 7)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "JJ1x6kw8bixF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Good job!👌 You now have a clean, tidy dataset on which you can build your new regression model!\n",
+        "<br>"
+      ],
+      "metadata": {
+        "id": "y8TJ0Za_bn5Y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Data visualization with ggplot2\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/data-visualization.png\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/data-visualization.png){width=\"600\"}-->\n",
+        "\n",
+        "There is a *wise* saying that goes like this:\n",
+        "\n",
+        "> \"The simple graph has brought more information to the data analyst's mind than any other device.\" --- John Tukey\n",
+        "\n",
+        "Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.\n",
+        "\n",
+        "Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.\n",
+        "\n",
+        "R offers a number of several systems for making graphs, but [`ggplot2`](https://ggplot2.tidyverse.org/index.html) is one of the most elegant and most versatile. `ggplot2` allows you to compose graphs by **combining independent components**.\n",
+        "\n",
+        "Let's start with a simple scatter plot for the Price and Month columns.\n",
+        "\n",
+        "So in this case, we'll start with [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), supply a dataset and aesthetic mapping (with [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) then add a layers (like [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) for scatter plots.\n"
+      ],
+      "metadata": {
+        "id": "mYSH6-EtbvNa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Set a theme for the plots\n",
+        "theme_set(theme_light())\n",
+        "\n",
+        "# Create a scatter plot\n",
+        "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+        "p + geom_point()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "g2YjnGeOcLo4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Is this a useful plot 🤷? Does anything about it surprise you?\n",
+        "\n",
+        "It's not particularly useful as all it does is display in your data as a spread of points in a given month.\n",
+        "<br>"
+      ],
+      "metadata": {
+        "id": "Ml7SDCLQcPvE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **How do we make it useful?**\n",
+        "\n",
+        "To get charts to display useful data, you usually need to group the data somehow. For instance in our case, finding the average price of pumpkins for each month would provide more insights to the underlying patterns in our data. This leads us to one more **dplyr** flyby:\n",
+        "\n",
+        "#### `dplyr::group_by() %>% summarize()`\n",
+        "\n",
+        "Grouped aggregation in R can be easily computed using\n",
+        "\n",
+        "`dplyr::group_by() %>% summarize()`\n",
+        "\n",
+        "-   `dplyr::group_by()` changes the unit of analysis from the complete dataset to individual groups such as per month.\n",
+        "\n",
+        "-   `dplyr::summarize()` creates a new data frame with one column for each grouping variable and one column for each of the summary statistics that you have specified.\n",
+        "\n",
+        "For example, we can use the `dplyr::group_by() %>% summarize()` to group the pumpkins into groups based on the **Month** columns and then find the **mean price** for each month."
+      ],
+      "metadata": {
+        "id": "jMakvJZIcVkh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Find the average price of pumpkins per month\r\n",
+        "new_pumpkins %>%\r\n",
+        "  group_by(Month) %>% \r\n",
+        "  summarise(mean_price = mean(Price))"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "6kVSUa2Bcilf"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Succinct!✨\n",
+        "\n",
+        "Categorical features such as months are better represented using a bar plot 📊. The layers responsible for bar charts are `geom_bar()` and `geom_col()`. Consult `?geom_bar` to find out more.\n",
+        "\n",
+        "Let's whip up one!"
+      ],
+      "metadata": {
+        "id": "Kds48GUBcj3W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+        "new_pumpkins %>%\r\n",
+        "  group_by(Month) %>% \r\n",
+        "  summarise(mean_price = mean(Price)) %>% \r\n",
+        "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+        "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+        "  ylab(\"Pumpkin Price\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "VNbU1S3BcrxO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤩🤩This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?\n",
+        "\n",
+        "Congratulations on finishing the second lesson 👏! You prepared your data for model building, then uncovered more insights using visualizations!"
+      ],
+      "metadata": {
+        "id": "zDm0VOzzcuzR"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/2-Data/solution/R/lesson_2.Rmd b/2-Regression/2-Data/solution/R/lesson_2.Rmd
new file mode 100644
index 000000000..4853882f4
--- /dev/null
+++ b/2-Regression/2-Data/solution/R/lesson_2.Rmd
@@ -0,0 +1,345 @@
+---
+title: 'Build a regression model: prepare and visualize data'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## **Linear Regression for Pumpkins - Lesson 2**
+
+#### Introduction
+
+Now that you are set up with the tools you need to start tackling machine learning model building with Tidymodels and the Tidyverse, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.
+
+In this lesson, you will learn:
+
+-   How to prepare your data for model-building.
+
+-   How to use `ggplot2` for data visualization.
+
+The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
+
+Let's see this by working through a practical exercise.
+
+![Artwork by \@allison_horst](../../images/unruly_data.jpg){width="700"}
+
+## 1. Importing pumpkins data and summoning the Tidyverse
+
+We'll require the following packages to slice and dice this lesson:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+You can have them installed as:
+
+`install.packages(c("tidyverse"))`
+
+The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+if (!require("pacman")) install.packages("pacman")
+pacman::p_load(tidyverse)
+```
+
+Now, let's fire up some packages and load the [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) provided for this lesson!
+
+```{r load_tidy_verse_models, message=F, warning=F}
+# Load the core Tidyverse packages
+library(tidyverse)
+
+# Import the pumpkins data
+pumpkins <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv")
+
+
+# Get a glimpse and dimensions of the data
+glimpse(pumpkins)
+
+
+# Print the first 50 rows of the data set
+pumpkins %>% 
+  slice_head(n =50)
+
+```
+
+A quick `glimpse()` immediately shows that there are blanks and a mix of strings (`chr`) and numeric data (`dbl`). The `Date` is of type character and there's also a strange column called `Package` where the data is a mix between `sacks`, `bins` and other values. The data, in fact, is a bit of a mess 😤.
+
+In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. But worry not, in this lesson, you will learn how to prepare a raw dataset using standard R libraries 🧑‍🔧. You will also learn various techniques to visualize the data.📈📊
+
+
+
+> A refresher: The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying "and then" in your code.
+
+
+## 2. Check for missing data
+
+One of the most common issues data scientists need to deal with is incomplete or missing data. R represents missing, or unknown values, with special sentinel value: `NA` (Not Available).
+
+So how would we know that the data frame contains missing values?
+
+-   One straight forward way would be to use the base R function `anyNA` which returns the logical objects `TRUE` or `FALSE`
+
+```{r anyNA, message=F, warning=F}
+pumpkins %>% 
+  anyNA()
+```
+
+Great, there seems to be some missing data! That's a good place to start.
+
+-   Another way would be to use the function `is.na()` that indicates which individual column elements are missing with a logical `TRUE`.
+
+```{r is_na, message=F, warning=F}
+pumpkins %>% 
+  is.na() %>% 
+  head(n = 7)
+```
+
+Okay, got the job done but with a large data frame such as this, it would be inefficient and practically impossible to review all of the rows and columns individually😴.
+
+-   A more intuitive way would be to calculate the sum of the missing values for each column:
+
+```{r colSum_NA, message=F, warning=F}
+pumpkins %>% 
+  is.na() %>% 
+  colSums()
+```
+
+Much better! There is missing data, but maybe it won't matter for the task at hand. Let's see what further analysis brings forth.
+
+> Along with the awesome sets of packages and functions, R has a very good documentation. For instance, use `help(colSums)` or `?colSums` to find out more about the function.
+
+## 3. Dplyr: A Grammar of Data Manipulation
+
+![Artwork by \@allison_horst](../../images/dplyr_wrangling.png){width="569"}
+
+[`dplyr`](https://dplyr.tidyverse.org/), a package in the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges. In this section, we'll explore some of dplyr's verbs!
+
+#### dplyr::select()
+
+`select()` is a function in the package `dplyr` which helps you pick columns to keep or exclude.
+
+To make your data frame easier to work with, drop several of its columns, using `select()`, keeping only the columns you need.
+
+For instance, in this exercise, our analysis will involve the columns `Package`, `Low Price`, `High Price` and `Date`. Let's select these columns.
+
+```{r select, message=F, warning=F}
+# Select desired columns
+pumpkins <- pumpkins %>% 
+  select(Package, `Low Price`, `High Price`, Date)
+
+
+# Print data set
+pumpkins %>% 
+  slice_head(n = 5)
+```
+
+#### dplyr::mutate()
+
+`mutate()` is a function in the package `dplyr` which helps you create or modify columns, while keeping the existing columns.
+
+The general structure of mutate is:
+
+`data %>%   mutate(new_column_name = what_it_contains)`
+
+Let's take `mutate` out for a spin using the `Date` column by doing the following operations:
+
+1.  Convert the dates (currently of type character) to a month format (these are US dates, so the format is `MM/DD/YYYY`).
+
+2.  Extract the month from the dates to a new column.
+
+In R, the package [lubridate](https://lubridate.tidyverse.org/) makes it easier to work with Date-time data. So, let's use `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` and see how to achieve the above objectives. We can drop the Date column since we won't be needing it again in subsequent operations.
+
+```{r mut_date, message=F, warning=F}
+# Load lubridate
+library(lubridate)
+
+pumpkins <- pumpkins %>% 
+  # Convert the Date column to a date object
+  mutate(Date = mdy(Date)) %>% 
+  # Extract month from Date
+  mutate(Month = month(Date)) %>% 
+  # Drop Date column
+  select(-Date)
+
+# View the first few rows
+pumpkins %>% 
+  slice_head(n = 7)
+```
+
+Woohoo! 🤩
+
+Next, let's create a new column `Price`, which represents the average price of a pumpkin. Now, let's take the average of the `Low Price` and `High Price` columns to populate the new Price column.
+
+```{r price, message=F, warning=F}
+# Create a new column Price
+pumpkins <- pumpkins %>% 
+  mutate(Price = (`Low Price` + `High Price`)/2)
+
+# View the first few rows of the data
+pumpkins %>% 
+  slice_head(n = 5)
+```
+
+Yeees!💪
+
+"But wait!", you'll say after skimming through the whole data set with `View(pumpkins)`, "There's something odd here!"🤔
+
+If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in `1 1/9 bushel` measures, and some in `1/2 bushel` measures, some per pumpkin, some per pound, and some in big boxes with varying widths.
+
+Let's verify this:
+
+```{r Package, message=F, warning=F}
+# Verify the distinct observations in Package column
+pumpkins %>% 
+  distinct(Package)
+
+```
+
+Amazing!👏
+
+Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string *bushel* in the `Package` column and put this in a new data frame `new_pumpkins`.
+
+#### dplyr::filter() and stringr::str_detect()
+
+[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): creates a subset of the data only containing **rows** that satisfy your conditions, in this case, pumpkins with the string *bushel* in the `Package` column.
+
+[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): detects the presence or absence of a pattern in a string.
+
+The [`stringr`](https://github.com/tidyverse/stringr) package provides simple functions for common string operations.
+
+```{r filter, message=F, warning=F}
+# Retain only pumpkins with "bushel"
+new_pumpkins <- pumpkins %>% 
+       filter(str_detect(Package, "bushel"))
+
+# Get the dimensions of the new data
+dim(new_pumpkins)
+
+# View a few rows of the new data
+new_pumpkins %>% 
+  slice_head(n = 5)
+```
+
+You can see that we have narrowed down to 415 or so rows of data containing pumpkins by the bushel.🤩
+
+#### dplyr::case_when()
+
+**But wait! There's one more thing to do**
+
+Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel. Time to do some math to standardize it.
+
+We'll use the function [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) to *mutate* the Price column depending on some conditions. `case_when` allows you to vectorise multiple `if_else()`statements.
+
+```{r normalize_price, message=F, warning=F}
+# Convert the price if the Package contains fractional bushel values
+new_pumpkins <- new_pumpkins %>% 
+  mutate(Price = case_when(
+    str_detect(Package, "1 1/9") ~ Price/(1 + 1/9),
+    str_detect(Package, "1/2") ~ Price/(1/2),
+    TRUE ~ Price))
+
+# View the first few rows of the data
+new_pumpkins %>% 
+  slice_head(n = 30)
+```
+
+Now, we can analyze the pricing per unit based on their bushel measurement. All this study of bushels of pumpkins, however, goes to show how very `important` it is to `understand the nature of your data`!
+
+> ✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
+>
+> ✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.
+
+Now lastly, for the sheer sake of adventure 💁‍♀️, let's also move the Month column to the first position i.e `before` column `Package`.
+
+`dplyr::relocate()` is used to change column positions.
+
+```{r new_pumpkins, message=F, warning=F}
+# Create a new data frame new_pumpkins
+new_pumpkins <- new_pumpkins %>% 
+  relocate(Month, .before = Package)
+
+new_pumpkins %>% 
+  slice_head(n = 7)
+  
+```
+
+Good job!👌 You now have a clean, tidy dataset on which you can build your new regression model!
+
+## 4. Data visualization with ggplot2
+
+![Infographic by Dasani Madipalli](../../images/data-visualization.png){width="600"}
+
+There is a *wise* saying that goes like this:
+
+> "The simple graph has brought more information to the data analyst's mind than any other device." --- John Tukey
+
+Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.
+
+Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
+
+R offers a number of several systems for making graphs, but [`ggplot2`](https://ggplot2.tidyverse.org/index.html) is one of the most elegant and most versatile. `ggplot2` allows you to compose graphs by **combining independent components**.
+
+Let's start with a simple scatter plot for the Price and Month columns.
+
+So in this case, we'll start with [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), supply a dataset and aesthetic mapping (with [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) then add a layers (like [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) for scatter plots.
+
+```{r scatter_plt, message=F, warning=F}
+# Set a theme for the plots
+theme_set(theme_light())
+
+# Create a scatter plot
+p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))
+p + geom_point()
+```
+
+Is this a useful plot 🤷? Does anything about it surprise you?
+
+It's not particularly useful as all it does is display in your data as a spread of points in a given month.
+
+### **How do we make it useful?**
+
+To get charts to display useful data, you usually need to group the data somehow. For instance in our case, finding the average price of pumpkins for each month would provide more insights to the underlying patterns in our data. This leads us to one more **dplyr** flyby:
+
+#### `dplyr::group_by() %>% summarize()`
+
+Grouped aggregation in R can be easily computed using
+
+`dplyr::group_by() %>% summarize()`
+
+-   `dplyr::group_by()` changes the unit of analysis from the complete dataset to individual groups such as per month.
+
+-   `dplyr::summarize()` creates a new data frame with one column for each grouping variable and one column for each of the summary statistics that you have specified.
+
+For example, we can use the `dplyr::group_by() %>% summarize()` to group the pumpkins into groups based on the **Month** columns and then find the **mean price** for each month.
+
+```{r grp_sumry, message=F, warning=F}
+# Find the average price of pumpkins per month
+new_pumpkins %>%
+  group_by(Month) %>% 
+  summarise(mean_price = mean(Price))
+```
+
+Succinct!✨
+
+Categorical features such as months are better represented using a bar plot 📊. The layers responsible for bar charts are `geom_bar()` and `geom_col()`. Consult
+
+`?geom_bar` to find out more.
+
+Let's whip up one!
+
+```{r bar_plt, message=F, warning=F}
+# Find the average price of pumpkins per month then plot a bar chart
+new_pumpkins %>%
+  group_by(Month) %>% 
+  summarise(mean_price = mean(Price)) %>% 
+  ggplot(aes(x = Month, y = mean_price)) +
+  geom_col(fill = "midnightblue", alpha = 0.7) +
+  ylab("Pumpkin Price")
+```
+
+🤩🤩This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
+
+Congratulations on finishing the second lesson 👏! You prepared your data for model building, then uncovered more insights using visualizations!
diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
index b9889ae8f..9d8b5f18d 100644
--- a/2-Regression/2-Data/translations/README.id.md
+++ b/2-Regression/2-Data/translations/README.id.md
@@ -3,7 +3,7 @@
 ![Infografik visualisasi data](../images/data-visualization.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
 ## Pembukaan
 
@@ -191,7 +191,7 @@ Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompo
 
 Jelajahi jenis-jenis visualisasi yang beda dan yang disediakan Matplotlib. Jenis mana yang paling cocok untuk kasus regresi?
 
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## Review & Pembelajaran Mandiri
 
diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md
new file mode 100644
index 000000000..b9882184a
--- /dev/null
+++ b/2-Regression/2-Data/translations/README.it.md
@@ -0,0 +1,201 @@
+# Costruire un modello di regressione usando Scikit-learn: preparare e visualizzare i dati
+
+> ![Infografica sulla visualizzazione dei dati](../images/data-visualization.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/?loc=it)
+
+## Introduzione
+
+Ora che si hanno a disposizione gli strumenti necessari per iniziare ad affrontare la creazione di modelli di machine learning con Scikit-learn, si è pronti per iniziare a porre domande sui propri dati. Mentre si lavora con i dati e si applicano soluzioni ML, è molto importante capire come porre la domanda giusta per sbloccare correttamente le potenzialità del proprio insieme di dati.
+
+In questa lezione, si imparerà:
+
+- Come preparare i dati per la creazione del modello.
+- Come utilizzare Matplotlib per la visualizzazione dei dati.
+
+## Fare la domanda giusta ai propri dati
+
+La domanda a cui si deve rispondere determinerà il tipo di algoritmi ML che verranno utilizzati. La qualità della risposta che si riceverà dipenderà fortemente dalla natura dei propri dati.
+
+Si dia un'occhiata ai [dati](../../data/US-pumpkins.csv) forniti per questa lezione. Si può aprire questo file .csv in VS Code. Una rapida scrematura mostra immediatamente che ci sono spazi vuoti e un mix di stringhe e dati numerici. C'è anche una strana colonna chiamata "Package" (pacchetto) in cui i dati sono un mix tra "sacks" (sacchi), "bins" (contenitori) e altri valori. I dati, infatti, sono un po' un pasticcio.
+
+In effetti, non è molto comune ricevere un insieme di dati completamente pronto per creare un modello ML pronto all'uso. In questa lezione si imparerà come preparare un insieme di dati non elaborato utilizzando le librerie standard di Python. Si impareranno anche varie tecniche per visualizzare i dati.
+
+## Caso di studio: 'il mercato della zucca'
+
+In questa cartella si troverà un file .csv nella cartella `data` radice chiamato [US-pumpkins.csv](../../data/US-pumpkins.csv) che include 1757 righe di dati sul mercato delle zucche, ordinate in raggruppamenti per città. Si tratta di dati grezzi estratti dai [Report Standard dei Mercati Terminali delle Colture Speciali](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribuiti dal Dipartimento dell'Agricoltura degli Stati Uniti.
+
+### Preparazione dati
+
+Questi dati sono di pubblico dominio. Possono essere scaricati in molti file separati, per città, dal sito web dell'USDA. Per evitare troppi file separati, sono stati concatenati tutti i dati della città in un unico foglio di calcolo, quindi un po' i dati sono già stati _preparati_ . Successivamente, si darà un'occhiata più da vicino ai dati.
+
+### I dati della zucca - prime conclusioni
+
+Cosa si nota riguardo a questi dati? Si è già visto che c'è un mix di stringhe, numeri, spazi e valori strani a cui occorre dare un senso.
+
+Che domanda si puà fare a questi dati, utilizzando una tecnica di Regressione? Che dire di "Prevedere il prezzo di una zucca in vendita durante un dato mese". Esaminando nuovamente i dati, ci sono alcune modifiche da apportare per creare la struttura dati necessaria per l'attività.
+
+## Esercizio: analizzare i dati della zucca
+
+Si usa [Pandas](https://pandas.pydata.org/), (il nome sta per `Python Data Analysis`) uno strumento molto utile per dare forma ai dati, per analizzare e preparare questi dati sulla zucca.
+
+### Innanzitutto, controllare le date mancanti
+
+Prima si dovranno eseguire i passaggi per verificare le date mancanti:
+
+1. Convertire le date in un formato mensile (queste sono date statunitensi, quindi il formato è `MM/GG/AAAA`).
+2. Estrarre il mese in una nuova colonna.
+
+Aprire il file _notebook.ipynb_ in Visual Studio Code e importare il foglio di calcolo in un nuovo dataframe Pandas.
+
+1. Usare la funzione `head()` per visualizzare le prime cinque righe.
+
+   ```python
+   import pandas as pd
+   pumpkins = pd.read_csv('../data/US-pumpkins.csv')
+   pumpkins.head()
+   ```
+
+   ✅ Quale funzione si userebbe per visualizzare le ultime cinque righe?
+
+1. Controllare se mancano dati nel dataframe corrente:
+
+   ```python
+   pumpkins.isnull().sum()
+   ```
+
+   Ci sono dati mancanti, ma forse non avrà importanza per l'attività da svolgere.
+
+1. Per rendere più facile lavorare con il dataframe, si scartano molte delle sue colonne, usando `drop()`, mantenendo solo le colonne di cui si ha bisogno:
+
+   ```python
+   new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
+   pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+   ```
+
+### Secondo, determinare il prezzo medio della zucca
+
+Si pensi a come determinare il prezzo medio di una zucca in un dato mese. Quali colonne si sceglierebbero per questa attività? Suggerimento: serviranno 3 colonne.
+
+Soluzione: prendere la media delle colonne `Low Price` e `High Price` per popolare la nuova colonna Price e convertire la colonna Date per mostrare solo il mese. Fortunatamente, secondo il controllo di cui sopra, non mancano dati per date o prezzi.
+
+1. Per calcolare la media, aggiungere il seguente codice:
+
+   ```python
+   price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
+
+   month = pd.DatetimeIndex(pumpkins['Date']).month
+
+   ```
+
+   ✅ Si possono di stampare tutti i dati che si desidera controllare utilizzando `print(month)`.
+
+2. Ora copiare i dati convertiti in un nuovo dataframe Pandas:
+
+   ```python
+   new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
+   ```
+
+   La stampa del dataframe mostrerà un insieme di dati pulito e ordinato su cui si può costruire il  nuovo modello di regressione.
+
+### Ma non è finita qui! C'è qualcosa di strano qui.
+
+Osservando la colonna `Package`, le zucche sono vendute in molte configurazioni diverse. Alcune sono venduti in misure '1 1/9 bushel' (bushel = staio) e alcuni in misure '1/2 bushel', alcuni per zucca, alcuni per libbra e alcuni in grandi scatole con larghezze variabili.
+
+> Le zucche sembrano molto difficili da pesare in modo coerente
+
+Scavando nei dati originali, è interessante notare che qualsiasi cosa con `Unit of Sale` (Unità di vendita) uguale a 'EACH' o 'PER BIN' ha anche il tipo di `Package` per 'inch' (pollice), per 'bin' (contenitore) o 'each' (entrambi). Le zucche sembrano essere molto difficili da pesare in modo coerente, quindi si filtrano selezionando solo zucche con la stringa "bushel" nella colonna `Package`.
+
+1. Aggiungere un filtro nella parte superiore del file, sotto l'importazione .csv iniziale:
+
+   ```python
+   pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
+   ```
+
+   Se si stampano i dati ora, si può vedere che si stanno ricevendo solo le circa 415 righe di dati contenenti zucche per bushel.
+
+### Ma non è finita qui! C'è un'altra cosa da fare.
+
+Si è notato che la quantità di bushel varia per riga? Si deve normalizzare il prezzo in modo da mostrare il prezzo per bushel, quindi si facciano un po' di calcoli per standardizzarlo.
+
+1. Aggiungere queste righe dopo il blocco che crea il dataframe new_pumpkins:
+
+   ```python
+   new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
+
+   new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
+   ```
+
+✅ Secondo [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), il peso di un bushel dipende dal tipo di prodotto, poiché è una misura di volume. "Un bushel di pomodori, per esempio, dovrebbe pesare 56 libbre... Foglie e verdure occupano più spazio con meno peso, quindi un bushel di spinaci è solo 20 libbre". È tutto piuttosto complicato! Non occorre preoccuparsi di fare una conversione da bushel a libbra, e invece si valuta a bushel. Tutto questo studio sui bushel di zucche, però, dimostra quanto sia importante capire la natura dei propri dati!
+
+Ora si può analizzare il prezzo per unità in base alla misurazione del bushel. Se si stampano i dati ancora una volta, si può vedere come sono standardizzati.
+
+✅ Si è notato che le zucche vendute a metà bushel sono molto costose? Si riesce a capire perché? Suggerimento: le zucche piccole sono molto più costose di quelle grandi, probabilmente perché ce ne sono molte di più per bushel, dato lo spazio inutilizzato occupato da una grande zucca cava.
+
+## Strategie di Visualizzazione
+
+Parte del ruolo del data scientist è dimostrare la qualità e la natura dei dati con cui sta lavorando. Per fare ciò, si creano spesso visualizzazioni interessanti o tracciati, grafici e diagrammi, che mostrano diversi aspetti dei dati. In questo modo, sono in grado di mostrare visivamente relazioni e lacune altrimenti difficili da scoprire.
+
+Le visualizzazioni possono anche aiutare a determinare la tecnica di machine learning più appropriata per i dati. Un grafico a dispersione che sembra seguire una linea, ad esempio, indica che i dati sono un buon candidato per un esercizio di regressione lineare.
+
+Una libreria di visualizzazione dei dati che funziona bene nei notebook Jupyter è [Matplotlib](https://matplotlib.org/) (che si è visto anche nella lezione precedente).
+
+> Per fare più esperienza con la visualizzazione dei dati si seguano [questi tutorial](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+
+## Esercizio - sperimentare con Matplotlib
+
+Provare a creare alcuni grafici di base per visualizzare il nuovo dataframe appena creato. Cosa mostrerebbe un grafico a linee di base?
+
+1. Importare Matplotlib nella parte superiore del file, sotto l'importazione di Pandas:
+
+   ```python
+   import matplotlib.pyplot as plt
+   ```
+
+1. Rieseguire l'intero notebook per aggiornare.
+1. Nella parte inferiore del notebook, aggiungere una cella per tracciare i dati come una casella:
+
+   ```python
+   price = new_pumpkins.Price
+   month = new_pumpkins.Month
+   plt.scatter(price, month)
+   plt.show()
+   ```
+
+   ![Un grafico a dispersione che mostra la relazione tra prezzo e mese](../images/scatterplot.png)
+
+   È un tracciato utile? C'è qualcosa che sorprende?
+
+   Non è particolarmente utile in quanto tutto ciò che fa è visualizzare nei propri dati come una diffusione di punti in un dato mese.
+
+### Renderlo utile
+
+Per fare in modo che i grafici mostrino dati utili, di solito è necessario raggruppare i dati in qualche modo. Si prova a creare un grafico che mostra la distribuzione dei dati dove  l'asse x mostra i mesi.
+
+1. Aggiungere una cella per creare un grafico a barre raggruppato:
+
+   ```python
+   new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
+   plt.ylabel("Pumpkin Price")
+   ```
+
+   ![Un grafico a barre che mostra la relazione tra prezzo e mese](../images/barchart.png)
+
+   Questa è una visualizzazione dei dati più utile! Sembra indicare che il prezzo più alto per le zucche si verifica a settembre e ottobre. Questo soddisfa le proprie aspettative? Perché o perché no?
+
+---
+
+## 🚀 Sfida
+
+Esplorare i diversi tipi di visualizzazione offerti da Matplotlib. Quali tipi sono più appropriati per i problemi di regressione?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Dare un'occhiata ai molti modi per visualizzare i dati. Fare un elenco delle varie librerie disponibili e annotare quali sono le migliori per determinati tipi di attività, ad esempio visualizzazioni 2D rispetto a visualizzazioni 3D. Cosa si è scoperto?
+
+## Compito
+
+[Esplorazione della visualizzazione](assignment.it.md)
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 1570be3c5..ddd01a775 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -4,7 +4,7 @@
 > 
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
 
-## [講義前のクイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ja)
 
 ## イントロダクション
 
@@ -61,7 +61,7 @@ Visual Studio Codeで _notebook.ipynb_ ファイルを開き、スプレッド
     ✅ 最後の5行を表示するには、どのような関数を使用しますか？
 
 
-1. 現在のデータフレームに欠損データがあるかどうかをチェックします。
+2. 現在のデータフレームに欠損データがあるかどうかをチェックします。
 
     ```python
     pumpkins.isnull().sum()
@@ -70,7 +70,7 @@ Visual Studio Codeで _notebook.ipynb_ ファイルを開き、スプレッド
     欠損データがありましたが、今回のタスクには影響がなさそうです。
 
 
-1. データフレームを扱いやすくするために、`drop()` 関数を使っていくつかの列を削除し、必要な列だけを残すようにします。
+3. データフレームを扱いやすくするために、`drop()` 関数を使っていくつかの列を削除し、必要な列だけを残すようにします。
 
     ```python
     new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
@@ -195,7 +195,7 @@ Jupyter notebookでうまく利用できるテータ可視化ライブラリの
 
 Matplotlibが提供する様々なタイプのビジュアライゼーションを探ってみましょう。回帰の問題にはどのタイプが最も適しているでしょうか？
 
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ja)
 
 ## レビュー & 自主学習
 
@@ -203,4 +203,4 @@ Matplotlibが提供する様々なタイプのビジュアライゼーション
 
 ## 課題
 
-[ビジュアライゼーションの探求](assignment.md)
+[ビジュアライゼーションの探求](./assignment.ja.md)
diff --git a/2-Regression/2-Data/translations/README.ko.md b/2-Regression/2-Data/translations/README.ko.md
new file mode 100644
index 000000000..64ddc721e
--- /dev/null
+++ b/2-Regression/2-Data/translations/README.ko.md
@@ -0,0 +1,202 @@
+# Scikit-learn 사용한 regression 모델 만들기: 데이터 준비와 시각화 
+
+> ![Data visualization infographic](.././images/data-visualization.png)
+
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+
+## 소개
+
+이제 Scikit-learn으로 머신러닝 모델을 만들기 시작할 때 필요한 도구를 세팅했으므로, 데이터에 대한 질문을 할 준비가 되었습니다. 데이터로 작업하고 ML 솔루션을 적용하려면, 데이터셋의 잠재력을 잘 분석하기 위하여 올바른 질문 방식을 이해하는 것이 매우 중요합니다.
+
+이 강의에서, 다음을 배웁니다:
+
+- 모델-제작 위한 데이터 준비하는 방식.
+- 데이터 시각화 위한 Matplotlib 사용하는 방식.
+
+## 데이터에 올바른 질문하기
+
+답변이 필요한 질문에 따라서 활용할 ML 알고리즘의 타입이 결정됩니다. 그리고 받는 답변의 퀄리티는 데이터의 성격에 크게 의존됩니다.
+
+이 강의에서 제공되는 [data](../data/US-pumpkins.csv)를 보세요. VS Code에서 .csv 파일을 열 수 있습니다. 빠르게 흝어보면 공백과 문자열과 숫자 데이터가 섞여진 것을 보여줍니다. 'Package'라고 불리는 이상한 열에 'sacks', 'bins'과 다른 값 사이에 섞인 데이터가 있습니다. 사실은, 조금 엉성합니다.
+
+실제로, ML 모델을 바로 꺼내 만들면서 완벽하게 사용할 준비가 된 데이터셋을 주는 건 매우 평범하지 않습니다. 이 강의에서는, 표준 Python 라이브러리로 원본 데이터셋을 준비하는 과정을 배우게 됩니다. 데이터 시각화하는 다양한 기술을 배웁니다.
+
+## 케이스 스터디: 'the pumpkin market'
+
+이 폴더에서는 호박 시장에 대한 데이터 1757 라인이 도시별로 분류된 [US-pumpkins.csv](../../data/US-pumpkins.csv) 라고 불리는 최상위 `data` 폴더에서 .csv 파일을 찾을 수 있습니다. United States Department of Agriculture가 배포한 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)에서 원본 데이터를 추출했습니다.
+
+### 데이터 준비하기
+
+이 데이터는 공개 도메인에 존재합니다. USDA 웹사이트에서, 도시별로, 많은 여러개 파일을 내려받을 수 있습니다. 너무 많이 분리된 파일들을 피하기 위해서, 모든 도시 데이터를 한 개의 스프레드 시트에 연결했으므로, 미리 데이터를 조금 _준비_ 했습니다. 다음으로, 데이터를 가까이 봅니다.
+
+### 호박 데이터 - 이른 결론
+
+데이터에서 어떤 것을 눈치챘나요? 이해할 문자열, 숫자, 공백과 이상한 값이 섞여있다는 것을 이미 봤습니다.
+
+Regression 기술을 사용해서, 데이터에 물어볼 수 있는 질문인가요? "Predict the price of a pumpkin for sale during a given month"는 어떤가요. 데이터를 다시보면, 작업에 필요한 데이터 구조를 만들기 위하여 조금 바꿀 점이 있습니다.
+
+## 연습 - 호박 데이터 분석하기
+
+호박 데이터를 분석하고 준비하며, 데이터를 구성할 때 매우 유용한 도구인, [Pandas](https://pandas.pydata.org/) (`Python Data Analysis`의 약자)를 사용해봅시다.
+
+### 먼저, 누락된 날짜를 확인합니다.
+
+먼저 누락된 데이터들을 확인하는 단계가 필요합니다:
+
+1. 날짜를 월 포맷으로 변환합니다 (US 날짜라서 `MM/DD/YYYY` 포맷).
+2. month를 새로운 열로 추출합니다.
+
+visual Studio Code에서 _notebook.ipynb_ 파일을 열고 새로운 Pandas 데아터프레임에 spreadsheet를 가져옵니다.
+
+1. 처음 5개 행을 보기 위하여 `head()` 함수를 사용합니다.
+
+    ```python
+    import pandas as pd
+    pumpkins = pd.read_csv('../data/US-pumpkins.csv')
+    pumpkins.head()
+    ```
+
+    ✅ 마지막 5개 행을 보려면 어떤 함수를 사용하나요?
+
+1. 지금 데이터프레임에 누락된 데이터가 있다면 확인합니다:
+
+    ```python
+    pumpkins.isnull().sum()
+    ```
+
+    누락된 데이터이지만, 당장 앞에 있는 작업에는 중요하지 않을 수 있습니다.
+
+1. 데이터프레임 작업을 더 쉽게 하려면, `drop()`으로, 여러 열을 지우고, 필요한 행만 둡니다.
+
+    ```python
+    new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
+    pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    ```
+
+### 두번째로, 호박의 평균 가격을 결정합니다.
+
+주어진 달에 호박의 평균 가격을 결정하는 방식에 대하여 생각합니다. 이 작업을 하기 위하여 어떤 열을 선택할까요? 힌트: 3개의 열이 필요합니다.
+
+솔루션: `Low Price`와 `High Price` 열의 평균으로 새로운 가격 열을 채우고, 이 달만 보여주기 위해 날짜 열을 변환합니다. 다행히, 확인해보니, 누락된 날짜나 가격 데이터가 없습니다.
+
+1. 평균을 계산하려면, 해당 코드를 추가합니다:
+
+    ```python
+    price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
+    
+    month = pd.DatetimeIndex(pumpkins['Date']).month
+    
+    ```
+
+   ✅ `print(month)`로 확인하려는 데이터를 마음껏 출력해보세요.
+
+2. 이제, 새로 만든 Pandas 데이터프레임으로 변환한 데이터를 복사해보세요:
+
+    ```python
+    new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
+    ```
+
+    데이터프레임을 출력해보면 새로운 regression 모델을 만들 수 있는 깨끗하고, 단정한 데이터셋이 보여집니다.
+
+### 하지만 기다려주세요! 여기 무언가 있습니다
+
+`Package` 열을 보면, 호박이 많이 다양한 구성으로 팔린 것을 볼 수 있습니다. 일부는 '1 1/9 bushel' 단위로 팔고, '1/2 bushel' 단위로 팔고, 호박 단위, 파운드 단위, 그리고 다양한 넓이의 큰 박스에도 넣어서 팔고 있습니다. 
+
+> 호박은 일정한 무게로 이루어지기 꽤 어려운 것 같습니다.
+
+원본 데이터를 파다보면, 모든 항목에 인치, 박스 당, 또는 'each'로 이루어져서 `Unit of Sale`이 'EACH' 또는 'PER BIN'이라는 사실은 흥미롭습니다. 호박은 일정하게 무게를 달기가 매우 어려워서, `Package` 열에 'bushel' 문자열이 있는 호박만 선택해서 필터링하겟습니다.
+
+1. 파일 상단, 처음 .csv import 하단에 필터를 추가합니다:
+
+    ```python
+    pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
+    ```
+
+    지금 데이터를 출력해보면, bushel 호박 포함한 대략 415개의 행만 가져올 수 있습니다.
+
+###  하지만 기다려주세요! 하나 더 있습니다
+
+bushel 수량이 행마다 다른 것을 알았나요? bushel 단위로 가격을 보여줄 수 있도록 가격을 노말라이즈해야 되므로, 수학으로 일반화해야 합니다.  
+
+1. new_pumpkins 데이터프레임을 만드는 블록 뒤에 이 라인들을 추가합니다:
+
+    ```python
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
+    
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
+    ```
+
+✅ [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308)에 따르면, bushel의 무게는 볼륨 측정이므로, 농산물 타입에 따릅니다. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." 모든 게 정말 복잡합니다! bushel에서 파운드로 변환하면서 신경쓰지 말고, bushel로 가격을 정합니다. 호박 bushels의 모든 연구는, 데이터의 특성을 이해하는 게 매우 중요하다는 것을 보여줍니다. 
+
+지금, bushel 측정을 기반으로 가격을 분석하는 게 가능해졌습니다. 만약 한 번 데이터를 출력하면, 표준화된 상태로 볼 수 있습니다.
+
+✅ half-bushel로 파는 게 매우 비싸다는 사실을 파악했나요? 왜 그런 지 알 수 있나요? 힌트: 작은 호박은 큰 호박보다 비쌉니다, 큰 hollow 파이 호박 하나가 차지하는 빈 공간을 생각해보면, bushel 당 더 많습니다.
+
+## 시각화 전략
+
+데이터 사이언티스트 룰의 일부는 작업하고 있는 데이터의 품질과 특성을 증명하는 것입니다. 데이터의 다양한 측면을 보여주는, 흥미로운 시각화, 또는 plots, 그래프 그리고 차트를 만드는 경우가 자주 있습니다. 이렇게, 다른 방식으로 밝히기 힘든 관계와 간격을 시각적으로 표현할 수 있습니다.
+
+시각화는 데이터에 가장 적합한 머신러닝 기술의 결정을 돕습니다. 라인을 따라가는 것처럼 보이는 scatterplot을(산점도) 예시로, 데이터가 linear regression 연습에 좋은 후보군이라는 것을 나타냅니다.
+
+Jupyter notebooks에서 잘 작동하는 데이터 시각화 라이브러리는 (이전 강의에서 보았던) [Matplotlib](https://matplotlib.org/)입니다.
+
+> [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)에서 데이터 시각화 연습을 더 해보세요.
+
+## 연습 - Matplotlib으로 실험하기
+
+직전에 만든 새로운 데이터프레임을 출력하려면 기초 plot을 만듭시다. 기초 라인 plot은 어떻게 보여주나요?
+
+1. 파일의 상단에, Pandas import 밑에서 Matplotlib을 Import 합니다:
+
+    ```python
+    import matplotlib.pyplot as plt
+    ```
+
+1. 전체 노트북을 다시 실행해서 새로 고칩니다.
+1. 노트북의 하단에, 데이터를 박스로 plot할 셀을 추가합니다:
+
+    ```python
+    price = new_pumpkins.Price
+    month = new_pumpkins.Month
+    plt.scatter(price, month)
+    plt.show()
+    ```
+
+    ![A scatterplot showing price to month relationship](.././images/scatterplot.png)
+
+    쓸모있는 plot인가요? 어떤 것에 놀랬나요?
+
+    주어진 달에 대하여 점의 발산은 데이터에 보여질 뿐이므로 특별히 유용하지 않습니다.
+
+### 유용하게 만들기
+
+차트에서 유용한 데이터를 보여지게 하려면, 데이터를 어떻게든지 그룹으로 묶어야 합니다. y축이 달을 나타내면서 데이터의 분포를 나타내는 데이터로 plot을 만들어 보겠습니다.
+
+1. 그룹화된 바 차트를 만들기 위한 셀을 추가합니다:
+
+    ```python
+    new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
+    plt.ylabel("Pumpkin Price")
+    ```
+
+    ![A bar chart showing price to month relationship](.././images/barchart.png)
+
+    조금 더 유용한 데이터 시각화힙니다! 호박 가격이 가장 높았을 때는 9월과 10월로 보여집니다. 기대하던 목표에 부합하나요? 왜 그렇게 생각하나요?
+
+---
+
+## 🚀 도전
+
+Matplotlib에서 제공하는 다양한 시각화 타입을 찾아보세요. regression 문제에 가장 적당한 타입은 무엇인가요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+
+## 검토 & 자기주도 학습
+
+데이터 시각화하는 많은 방식을 찾아보세요. 사용할 수 있는 다양한 라이브러리 목록을 만들고 2D visualizations vs. 3D visualizations 예시처럼, 주어진 작업의 타입에 적당한 라이브러리를 확인합니다. 어떤 것을 찾았나요?
+
+## 과제
+
+[Exploring visualization](../assignment.md)
diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index c3fbf3483..c31c87261 100644
--- a/2-Regression/2-Data/translations/README.zh-cn.md
+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -1,55 +1,56 @@
-# 使用Scikit-learn构建回归模型：准备和可视化数据 
+# 使用 Scikit-learn 构建回归模型：准备和可视化数据
 
-> ![数据可视化信息图](../images/data-visualization.png)
-> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
+![数据可视化信息图](../images/data-visualization.png)
+> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
 ## 介绍
 
-既然你已经设置了开始使用Scikit-learn处理机器学习模型构建所需的工具，你就可以开始对数据提出问题了。当你处理数据并应用ML解决方案时，了解如何提出正确的问题以正确释放数据集的潜力非常重要。
+既然你已经设置了开始使用 Scikit-learn 处理机器学习模型构建所需的工具，你就可以开始对数据提出问题了。当你处理数据并应用ML解决方案时，了解如何提出正确的问题以正确释放数据集的潜力非常重要。
 
 在本课中，你将学习：
 
 - 如何为模型构建准备数据。
-- 如何使用Matplotlib进行数据可视化。 
+- 如何使用 Matplotlib 进行数据可视化。
 
-## 对你的数据提出正确的问题 
+## 对你的数据提出正确的问题
 
-你需要回答的问题将决定你将使用哪种类型的ML算法。你得到的答案的质量将在很大程度上取决于你的数据的性质。
+你提出的问题将决定你将使用哪种类型的 ML 算法。你得到的答案的质量将在很大程度上取决于你的数据的性质。
 
-查看为本课程提供的[数据](../data/US-pumpkins.csv)。你可以在VS Code中打开这个.csv文件。快速浏览一下就会发现有空格，还有字符串和数字数据的混合。还有一个奇怪的列叫做“Package”，其中的数据是“sacks”、“bins”和其他值的混合。事实上，数据有点乱。
+查看为本课程提供的[数据](../data/US-pumpkins.csv)。你可以在 VS Code 中打开这个 .csv 文件。快速浏览一下就会发现有空格，还有字符串和数字数据的混合。还有一个奇怪的列叫做“Package”，其中的数据是“sacks”、“bins”和其他值的混合。事实上，数据有点乱。
 
-事实上，获得一个完全准备好用于创建开箱即用的ML模型的数据集并不是很常见。在本课中，你将学习如何使用标准Python库准备原始数据集。你还将学习各种技术来可视化数据。
+事实上，得到一个完全准备好用于创建 ML 模型的开箱即用数据集并不是很常见。在本课中，你将学习如何使用标准 Python 库准备原始数据集。你还将学习各种技术来可视化数据。
 
 ## 案例研究：“南瓜市场”
 
-你将在`data`文件夹中找到一个名为[US-pumpkins.csv](../data/US-pumpkins.csv)的.csv 文件，其中包含有关南瓜市场的1757行数据，已 按城市排序分组。这是从美国农业部分发的[特种作物终端市场标准报告](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)中提取的原始数据。
+你将在 `data` 文件夹中找到一个名为 [US-pumpkins.csv](../data/US-pumpkins.csv) 的 .csv 文件，其中包含有关南瓜市场的 1757 行数据，已按城市排序分组。这是从美国农业部分发的[特种作物终端市场标准报告](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)中提取的原始数据。
 
 ### 准备数据
 
 这些数据属于公共领域。它可以从美国农业部网站下载，每个城市有许多不同的文件。为了避免太多单独的文件，我们将所有城市数据合并到一个电子表格中，因此我们已经准备了一些数据。接下来，让我们仔细看看数据。
 
-### 南瓜数据 - 早期结论 
+### 南瓜数据 - 早期结论
 
 你对这些数据有什么看法？你已经看到了无法理解的字符串、数字、空格和奇怪值的混合体。
 
-你可以使用回归技术对这些数据提出什么问题？“预测给定月份内待售南瓜的价格”怎么样？再次查看数据，你需要进行一些更改才能创建任务所需的数据结构。 
-## 练习 - 分析南瓜数据 
+你可以使用回归技术对这些数据提出什么问题？“预测给定月份内待售南瓜的价格”怎么样？再次查看数据，你需要进行一些更改才能创建任务所需的数据结构。
 
-让我们使用[Pandas](https://pandas.pydata.org/)，（“Python 数据分析”的意思）一个非常有用的工具，用于分析和准备南瓜数据。
+## 练习 - 分析南瓜数据
+
+让我们使用 [Pandas](https://pandas.pydata.org/)，（“Python 数据分析” Python Data Analysis 的意思）一个非常有用的工具，用于分析和准备南瓜数据。
 
 ### 首先，检查遗漏的日期
 
 你首先需要采取以下步骤来检查缺少的日期：
 
-1. 将日期转换为月份格式（这些是美国日期，因此格式为`MM/DD/YYYY`）。
+1. 将日期转换为月份格式（这些是美国日期，因此格式为 `MM/DD/YYYY`）。
 
 2. 将月份提取到新列。
 
-在 Visual Studio Code 中打开notebook.ipynb文件，并将电子表格导入到新的Pandas dataframe中。
+在 Visual Studio Code 中打开 notebook.ipynb 文件，并将电子表格导入到新的 Pandas dataframe 中。
 
-1. 使用 `head()`函数查看前五行。 
+1. 使用 `head()` 函数查看前五行。
 
     ```python
     import pandas as pd
@@ -57,9 +58,9 @@
     pumpkins.head()
     ```
 
-    ✅ 使用什么函数来查看最后五行？ 
+    ✅ 使用什么函数来查看最后五行？
 
-2. 检查当前dataframe中是否缺少数据： 
+2. 检查当前 dataframe 中是否缺少数据：
 
     ```python
     pumpkins.isnull().sum()
@@ -67,20 +68,20 @@
 
     有数据丢失，但可能对手头的任务来说无关紧要。
 
-3. 为了让你的dataframe更容易使用，使用`drop()`删除它的几个列，只保留你需要的列： 
+3. 为了让你的 dataframe 更容易使用，使用 `drop()` 删除它的几个列，只保留你需要的列：
 
     ```python
     new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
     pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
     ```
 
-### 然后，确定南瓜的平均价格 
+### 然后，确定南瓜的平均价格
 
-考虑如何确定给定月份南瓜的平均价格。你会为此任务选择哪些列？提示：你需要3列。
+考虑如何确定给定月份南瓜的平均价格。你会为此任务选择哪些列？提示：你需要 3 列。
 
-解决方案：取`Low Price`和`High Price`列的平均值来填充新的Price列，将Date列转换成只显示月份。幸运的是，根据上面的检查，没有丢失日期或价格的数据。 
+解决方案：取 `Low Price` 和 `High Price` 列的平均值来填充新的 Price 列，将 Date 列转换成只显示月份。幸运的是，根据上面的检查，没有丢失日期或价格的数据。
 
-1. 要计算平均值，请添加以下代码： 
+1. 要计算平均值，请添加以下代码：
 
     ```python
     price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
@@ -89,37 +90,37 @@
     
     ```
 
-   ✅ 请随意使用`print(month)`打印你想检查的任何数据。 
+   ✅ 请随意使用 `print(month)` 打印你想检查的任何数据。
 
-2. 现在，将转换后的数据复制到新的Pandas dataframe中： 
+2. 现在，将转换后的数据复制到新的 Pandas dataframe 中：
 
     ```python
     new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
     ```
 
-    打印出的dataframe将向你展示一个干净整洁的数据集，你可以在此数据集上构建新的回归模型。
+    打印出的 dataframe 将向你展示一个干净整洁的数据集，你可以在此数据集上构建新的回归模型。
 
 ### 但是等等！这里有点奇怪
 
-如果你看看`Package`(包装)一栏，南瓜有很多不同的配置。有的以1 1/9蒲式耳的尺寸出售，有的以1/2蒲式耳的尺寸出售，有的以每只南瓜出售，有的以每磅出售，有的以不同宽度的大盒子出售。
+如果你看看 `Package`(包装)一栏，南瓜有很多不同的配置。有的以 1 1/9 蒲式耳的尺寸出售，有的以 1/2 蒲式耳的尺寸出售，有的以每只南瓜出售，有的以每磅出售，有的以不同宽度的大盒子出售。
 
 > 南瓜似乎很难统一称重方式
 
-深入研究原始数据，有趣的是，任何`Unit of Sale`等于“EACH”或“PER BIN”的东西也具有每英寸、每箱或“每个”的`Package`类型。南瓜似乎很难采用统一称重方式，因此让我们通过仅选择`Package`列中带有字符串“蒲式耳”的南瓜来过滤它们。
+深入研究原始数据，有趣的是，任何 `Unit of Sale` 等于“EACH”或“PER BIN”的东西也具有每英寸、每箱或“每个”的 `Package` 类型。南瓜似乎很难采用统一称重方式，因此让我们通过仅选择 `Package` 列中带有字符串“蒲式耳”的南瓜来过滤它们。
 
-1. 在初始.csv导入下添加过滤器： 
+1. 在初始 .csv 导入下添加过滤器：
 
     ```python
     pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
     ```
 
-    如果你现在打印数据，你可以看到你只获得了 415 行左右包含按蒲式耳计算的南瓜的数据。 
+    如果你现在打印数据，你可以看到你只获得了 415 行左右包含按蒲式耳计算的南瓜的数据。
 
-### 可是等等！ 还有一件事要做 
+### 可是等等！ 还有一件事要做
 
-你是否注意到每行的蒲式耳数量不同？你需要对定价进行标准化，以便显示每蒲式耳的定价，因此请进行一些数学计算以对其进行标准化。 
+你是否注意到每行的蒲式耳数量不同？你需要对定价进行标准化，以便显示每蒲式耳的定价，因此请进行一些数学计算以对其进行标准化。
 
-1. 在创建 new_pumpkins dataframe的代码块之后添加这些行： 
+1. 在创建 new_pumpkins dataframe 的代码块之后添加这些行：
 
     ```python
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
@@ -129,33 +130,33 @@
 
 ✅ 根据 [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308)，蒲式耳的重量取决于产品的类型，因为它是一种体积测量。“例如，一蒲式耳西红柿应该重56 磅……叶子和蔬菜占据更多空间，重量更轻，所以一蒲式耳菠菜只有20磅。” 这一切都相当复杂！让我们不要费心进行蒲式耳到磅的转换，而是按蒲式耳定价。然而，所有这些对蒲式耳南瓜的研究表明，了解数据的性质是多么重要！
 
-现在，你可以根据蒲式耳测量来分析每单位的定价。如果你再打印一次数据，你可以看到它是如何标准化的。 
+现在，你可以根据蒲式耳测量来分析每单位的定价。如果你再打印一次数据，你可以看到它是如何标准化的。
 
-✅ 你有没有注意到半蒲式耳卖的南瓜很贵？你能弄清楚为什么吗？提示：小南瓜比大南瓜贵得多，这可能是因为考虑到一个大的空心馅饼南瓜占用的未使用空间，每蒲式耳的南瓜要多得多。 
+✅ 你有没有注意到半蒲式耳卖的南瓜很贵？你能弄清楚为什么吗？提示：小南瓜比大南瓜贵得多，这可能是因为考虑到一个大的空心馅饼南瓜占用的未使用空间，每蒲式耳的南瓜要多得多。
 
-## 可视化策略 
+## 可视化策略
 
-数据科学家的部分职责是展示他们使用的数据的质量和性质。为此，他们通常会创建有趣的可视化或绘图、图形和图表，以显示数据的不同方面。通过这种方式，他们能够直观地展示难以发现的关系和差距。 
+数据科学家的部分职责是展示他们使用的数据的质量和性质。为此，他们通常会创建有趣的可视化或绘图、图形和图表，以显示数据的不同方面。通过这种方式，他们能够直观地展示难以发现的关系和差距。
 
 可视化还可以帮助确定最适合数据的机器学习技术。例如，似乎沿着一条线的散点图表明该数据是线性回归练习的良好候选者。
 
-一个在Jupyter notebooks中运行良好的数据可视化库是[Matplotlib](https://matplotlib.org/)（你在上一课中也看到过）。 
+一个在 Jupyter notebooks 中运行良好的数据可视化库是 [Matplotlib](https://matplotlib.org/)（你在上一课中也看到过）。
 
-> 在[这些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)中获得更多数据可视化经验。 
+> 在[这些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)中获得更多数据可视化经验。
 
-## 练习 - 使用 Matplotlib 进行实验 
+## 练习 - 使用 Matplotlib 进行实验
 
-尝试创建一些基本图形来显示你刚刚创建的新dataframe。基本线图会显示什么？ 
+尝试创建一些基本图形来显示你刚刚创建的新 dataframe。基本线图会显示什么？
 
-1. 在文件顶部导入Matplotlib： 
+1. 在文件顶部导入 Matplotlib：
 
     ```python
     import matplotlib.pyplot as plt
     ```
 
-2. 重新刷新以运行整个notebook。 
+2. 重新刷新以运行整个 notebook。
 
-3. 在notebook底部，添加一个单元格以绘制数据：
+3. 在 notebook 底部，添加一个单元格以绘制数据：
 
     ```python
     price = new_pumpkins.Price
@@ -170,33 +171,33 @@
 
     它并不是特别有用，因为它所做的只是在你的数据中显示为给定月份的点数分布。
 
-### 让它有用 
+### 让它有用
 
-为了让图表显示有用的数据，你通常需要以某种方式对数据进行分组。让我们尝试创建一个图，其中y轴显示月份，数据显示数据的分布。 
+为了让图表显示有用的数据，你通常需要以某种方式对数据进行分组。让我们尝试创建一个图，其中 y 轴显示月份，数据显示数据的分布。
 
-1. 添加单元格以创建分组条形图：
+1. 添加单元格以创建分组柱状图：
 
     ```python
     new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![显示价格与月份关系的条形图](../images/barchart.png)
+    ![显示价格与月份关系的柱状图](../images/barchart.png)
 
-    这是一个更有用的数据可视化！似乎表明南瓜的最高价格出现在9月和10月。这符合你的期望吗？为什么？为什么不？
+    这是一个更有用的数据可视化！似乎表明南瓜的最高价格出现在 9 月和 10 月。这符合你的期望吗？为什么？为什么不？
 
 ---
 
 ## 🚀挑战
 
-探索Matplotlib提供的不同类型的可视化。哪种类型最适合回归问题？
+探索 Matplotlib 提供的不同类型的可视化。哪种类型最适合回归问题？
 
-## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## 复习与自学
 
-请看一下可视化数据的多种方法。列出各种可用的库，并注意哪些库最适合给定类型的任务，例如2D可视化与3D可视化。你发现了什么？
+请看一下可视化数据的多种方法。列出各种可用的库，并注意哪些库最适合给定类型的任务，例如 2D 可视化与 3D 可视化。你发现了什么？
 
 ## 任务
 
-[探索可视化](../assignment.md)
+[探索可视化](./assignment.zh-cn.md)
diff --git a/2-Regression/2-Data/translations/assignment.es.md b/2-Regression/2-Data/translations/assignment.es.md
new file mode 100644
index 000000000..b19a5d984
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.es.md
@@ -0,0 +1,9 @@
+# Explorando visualizaciones
+
+Hay varias librerías diferentes que están disponibles para la visualización de los datos. Cree algunas visualizaciones utilizando los datos de 'Pumpkin' en esta lección con _matplotlib_ y _seaborn_ en un cuaderno de muestra.¿Con qué bibliotecas es más fácil trabajar?
+
+## Rúbrica
+
+| Criterios | Ejemplar | Adecuado | Necesita mejorar |
+| -------- | --------- | -------- | ----------------- |
+|          | Se envía un cuaderno con dos exploraciones/visualizaciones         |   Se envía un cuadernos con una exploración/visualización       |  No se envía un cuaderno                 |
diff --git a/2-Regression/2-Data/translations/assignment.it.md b/2-Regression/2-Data/translations/assignment.it.md
new file mode 100644
index 000000000..14527fcae
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.it.md
@@ -0,0 +1,9 @@
+# Esplorazione delle visualizzazioni
+
+Sono disponibili diverse librerie per la visualizzazione dei dati. Creare alcune visualizzazioni utilizzando i dati della zucca in questa lezione con matplotlib e seaborn in un notebook di esempio. Con quali librerie è più facile lavorare?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------- | -------- | ----------------- |
+|          | Viene inviato un notebook con due esplorazioni/visualizzazioni | Viene inviato un notebook con una esplorazione/visualizzazione | Non è stato inviato un notebook |
diff --git a/2-Regression/2-Data/translations/assignment.ja.md b/2-Regression/2-Data/translations/assignment.ja.md
new file mode 100644
index 000000000..09f344d61
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.ja.md
@@ -0,0 +1,9 @@
+# ビジュアライゼーションの探求
+
+データのビジュアライゼーションには、いくつかの異なるライブラリがあります。このレッスンのPumpkinデータを使って、matplotlibとseabornを使って、サンプルノートブックでいくつかのビジュアライゼーションを作ってみましょう。どのライブラリが作業しやすいでしょうか？
+
+## ルーブリック
+
+| 指標 | 模範的 | 適切 | 要改善 |
+| -------- | --------- | -------- | ----------------- |
+|          | ノートブックには2つの活用法/可視化方法が示されている。         |   ノートブックには1つの活用法/可視化方法が示されている。       |  ノートブックが提出されていない。                 |
diff --git a/2-Regression/2-Data/translations/assignment.zh-cn.md b/2-Regression/2-Data/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..0829fd3ae
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.zh-cn.md
@@ -0,0 +1,9 @@
+# 探索数据可视化
+
+有好几个库都可以进行数据可视化。用 matplotlib 和 seaborn 对本课中涉及的 Pumpkin 数据集创建一些数据可视化的图标。并思考哪个库更容易使用？
+
+## 评判标准
+
+| 标准 | 优秀 | 中规中矩 | 仍需努力 |
+| -------- | --------- | -------- | ----------------- |
+|          | 提交了含有两种探索可视化方法的 notebook 工程文件         |   提交了只包含有一种探索可视化方法的 notebook 工程文件       |  没提交  notebook 工程文件                 |
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index 2a60658b2..c2fe13893 100644
--- a/2-Regression/3-Linear/README.md
+++ b/2-Regression/3-Linear/README.md
@@ -2,7 +2,9 @@
 
 ![Linear vs polynomial regression infographic](./images/linear-polynomial.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+
+> ### [This lesson is available in R!](./solution/R/lesson_3-R.ipynb)
 ### Introduction 
 
 So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib. 
@@ -320,7 +322,7 @@ It does make sense, given the plot! And, if this is a better model than the prev
 
 Test several different variables in this notebook to see how correlation corresponds to model accuracy.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/3-Linear/images/janitor.jpg b/2-Regression/3-Linear/images/janitor.jpg
new file mode 100644
index 000000000..93e6f011c
Binary files /dev/null and b/2-Regression/3-Linear/images/janitor.jpg differ
diff --git a/2-Regression/3-Linear/images/recipes.png b/2-Regression/3-Linear/images/recipes.png
new file mode 100644
index 000000000..7fd24b06b
Binary files /dev/null and b/2-Regression/3-Linear/images/recipes.png differ
diff --git a/2-Regression/3-Linear/solution/Julia/README.md b/2-Regression/3-Linear/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/2-Regression/3-Linear/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..4580481d3
--- /dev/null
+++ b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1082 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "lesson_3-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: linear and polynomial regression models"
+      ],
+      "metadata": {
+        "id": "EgQw8osnsUV-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Linear and Polynomial Regression for Pumpkin Pricing - Lesson 3\n",
+        "<p >\n",
+        "   <img src=\"../../images/linear-polynomial.png\"\n",
+        "   width=\"800\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/linear-polynomial.png){width=\"800\"}-->\n",
+        "\n",
+        "#### Introduction\n",
+        "\n",
+        "So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using `ggplot2`.💪\n",
+        "\n",
+        "Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: *basic linear regression* and *polynomial regression*, along with some of the math underlying these techniques.\n",
+        "\n",
+        "> Throughout this curriculum, we assume minimal knowledge of math, and seek to make it accessible for students coming from other fields, so watch for notes, 🧮 callouts, diagrams, and other learning tools to aid in comprehension.\n",
+        "\n",
+        "#### Preparation\n",
+        "\n",
+        "As a reminder, you are loading this data so as to ask questions of it.\n",
+        "\n",
+        "-   When is the best time to buy pumpkins?\n",
+        "\n",
+        "-   What price can I expect of a case of miniature pumpkins?\n",
+        "\n",
+        "-   Should I buy them in half-bushel baskets or by the 1 1/9 bushel box? Let's keep digging into this data.\n",
+        "\n",
+        "In the previous lesson, you created a `tibble` (a modern reimagining of the data frame) and populated it with part of the original dataset, standardizing the pricing by the bushel. By doing that, however, you were only able to gather about 400 data points and only for the fall months. Maybe we can get a little more detail about the nature of the data by cleaning it more? We'll see... 🕵️‍♀️\n",
+        "\n",
+        "For this task, we'll require the following packages:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.\n",
+        "\n",
+        "-   `corrplot`: The [corrplot package](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+        "\n",
+        "The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ],
+      "metadata": {
+        "id": "WqQPS1OAsg3H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+        "\n",
+        "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "tA4C2WN3skCf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll later load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)\r\n",
+        "\r\n",
+        "## 1. A linear regression line\r\n",
+        "\r\n",
+        "As you learned in Lesson 1, the goal of a linear regression exercise is to be able to plot a *line* *of* *best fit* to:\r\n",
+        "\r\n",
+        "-   **Show variable relationships**. Show the relationship between variables\r\n",
+        "\r\n",
+        "-   **Make predictions**. Make accurate predictions on where a new data point would fall in relationship to that line.\r\n",
+        "\r\n",
+        "To draw this type of line, we use a statistical technique called **Least-Squares Regression**. The term `least-squares` means that all the data points surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. As such, the line of best fit is the line that gives us the lowest value for the sum of the squared errors - hence the name *least squares regression*.\r\n",
+        "\r\n",
+        "We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.\r\n",
+        "\r\n",
+        "> **🧮 Show me the math**\r\n",
+        ">\r\n",
+        "> This line, called the *line of best fit* can be expressed by [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression):\r\n",
+        ">\r\n",
+        ">     Y = a + bX\r\n",
+        ">\r\n",
+        "> `X` is the '`explanatory variable` or `predictor`'. `Y` is the '`dependent variable` or `outcome`'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`.\r\n",
+        ">\r\n",
+        "\r\n",
+        "> ![](../images/slope.png \"slope = $y/x$\")\r\n",
+        "    Infographic by Jen Looper\r\n",
+        ">\r\n",
+        "> First, calculate the slope `b`.\r\n",
+        ">\r\n",
+        "> In other words, and referring to our pumpkin data's original question: \"predict the price of a pumpkin per bushel by month\", `X` would refer to the price and `Y` would refer to the month of sale.\r\n",
+        ">\r\n",
+        "> ![](../images/calculation.png)\r\n",
+        "    Infographic by Jen Looper\r\n",
+        "> \r\n",
+        "> Calculate the value of Y. If you're paying around \\$4, it must be April!\r\n",
+        ">\r\n",
+        "> The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.\r\n",
+        ">\r\n",
+        "> You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site. Also visit [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) to watch how the numbers' values impact the line.\r\n",
+        "\r\n",
+        "Not so scary, right? 🤓\r\n",
+        "\r\n",
+        "#### Correlation\r\n",
+        "\r\n",
+        "One more term to understand is the **Correlation Coefficient** between given X and Y variables. Using a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.\r\n",
+        "\r\n",
+        "A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.\r\n",
+        "\r\n"
+      ],
+      "metadata": {
+        "id": "cdX5FRpvsoP5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **2. A dance with data: creating a data frame that will be used for modelling**\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/janitor.jpg\"\n",
+        "   width=\"700\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Artwork by \\@allison_horst](../../images/janitor.jpg){width=\"700\"}-->"
+      ],
+      "metadata": {
+        "id": "WdUKXk7Bs8-V"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Load up required libraries and dataset. Convert the data to a data frame containing a subset of the data:\n",
+        "\n",
+        "-   Only get pumpkins priced by the bushel\n",
+        "\n",
+        "-   Convert the date to a month\n",
+        "\n",
+        "-   Calculate the price to be an average of high and low prices\n",
+        "\n",
+        "-   Convert the price to reflect the pricing by bushel quantity\n",
+        "\n",
+        "> We covered these steps in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb)."
+      ],
+      "metadata": {
+        "id": "fMCtu2G2s-p8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the core Tidyverse packages\n",
+        "library(tidyverse)\n",
+        "library(lubridate)\n",
+        "\n",
+        "# Import the pumpkins data\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+        "\n",
+        "\n",
+        "# Get a glimpse and dimensions of the data\n",
+        "glimpse(pumpkins)\n",
+        "\n",
+        "\n",
+        "# Print the first 50 rows of the data set\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ryMVZEEPtERn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In the spirit of sheer adventure, let's explore the [`janitor package`](github.com/sfirke/janitor) that provides simple functions for examining and cleaning dirty data. For instance, let's take a look at the column names for our data:"
+      ],
+      "metadata": {
+        "id": "xcNxM70EtJjb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Return column names\n",
+        "pumpkins %>% \n",
+        "  names()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "5XtpaIigtPfW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤔 We can do better. Let's make these column names `friendR` by converting them to the [snake_case](https://en.wikipedia.org/wiki/Snake_case) convention using `janitor::clean_names`. To find out more about this function: `?clean_names`"
+      ],
+      "metadata": {
+        "id": "IbIqrMINtSHe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Clean names to the snake_case convention\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  clean_names(case = \"snake\")\n",
+        "\n",
+        "# Return column names\n",
+        "pumpkins %>% \n",
+        "  names()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "a2uYvclYtWvX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Much tidyR 🧹! Now, a dance with the data using `dplyr` as in the previous lesson! 💃\n"
+      ],
+      "metadata": {
+        "id": "HfhnuzDDtaDd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Select desired columns\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  select(variety, city_name, package, low_price, high_price, date)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Extract the month from the dates to a new column\n",
+        "pumpkins <- pumpkins %>%\n",
+        "  mutate(date = mdy(date),\n",
+        "         month = month(date)) %>% \n",
+        "  select(-date)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Create a new column for average Price\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  mutate(price = (low_price + high_price)/2)\n",
+        "\n",
+        "\n",
+        "# Retain only pumpkins with the string \"bushel\"\n",
+        "new_pumpkins <- pumpkins %>% \n",
+        "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+        "\n",
+        "\n",
+        "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+        "new_pumpkins <- new_pumpkins %>% \n",
+        "  mutate(price = case_when(\n",
+        "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+        "    str_detect(package, \"1/2\") ~ price*2,\n",
+        "    TRUE ~ price))\n",
+        "\n",
+        "# Relocate column positions\n",
+        "new_pumpkins <- new_pumpkins %>% \n",
+        "  relocate(month, .before = variety)\n",
+        "\n",
+        "\n",
+        "# Display the first 5 rows\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "X0wU3gQvtd9f"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Good job!👌 You now have a clean, tidy data set on which you can build your new regression model!\n",
+        "\n",
+        "Mind a scatter plot?\n"
+      ],
+      "metadata": {
+        "id": "UpaIwaxqth82"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Set theme\n",
+        "theme_set(theme_light())\n",
+        "\n",
+        "# Make a scatter plot of month and price\n",
+        "new_pumpkins %>% \n",
+        "  ggplot(mapping = aes(x = month, y = price)) +\n",
+        "  geom_point(size = 1.6)\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "DXgU-j37tl5K"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "A scatter plot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion.\n",
+        "\n",
+        "Let's take a look at our modelling data again:"
+      ],
+      "metadata": {
+        "id": "Ve64wVbwtobI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Display first 5 rows\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "HFQX2ng1tuSJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "What if we wanted to predict the `price` of a pumpkin based on the `city` or `package` columns which are of type character? Or even more simply, how could we find the correlation (which requires both of its inputs to be numeric) between, say, `package` and `price`? 🤷🤷\n",
+        "\n",
+        "Machine learning models work best with numeric features rather than text values, so you generally need to convert categorical features into numeric representations.\n",
+        "\n",
+        "This means that we have to find a way to reformat our predictors to make them easier for a model to use effectively, a process known as `feature engineering`."
+      ],
+      "metadata": {
+        "id": "7hsHoxsStyjJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Preprocessing data for modelling with recipes 👩‍🍳👨‍🍳\n",
+        "\n",
+        "Activities that reformat predictor values to make them easier for a model to use effectively has been termed `feature engineering`.\n",
+        "\n",
+        "Different models have different preprocessing requirements. For instance, least squares requires `encoding categorical variables` such as month, variety and city_name. This simply involves `translating` a column with `categorical values` into one or more `numeric columns` that take the place of the original.\n",
+        "\n",
+        "For example, suppose your data includes the following categorical feature:\n",
+        "\n",
+        "|  city   |\n",
+        "|:-------:|\n",
+        "| Denver  |\n",
+        "| Nairobi |\n",
+        "|  Tokyo  |\n",
+        "\n",
+        "You can apply *ordinal encoding* to substitute a unique integer value for each category, like this:\n",
+        "\n",
+        "| city |\n",
+        "|:----:|\n",
+        "|  0   |\n",
+        "|  1   |\n",
+        "|  2   |\n",
+        "\n",
+        "And that's what we'll do to our data!\n",
+        "\n",
+        "In this section, we'll explore another amazing Tidymodels package: [recipes](https://tidymodels.github.io/recipes/) - which is designed to help you preprocess your data **before** training your model. At its core, a recipe is an object that defines what steps should be applied to a data set in order to get it ready for modelling.\n",
+        "\n",
+        "Now, let's create a recipe that prepares our data for modelling by substituting a unique integer for all the observations in the predictor columns:"
+      ],
+      "metadata": {
+        "id": "AD5kQbcvt3Xl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Specify a recipe\n",
+        "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+        "  step_integer(all_predictors(), zero_based = TRUE)\n",
+        "\n",
+        "\n",
+        "# Print out the recipe\n",
+        "pumpkins_recipe"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "BNaFKXfRt9TU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Awesome! 👏 We just created our first recipe that specifies an outcome (price) and its corresponding predictors and that all the predictor columns should be encoded into a set of integers 🙌! Let's quickly break it down:\n",
+        "\n",
+        "-   The call to `recipe()` with a formula tells the recipe the *roles* of the variables using `new_pumpkins` data as the reference. For instance the `price` column has been assigned an `outcome` role while the rest of the columns have been assigned a `predictor` role.\n",
+        "\n",
+        "-   `step_integer(all_predictors(), zero_based = TRUE)` specifies that all the predictors should be converted into a set of integers with the numbering starting at 0.\n",
+        "\n",
+        "We are sure you may be having thoughts such as: \"This is so cool!! But what if I needed to confirm that the recipes are doing exactly what I expect them to do? 🤔\"\n",
+        "\n",
+        "That's an awesome thought! You see, once your recipe is defined, you can estimate the parameters required to actually preprocess the data, and then extract the processed data. You don't typically need to do this when you use Tidymodels (we'll see the normal convention in just a minute-\\> `workflows`) but it can come in handy when you want to do some kind of sanity check for confirming that recipes are doing what you expect.\n",
+        "\n",
+        "For that, you'll need two more verbs: `prep()` and `bake()` and as always, our little R friends by [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) help you in understanding this better!\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/recipes.png\"\n",
+        "   width=\"550\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Artwork by \\@allison_horst](../../images/recipes.png){width=\"550\"}-->"
+      ],
+      "metadata": {
+        "id": "KEiO0v7kuC9O"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): estimates the required parameters from a training set that can be later applied to other data sets. For instance, for a given predictor column, what observation will be assigned integer 0 or 1 or 2 etc\n",
+        "\n",
+        "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): takes a prepped recipe and applies the operations to any data set.\n",
+        "\n",
+        "That said, lets prep and bake our recipes to really confirm that under the hood, the predictor columns will be first encoded before a model is fit."
+      ],
+      "metadata": {
+        "id": "Q1xtzebuuTCP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Prep the recipe\n",
+        "pumpkins_prep <- prep(pumpkins_recipe)\n",
+        "\n",
+        "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+        "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+        "\n",
+        "# Print out the baked data set\n",
+        "baked_pumpkins %>% \n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "FGBbJbP_uUUn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Woo-hoo!🥳 The processed data `baked_pumpkins` has all it's predictors encoded confirming that indeed the preprocessing steps defined as our recipe will work as expected. This makes it harder for you to read but much more intelligible for Tidymodels! Take some time to find out what observation has been mapped to a corresponding integer.\n",
+        "\n",
+        "It is also worth mentioning that `baked_pumpkins` is a data frame that we can perform computations on.\n",
+        "\n",
+        "For instance, let's try to find a good correlation between two points of your data to potentially build a good predictive model. We'll use the function `cor()` to do this. Type `?cor()` to find out more about the function."
+      ],
+      "metadata": {
+        "id": "1dvP0LBUueAW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Find the correlation between the city_name and the price\n",
+        "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+        "\n",
+        "# Find the correlation between the package and the price\n",
+        "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "3bQzXCjFuiSV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As it turns out, there's only weak correlation between the City and Price. However there's a bit better correlation between the Package and its Price. That makes sense, right? Normally, the bigger the produce box, the higher the price.\n",
+        "\n",
+        "While we are at it, let's also try and visualize a correlation matrix of all the columns using the `corrplot` package."
+      ],
+      "metadata": {
+        "id": "BToPWbgjuoZw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the corrplot package\n",
+        "library(corrplot)\n",
+        "\n",
+        "# Obtain correlation matrix\n",
+        "corr_mat <- cor(baked_pumpkins %>% \n",
+        "                  # Drop columns that are not really informative\n",
+        "                  select(-c(low_price, high_price)))\n",
+        "\n",
+        "# Make a correlation plot between the variables\n",
+        "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ZwAL3ksmutVR"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤩🤩 Much better.\n",
+        "\n",
+        "A good question to now ask of this data will be: '`What price can I expect of a given pumpkin package?`' Let's get right into it!\n",
+        "\n",
+        "> Note: When you **`bake()`** the prepped recipe **`pumpkins_prep`** with **`new_data = NULL`**, you extract the processed (i.e. encoded) training data. If you had another data set for example a test set and would want to see how a recipe would pre-process it, you would simply bake **`pumpkins_prep`** with **`new_data = test_set`**\n",
+        "\n",
+        "## 4. Build a linear regression model\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/linear-polynomial.png\"\n",
+        "   width=\"800\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/linear-polynomial.png){width=\"800\"}-->"
+      ],
+      "metadata": {
+        "id": "YqXjLuWavNxW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have build a recipe, and actually confirmed that the data will be pre-processed appropriately, let's now build a regression model to answer the question: `What price can I expect of a given pumpkin package?`\n",
+        "\n",
+        "#### Train a linear regression model using the training set\n",
+        "\n",
+        "As you may have already figured out, the column *price* is the `outcome` variable while the *package* column is the `predictor` variable.\n",
+        "\n",
+        "To do this, we'll first split the data such that 80% goes into training and 20% into test set, then define a recipe that will encode the predictor column into a set of integers, then build a model specification. We won't prep and bake our recipe since we already know it will preprocess the data as expected."
+      ],
+      "metadata": {
+        "id": "Pq0bSzCevW-h"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "set.seed(2056)\n",
+        "# Split the data into training and test sets\n",
+        "pumpkins_split <- new_pumpkins %>% \n",
+        "  initial_split(prop = 0.8)\n",
+        "\n",
+        "\n",
+        "# Extract training and test data\n",
+        "pumpkins_train <- training(pumpkins_split)\n",
+        "pumpkins_test <- testing(pumpkins_split)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Create a recipe for preprocessing the data\n",
+        "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+        "  step_integer(all_predictors(), zero_based = TRUE)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Create a linear model specification\n",
+        "lm_spec <- linear_reg() %>% \n",
+        "  set_engine(\"lm\") %>% \n",
+        "  set_mode(\"regression\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "CyoEh_wuvcLv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Good job! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities. How's that for your peace of mind!🤩\n",
+        "\n",
+        "In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.\n",
+        "\n",
+        "So let's bundle everything up into a workflow!📦"
+      ],
+      "metadata": {
+        "id": "G3zF_3DqviFJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Hold modelling components in a workflow\n",
+        "lm_wf <- workflow() %>% \n",
+        "  add_recipe(lm_pumpkins_recipe) %>% \n",
+        "  add_model(lm_spec)\n",
+        "\n",
+        "# Print out the workflow\n",
+        "lm_wf"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "T3olroU3v-WX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "👌 Into the bargain, a workflow can be fit/trained in much the same way a model can."
+      ],
+      "metadata": {
+        "id": "zd1A5tgOwEPX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Train the model\n",
+        "lm_wf_fit <- lm_wf %>% \n",
+        "  fit(data = pumpkins_train)\n",
+        "\n",
+        "# Print the model coefficients learned \n",
+        "lm_wf_fit"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "NhJagFumwFHf"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.\n",
+        "\n",
+        "\n",
+        "#### Evaluate model performance using the test set\n",
+        "\n",
+        "It's time to see how the model performed 📏! How do we do this?\n",
+        "\n",
+        "Now that we've trained the model, we can use it to make predictions for the test_set using `parsnip::predict()`. Then we can compare these predictions to the actual label values to evaluate how well (or not!) the model is working.\n",
+        "\n",
+        "Let's start with making predictions for the test set then bind the columns to the test set."
+      ],
+      "metadata": {
+        "id": "_4QkGtBTwItF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make predictions for the test set\n",
+        "predictions <- lm_wf_fit %>% \n",
+        "  predict(new_data = pumpkins_test)\n",
+        "\n",
+        "\n",
+        "# Bind predictions to the test set\n",
+        "lm_results <- pumpkins_test %>% \n",
+        "  select(c(package, price)) %>% \n",
+        "  bind_cols(predictions)\n",
+        "\n",
+        "\n",
+        "# Print the first ten rows of the tibble\n",
+        "lm_results %>% \n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "UFZzTG0gwTs9"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Yes, you have just trained a model and used it to make predictions!🔮 Is it any good, let's evaluate the model's performance!\n",
+        "\n",
+        "In Tidymodels, we do this using `yardstick::metrics()`! For linear regression, let's focus on the following metrics:\n",
+        "\n",
+        "-   `Root Mean Square Error (RMSE)`: The square root of the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). This yields an absolute metric in the same unit as the label (in this case, the price of a pumpkin). The smaller the value, the better the model (in a simplistic sense, it represents the average price by which the predictions are wrong!)\n",
+        "\n",
+        "-   `Coefficient of Determination (usually known as R-squared or R2)`: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain."
+      ],
+      "metadata": {
+        "id": "0A5MjzM7wW9M"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Evaluate performance of linear regression\n",
+        "metrics(data = lm_results,\n",
+        "        truth = price,\n",
+        "        estimate = .pred)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "reJ0UIhQwcEH"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "There goes the model performance. Let's see if we can get a better indication by visualizing a scatter plot of the package and price then use the predictions made to overlay a line of best fit.\n",
+        "\n",
+        "This means we'll have to prep and bake the test set in order to encode the package column then bind this to the predictions made by our model."
+      ],
+      "metadata": {
+        "id": "fdgjzjkBwfWt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Encode package column\n",
+        "package_encode <- lm_pumpkins_recipe %>% \n",
+        "  prep() %>% \n",
+        "  bake(new_data = pumpkins_test) %>% \n",
+        "  select(package)\n",
+        "\n",
+        "\n",
+        "# Bind encoded package column to the results\n",
+        "lm_results <- lm_results %>% \n",
+        "  bind_cols(package_encode %>% \n",
+        "              rename(package_integer = package)) %>% \n",
+        "  relocate(package_integer, .after = package)\n",
+        "\n",
+        "\n",
+        "# Print new results data frame\n",
+        "lm_results %>% \n",
+        "  slice_head(n = 5)\n",
+        "\n",
+        "\n",
+        "# Make a scatter plot\n",
+        "lm_results %>% \n",
+        "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+        "  geom_point(size = 1.6) +\n",
+        "  # Overlay a line of best fit\n",
+        "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+        "  xlab(\"package\")\n",
+        "  \n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "R0nw719lwkHE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Great! As you can see, the linear regression model does not really well generalize the relationship between a package and its corresponding price.\n",
+        "\n",
+        "🎃 Congratulations, you just created a model that can help predict the price of a few varieties of pumpkins. Your holiday pumpkin patch will be beautiful. But you can probably create a better model!\n",
+        "\n",
+        "## 5. Build a polynomial regression model\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/linear-polynomial.png\"\n",
+        "   width=\"800\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/linear-polynomial.png){width=\"800\"}-->"
+      ],
+      "metadata": {
+        "id": "HOCqJXLTwtWI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Sometimes our data may not have a linear relationship, but we still want to predict an outcome. Polynomial regression can help us make predictions for more complex non-linear relationships.\n",
+        "\n",
+        "Take for instance the relationship between the package and price for our pumpkins data set. While sometimes there's a linear relationship between variables - the bigger the pumpkin in volume, the higher the price - sometimes these relationships can't be plotted as a plane or straight line.\n",
+        "\n",
+        "> ✅ Here are [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8) of data that could use polynomial regression\n",
+        ">\n",
+        "> Take another look at the relationship between Variety to Price in the previous plot. Does this scatterplot seem like it should necessarily be analyzed by a straight line? Perhaps not. In this case, you can try polynomial regression.\n",
+        ">\n",
+        "> ✅ Polynomials are mathematical expressions that might consist of one or more variables and coefficients\n",
+        "\n",
+        "#### Train a polynomial regression model using the training set\n",
+        "\n",
+        "Polynomial regression creates a *curved line* to better fit nonlinear data.\n",
+        "\n",
+        "Let's see whether a polynomial model will perform better in making predictions. We'll follow a somewhat similar procedure as we did before:\n",
+        "\n",
+        "-   Create a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding predictors and computing polynomials of degree *n*\n",
+        "\n",
+        "-   Build a model specification\n",
+        "\n",
+        "-   Bundle the recipe and model specification into a workflow\n",
+        "\n",
+        "-   Create a model by fitting the workflow\n",
+        "\n",
+        "-   Evaluate how well the model performs on the test data\n",
+        "\n",
+        "Let's get right into it!\n"
+      ],
+      "metadata": {
+        "id": "VcEIpRV9wzYr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Specify a recipe\r\n",
+        "poly_pumpkins_recipe <-\r\n",
+        "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+        "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+        "  step_poly(all_predictors(), degree = 4)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a model specification\r\n",
+        "poly_spec <- linear_reg() %>% \r\n",
+        "  set_engine(\"lm\") %>% \r\n",
+        "  set_mode(\"regression\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Bundle recipe and model spec into a workflow\r\n",
+        "poly_wf <- workflow() %>% \r\n",
+        "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+        "  add_model(poly_spec)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a model\r\n",
+        "poly_wf_fit <- poly_wf %>% \r\n",
+        "  fit(data = pumpkins_train)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print learned model coefficients\r\n",
+        "poly_wf_fit\r\n",
+        "\r\n",
+        "  "
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "63n_YyRXw3CC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Evaluate model performance\n",
+        "\n",
+        "👏👏You've built a polynomial model let's make predictions on the test set!"
+      ],
+      "metadata": {
+        "id": "-LHZtztSxDP0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make price predictions on test data\r\n",
+        "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+        "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+        "  relocate(.pred, .after = last_col())\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the results\r\n",
+        "poly_results %>% \r\n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "YUFpQ_dKxJGx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Woo-hoo, let's evaluate how the model performed on the test_set using `yardstick::metrics()`."
+      ],
+      "metadata": {
+        "id": "qxdyj86bxNGZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "metrics(data = poly_results, truth = price, estimate = .pred)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "8AW5ltkBxXDm"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤩🤩 Much better performance.\n",
+        "\n",
+        "The `rmse` decreased from about 7. to about 3. an indication that of a reduced error between the actual price and the predicted price. You can *loosely* interpret this as meaning that on average, incorrect predictions are wrong by around \\$3. The `rsq` increased from about 0.4 to 0.8.\n",
+        "\n",
+        "All these metrics indicate that the polynomial model performs way better than the linear model. Good job!\n",
+        "\n",
+        "Let's see if we can visualize this!"
+      ],
+      "metadata": {
+        "id": "6gLHNZDwxYaS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Bind encoded package column to the results\r\n",
+        "poly_results <- poly_results %>% \r\n",
+        "  bind_cols(package_encode %>% \r\n",
+        "              rename(package_integer = package)) %>% \r\n",
+        "  relocate(package_integer, .after = package)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print new results data frame\r\n",
+        "poly_results %>% \r\n",
+        "  slice_head(n = 5)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Make a scatter plot\r\n",
+        "poly_results %>% \r\n",
+        "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+        "  geom_point(size = 1.6) +\r\n",
+        "  # Overlay a line of best fit\r\n",
+        "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+        "  xlab(\"package\")\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "A83U16frxdF1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see a curved line that fits your data better! 🤩\n",
+        "\n",
+        "You can make this more smoother by passing a polynomial formula to `geom_smooth` like this:"
+      ],
+      "metadata": {
+        "id": "4U-7aHOVxlGU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a scatter plot\r\n",
+        "poly_results %>% \r\n",
+        "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+        "  geom_point(size = 1.6) +\r\n",
+        "  # Overlay a line of best fit\r\n",
+        "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+        "  xlab(\"package\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "5vzNT0Uexm-w"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Much like a smooth curve!🤩\n",
+        "\n",
+        "Here's how you would make a new prediction:"
+      ],
+      "metadata": {
+        "id": "v9u-wwyLxq4G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a hypothetical data frame\r\n",
+        "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+        "\r\n",
+        "# Make predictions using linear model\r\n",
+        "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+        "\r\n",
+        "# Make predictions using polynomial model\r\n",
+        "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+        "\r\n",
+        "# Return predictions in a list\r\n",
+        "list(\"linear model prediction\" = lm_pred, \r\n",
+        "     \"polynomial model prediction\" = poly_pred)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "jRPSyfQGxuQv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The `polynomial model` prediction does make sense, given the scatter plots of `price` and `package`! And, if this is a better model than the previous one, looking at the same data, you need to budget for these more expensive pumpkins!\n",
+        "\n",
+        "🏆 Well done! You created two regression models in one lesson. In the final section on regression, you will learn about logistic regression to determine categories.\n",
+        "\n",
+        "## **🚀Challenge**\n",
+        "\n",
+        "Test several different variables in this notebook to see how correlation corresponds to model accuracy.\n",
+        "\n",
+        "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)\n",
+        "\n",
+        "## **Review & Self Study**\n",
+        "\n",
+        "In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)\n",
+        "\n",
+        "If you want to learn more about how to use the amazing Tidymodels framework, please check out the following resources:\n",
+        "\n",
+        "-   Tidymodels website: [Get started with Tidymodels](https://www.tidymodels.org/start/)\n",
+        "\n",
+        "-   Max Kuhn and Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+        "\n",
+        "###### **THANK YOU TO:**\n",
+        "\n",
+        "[Allison Horst](https://twitter.com/allison_horst?lang=en) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+      ],
+      "metadata": {
+        "id": "8zOLOWqMxzk5"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/3-Linear/solution/R/lesson_3.Rmd b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
new file mode 100644
index 000000000..7997b0d8f
--- /dev/null
+++ b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
@@ -0,0 +1,679 @@
+---
+title: 'Build a regression model: linear and polynomial regression models'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Linear and Polynomial Regression for Pumpkin Pricing - Lesson 3
+
+![Infographic by Dasani Madipalli](../../images/linear-polynomial.png){width="800"}
+
+#### Introduction
+
+So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using `ggplot2`.💪
+
+Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: *basic linear regression* and *polynomial regression*, along with some of the math underlying these techniques.
+
+> Throughout this curriculum, we assume minimal knowledge of math, and seek to make it accessible for students coming from other fields, so watch for notes, 🧮 callouts, diagrams, and other learning tools to aid in comprehension.
+
+#### Preparation
+
+As a reminder, you are loading this data so as to ask questions of it.
+
+-   When is the best time to buy pumpkins?
+
+-   What price can I expect of a case of miniature pumpkins?
+
+-   Should I buy them in half-bushel baskets or by the 1 1/9 bushel box? Let's keep digging into this data.
+
+In the previous lesson, you created a `tibble` (a modern reimagining of the data frame) and populated it with part of the original dataset, standardizing the pricing by the bushel. By doing that, however, you were only able to gather about 400 data points and only for the fall months. Maybe we can get a little more detail about the nature of the data by cleaning it more? We'll see... 🕵️‍♀️
+
+For this task, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.
+
+-   `corrplot`: The [corrplot package](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "janitor", "corrplot"))`
+
+The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman")) install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, janitor, corrplot)
+```
+
+We'll later load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)
+
+## 1. A linear regression line
+
+As you learned in Lesson 1, the goal of a linear regression exercise is to be able to plot a *line* *of* *best fit* to:
+
+-   **Show variable relationships**. Show the relationship between variables
+
+-   **Make predictions**. Make accurate predictions on where a new data point would fall in relationship to that line.
+
+To draw this type of line, we use a statistical technique called **Least-Squares Regression**. The term `least-squares` means that all the data points surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. As such, the line of best fit is the line that gives us the lowest value for the sum of the squared errors - hence the name *least squares regression*.
+
+We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
+
+> **🧮 Show me the math**
+>
+> This line, called the *line of best fit* can be expressed by [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression):
+>
+>     Y = a + bX
+>
+> `X` is the '`explanatory variable` or `predictor`'. `Y` is the '`dependent variable` or `outcome`'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`.
+>
+> ![Infographic by Jen Looper](../../images/slope.png){width="400"}
+>
+> First, calculate the slope `b`.
+>
+> In other words, and referring to our pumpkin data's original question: "predict the price of a pumpkin per bushel by month", `X` would refer to the price and `Y` would refer to the month of sale.
+>
+> ![Infographic by Jen Looper](../../images/calculation.png)
+>
+> Calculate the value of Y. If you're paying around \$4, it must be April!
+>
+> The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.
+>
+> You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site. Also visit [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) to watch how the numbers' values impact the line.
+
+Not so scary, right? 🤓
+
+#### Correlation
+
+One more term to understand is the **Correlation Coefficient** between given X and Y variables. Using a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.
+
+A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.
+
+## **2. A dance with data: creating a data frame that will be used for modelling**
+
+![Artwork by \@allison_horst](../../images/janitor.jpg){width="700"}
+
+Load up required libraries and dataset. Convert the data to a data frame containing a subset of the data:
+
+-   Only get pumpkins priced by the bushel
+
+-   Convert the date to a month
+
+-   Calculate the price to be an average of high and low prices
+
+-   Convert the price to reflect the pricing by bushel quantity
+
+> We covered these steps in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).
+
+```{r load_tidy_verse_models, message=F, warning=F}
+# Load the core Tidyverse packages
+library(tidyverse)
+library(lubridate)
+
+# Import the pumpkins data
+pumpkins <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv")
+
+
+# Get a glimpse and dimensions of the data
+glimpse(pumpkins)
+
+
+# Print the first 50 rows of the data set
+pumpkins %>% 
+  slice_head(n = 5)
+
+
+```
+
+In the spirit of sheer adventure, let's explore the [`janitor package`](github.com/sfirke/janitor) that provides simple functions for examining and cleaning dirty data. For instance, let's take a look at the column names for our data:
+
+```{r col_names}
+# Return column names
+pumpkins %>% 
+  names()
+
+```
+
+🤔 We can do better. Let's make these column names `friendR` by converting them to the [snake_case](https://en.wikipedia.org/wiki/Snake_case) convention using `janitor::clean_names`. To find out more about this function: `?clean_names`
+
+```{r friendR}
+# Clean names to the snake_case convention
+pumpkins <- pumpkins %>% 
+  clean_names(case = "snake")
+
+# Return column names
+pumpkins %>% 
+  names()
+
+```
+
+Much tidyR 🧹! Now, a dance with the data using `dplyr` as in the previous lesson! 💃
+
+```{r prep_data, message=F, warning=F}
+# Select desired columns
+pumpkins <- pumpkins %>% 
+  select(variety, city_name, package, low_price, high_price, date)
+
+
+
+# Extract the month from the dates to a new column
+pumpkins <- pumpkins %>%
+  mutate(date = mdy(date),
+         month = month(date)) %>% 
+  select(-date)
+
+
+
+# Create a new column for average Price
+pumpkins <- pumpkins %>% 
+  mutate(price = (low_price + high_price)/2)
+
+
+# Retain only pumpkins with the string "bushel"
+new_pumpkins <- pumpkins %>% 
+  filter(str_detect(string = package, pattern = "bushel"))
+
+
+# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel
+new_pumpkins <- new_pumpkins %>% 
+  mutate(price = case_when(
+    str_detect(package, "1 1/9") ~ price/(1.1),
+    str_detect(package, "1/2") ~ price*2,
+    TRUE ~ price))
+
+# Relocate column positions
+new_pumpkins <- new_pumpkins %>% 
+  relocate(month, .before = variety)
+
+
+# Display the first 5 rows
+new_pumpkins %>% 
+  slice_head(n = 5)
+```
+
+Good job!👌 You now have a clean, tidy data set on which you can build your new regression model!
+
+Mind a scatter plot?
+
+```{r scatter_price_month}
+# Set theme
+theme_set(theme_light())
+
+# Make a scatter plot of month and price
+new_pumpkins %>% 
+  ggplot(mapping = aes(x = month, y = price)) +
+  geom_point(size = 1.6)
+
+```
+
+A scatter plot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion.
+
+Let's take a look at our modelling data again:
+
+```{r modelling data}
+# Display first 5 rows
+new_pumpkins %>% 
+  slice_head(n = 5)
+
+```
+
+What if we wanted to predict the `price` of a pumpkin based on the `city` or `package` columns which are of type character? Or even more simply, how could we find the correlation (which requires both of its inputs to be numeric) between, say, `package` and `price`? 🤷🤷
+
+Machine learning models work best with numeric features rather than text values, so you generally need to convert categorical features into numeric representations.
+
+This means that we have to find a way to reformat our predictors to make them easier for a model to use effectively, a process known as `feature engineering`.
+
+## 3. Preprocessing data for modelling with recipes 👩‍🍳👨‍🍳
+
+Activities that reformat predictor values to make them easier for a model to use effectively has been termed `feature engineering`.
+
+Different models have different preprocessing requirements. For instance, least squares requires `encoding categorical variables` such as month, variety and city_name. This simply involves `translating` a column with `categorical values` into one or more `numeric columns` that take the place of the original.
+
+For example, suppose your data includes the following categorical feature:
+
+|  city   |
+|:-------:|
+| Denver  |
+| Nairobi |
+|  Tokyo  |
+
+You can apply *ordinal encoding* to substitute a unique integer value for each category, like this:
+
+| city |
+|:----:|
+|  0   |
+|  1   |
+|  2   |
+
+And that's what we'll do to our data!
+
+In this section, we'll explore another amazing Tidymodels package: [recipes](https://tidymodels.github.io/recipes/) - which is designed to help you preprocess your data **before** training your model. At its core, a recipe is an object that defines what steps should be applied to a data set in order to get it ready for modelling.
+
+Now, let's create a recipe that prepares our data for modelling by substituting a unique integer for all the observations in the predictor columns:
+
+```{r pumpkins_recipe}
+# Specify a recipe
+pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% 
+  step_integer(all_predictors(), zero_based = TRUE)
+
+
+# Print out the recipe
+pumpkins_recipe
+
+```
+
+Awesome! 👏 We just created our first recipe that specifies an outcome (price) and its corresponding predictors and that all the predictor columns should be encoded into a set of integers 🙌! Let's quickly break it down:
+
+-   The call to `recipe()` with a formula tells the recipe the *roles* of the variables using `new_pumpkins` data as the reference. For instance the `price` column has been assigned an `outcome` role while the rest of the columns have been assigned a `predictor` role.
+
+-   `step_integer(all_predictors(), zero_based = TRUE)` specifies that all the predictors should be converted into a set of integers with the numbering starting at 0.
+
+We are sure you may be having thoughts such as: "This is so cool!! But what if I needed to confirm that the recipes are doing exactly what I expect them to do? 🤔"
+
+That's an awesome thought! You see, once your recipe is defined, you can estimate the parameters required to actually preprocess the data, and then extract the processed data. You don't typically need to do this when you use Tidymodels (we'll see the normal convention in just a minute-\> `workflows`) but it can come in handy when you want to do some kind of sanity check for confirming that recipes are doing what you expect.
+
+For that, you'll need two more verbs: `prep()` and `bake()` and as always, our little R friends by [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) help you in understanding this better!
+
+![Artwork by \@allison_horst](../images/recipes.png){width="550"}
+
+[`prep()`](https://recipes.tidymodels.org/reference/prep.html): estimates the required parameters from a training set that can be later applied to other data sets. For instance, for a given predictor column, what observation will be assigned integer 0 or 1 or 2 etc
+
+[`bake()`](https://recipes.tidymodels.org/reference/bake.html): takes a prepped recipe and applies the operations to any data set.
+
+That said, lets prep and bake our recipes to really confirm that under the hood, the predictor columns will be first encoded before a model is fit.
+
+```{r prep_bake}
+# Prep the recipe
+pumpkins_prep <- prep(pumpkins_recipe)
+
+# Bake the recipe to extract a preprocessed new_pumpkins data
+baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)
+
+# Print out the baked data set
+baked_pumpkins %>% 
+  slice_head(n = 10)
+```
+
+Woo-hoo!🥳 The processed data `baked_pumpkins` has all it's predictors encoded confirming that indeed the preprocessing steps defined as our recipe will work as expected. This makes it harder for you to read but much more intelligible for Tidymodels! Take some time to find out what observation has been mapped to a corresponding integer.
+
+It is also worth mentioning that `baked_pumpkins` is a data frame that we can perform computations on.
+
+For instance, let's try to find a good correlation between two points of your data to potentially build a good predictive model. We'll use the function `cor()` to do this. Type `?cor()` to find out more about the function.
+
+```{r corr}
+# Find the correlation between the city_name and the price
+cor(baked_pumpkins$city_name, baked_pumpkins$price)
+
+# Find the correlation between the package and the price
+cor(baked_pumpkins$package, baked_pumpkins$price)
+
+```
+
+As it turns out, there's only weak correlation between the City and Price. However there's a bit better correlation between the Package and its Price. That makes sense, right? Normally, the bigger the produce box, the higher the price.
+
+While we are at it, let's also try and visualize a correlation matrix of all the columns using the `corrplot` package.
+
+```{r corrplot}
+# Load the corrplot package
+library(corrplot)
+
+# Obtain correlation matrix
+corr_mat <- cor(baked_pumpkins %>% 
+                  # Drop columns that are not really informative
+                  select(-c(low_price, high_price)))
+
+# Make a correlation plot between the variables
+corrplot(corr_mat, method = "shade", shade.col = NA, tl.col = "black", tl.srt = 45, addCoef.col = "black", cl.pos = "n", order = "original")
+
+```
+
+🤩🤩 Much better.
+
+A good question to now ask of this data will be: '`What price can I expect of a given pumpkin package?`' Let's get right into it!
+
+> Note: When you **`bake()`** the prepped recipe **`pumpkins_prep`** with **`new_data = NULL`**, you extract the processed (i.e. encoded) training data. If you had another data set for example a test set and would want to see how a recipe would pre-process it, you would simply bake **`pumpkins_prep`** with **`new_data = test_set`**
+
+## 4. Build a linear regression model
+
+![Infographic by Dasani Madipalli](../../images/linear-polynomial.png){width="800"}
+
+Now that we have build a recipe, and actually confirmed that the data will be pre-processed appropriately, let's now build a regression model to answer the question: `What price can I expect of a given pumpkin package?`
+
+#### Train a linear regression model using the training set
+
+As you may have already figured out, the column *price* is the `outcome` variable while the *package* column is the `predictor` variable.
+
+To do this, we'll first split the data such that 80% goes into training and 20% into test set, then define a recipe that will encode the predictor column into a set of integers, then build a model specification. We won't prep and bake our recipe since we already know it will preprocess the data as expected.
+
+```{r lm_rec_spec}
+set.seed(2056)
+# Split the data into training and test sets
+pumpkins_split <- new_pumpkins %>% 
+  initial_split(prop = 0.8)
+
+
+# Extract training and test data
+pumpkins_train <- training(pumpkins_split)
+pumpkins_test <- testing(pumpkins_split)
+
+
+
+# Create a recipe for preprocessing the data
+lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% 
+  step_integer(all_predictors(), zero_based = TRUE)
+
+
+
+# Create a linear model specification
+lm_spec <- linear_reg() %>% 
+  set_engine("lm") %>% 
+  set_mode("regression")
+
+
+```
+
+Good job! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities. How's that for your peace of mind!🤩
+
+In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.
+
+So let's bundle everything up into a workflow!📦
+
+```{r lm_workflow}
+# Hold modelling components in a workflow
+lm_wf <- workflow() %>% 
+  add_recipe(lm_pumpkins_recipe) %>% 
+  add_model(lm_spec)
+
+# Print out the workflow
+lm_wf
+
+```
+
+👌 Into the bargain, a workflow can be fit/trained in much the same way a model can.
+
+```{r lm_wf_fit}
+# Train the model
+lm_wf_fit <- lm_wf %>% 
+  fit(data = pumpkins_train)
+
+# Print the model coefficients learned 
+lm_wf_fit
+
+```
+
+From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.
+
+#### Evaluate model performance using the test set
+
+It's time to see how the model performed 📏! How do we do this?
+
+Now that we've trained the model, we can use it to make predictions for the test_set using `parsnip::predict()`. Then we can compare these predictions to the actual label values to evaluate how well (or not!) the model is working.
+
+Let's start with making predictions for the test set then bind the columns to the test set.
+
+```{r lm_pred}
+# Make predictions for the test set
+predictions <- lm_wf_fit %>% 
+  predict(new_data = pumpkins_test)
+
+
+# Bind predictions to the test set
+lm_results <- pumpkins_test %>% 
+  select(c(package, price)) %>% 
+  bind_cols(predictions)
+
+
+# Print the first ten rows of the tibble
+lm_results %>% 
+  slice_head(n = 10)
+```
+
+Yes, you have just trained a model and used it to make predictions!🔮 Is it any good, let's evaluate the model's performance!
+
+In Tidymodels, we do this using `yardstick::metrics()`! For linear regression, let's focus on the following metrics:
+
+-   `Root Mean Square Error (RMSE)`: The square root of the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). This yields an absolute metric in the same unit as the label (in this case, the price of a pumpkin). The smaller the value, the better the model (in a simplistic sense, it represents the average price by which the predictions are wrong!)
+
+-   `Coefficient of Determination (usually known as R-squared or R2)`: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain.
+
+```{r lm_yardstick}
+# Evaluate performance of linear regression
+metrics(data = lm_results,
+        truth = price,
+        estimate = .pred)
+
+
+```
+
+There goes the model performance. Let's see if we can get a better indication by visualizing a scatter plot of the package and price then use the predictions made to overlay a line of best fit.
+
+This means we'll have to prep and bake the test set in order to encode the package column then bind this to the predictions made by our model.
+
+```{r lm_plot}
+# Encode package column
+package_encode <- lm_pumpkins_recipe %>% 
+  prep() %>% 
+  bake(new_data = pumpkins_test) %>% 
+  select(package)
+
+
+# Bind encoded package column to the results
+lm_results <- lm_results %>% 
+  bind_cols(package_encode %>% 
+              rename(package_integer = package)) %>% 
+  relocate(package_integer, .after = package)
+
+
+# Print new results data frame
+lm_results %>% 
+  slice_head(n = 5)
+
+
+# Make a scatter plot
+lm_results %>% 
+  ggplot(mapping = aes(x = package_integer, y = price)) +
+  geom_point(size = 1.6) +
+  # Overlay a line of best fit
+  geom_line(aes(y = .pred), color = "orange", size = 1.2) +
+  xlab("package")
+  
+
+
+```
+
+Great! As you can see, the linear regression model does not really well generalize the relationship between a package and its corresponding price.
+
+🎃 Congratulations, you just created a model that can help predict the price of a few varieties of pumpkins. Your holiday pumpkin patch will be beautiful. But you can probably create a better model!
+
+## 5. Build a polynomial regression model
+
+![Infographic by Dasani Madipalli](../../images/linear-polynomial.png){width="800"}
+
+Sometimes our data may not have a linear relationship, but we still want to predict an outcome. Polynomial regression can help us make predictions for more complex non-linear relationships.
+
+Take for instance the relationship between the package and price for our pumpkins data set. While sometimes there's a linear relationship between variables - the bigger the pumpkin in volume, the higher the price - sometimes these relationships can't be plotted as a plane or straight line.
+
+> ✅ Here are [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8) of data that could use polynomial regression
+>
+> Take another look at the relationship between Variety to Price in the previous plot. Does this scatterplot seem like it should necessarily be analyzed by a straight line? Perhaps not. In this case, you can try polynomial regression.
+>
+> ✅ Polynomials are mathematical expressions that might consist of one or more variables and coefficients
+
+#### Train a polynomial regression model using the training set
+
+Polynomial regression creates a *curved line* to better fit nonlinear data.
+
+Let's see whether a polynomial model will perform better in making predictions. We'll follow a somewhat similar procedure as we did before:
+
+-   Create a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding predictors and computing polynomials of degree *n*
+
+-   Build a model specification
+
+-   Bundle the recipe and model specification into a workflow
+
+-   Create a model by fitting the workflow
+
+-   Evaluate how well the model performs on the test data
+
+Let's get right into it!
+
+```{r polynomial_reg}
+# Specify a recipe
+poly_pumpkins_recipe <-
+  recipe(price ~ package, data = pumpkins_train) %>%
+  step_integer(all_predictors(), zero_based = TRUE) %>% 
+  step_poly(all_predictors(), degree = 4)
+
+
+# Create a model specification
+poly_spec <- linear_reg() %>% 
+  set_engine("lm") %>% 
+  set_mode("regression")
+
+
+# Bundle recipe and model spec into a workflow
+poly_wf <- workflow() %>% 
+  add_recipe(poly_pumpkins_recipe) %>% 
+  add_model(poly_spec)
+
+
+# Create a model
+poly_wf_fit <- poly_wf %>% 
+  fit(data = pumpkins_train)
+
+
+# Print learned model coefficients
+poly_wf_fit
+
+  
+
+```
+
+#### Evaluate model performance
+
+👏👏You've built a polynomial model let's make predictions on the test set!
+
+```{r poly_predict}
+# Make price predictions on test data
+poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% 
+  bind_cols(pumpkins_test %>% select(c(package, price))) %>% 
+  relocate(.pred, .after = last_col())
+
+
+# Print the results
+poly_results %>% 
+  slice_head(n = 10)
+```
+
+Woo-hoo , let's evaluate how the model performed on the test_set using `yardstick::metrics()`.
+
+```{r poly_eval}
+metrics(data = poly_results, truth = price, estimate = .pred)
+```
+
+🤩🤩 Much better performance.
+
+The `rmse` decreased from about 7. to about 3. an indication that of a reduced error between the actual price and the predicted price. You can *loosely* interpret this as meaning that on average, incorrect predictions are wrong by around \$3. The `rsq` increased from about 0.4 to 0.8.
+
+All these metrics indicate that the polynomial model performs way better than the linear model. Good job!
+
+Let's see if we can visualize this!
+
+```{r poly_viz}
+# Bind encoded package column to the results
+poly_results <- poly_results %>% 
+  bind_cols(package_encode %>% 
+              rename(package_integer = package)) %>% 
+  relocate(package_integer, .after = package)
+
+
+# Print new results data frame
+poly_results %>% 
+  slice_head(n = 5)
+
+
+# Make a scatter plot
+poly_results %>% 
+  ggplot(mapping = aes(x = package_integer, y = price)) +
+  geom_point(size = 1.6) +
+  # Overlay a line of best fit
+  geom_line(aes(y = .pred), color = "midnightblue", size = 1.2) +
+  xlab("package")
+
+
+
+```
+
+You can see a curved line that fits your data better! 🤩
+
+You can make this more smoother by passing a polynomial formula to `geom_smooth` like this:
+
+```{r smooth curve}
+# Make a scatter plot
+poly_results %>% 
+  ggplot(mapping = aes(x = package_integer, y = price)) +
+  geom_point(size = 1.6) +
+  # Overlay a line of best fit
+  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = "midnightblue", size = 1.2, se = FALSE) +
+  xlab("package")
+
+
+
+
+```
+
+Much like a smooth curve!🤩
+
+Here's how you would make a new prediction:
+
+```{r predict}
+# Make a hypothetical data frame
+hypo_tibble <- tibble(package = "bushel baskets")
+
+# Make predictions using linear model
+lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)
+
+# Make predictions using polynomial model
+poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)
+
+# Return predictions in a list
+list("linear model prediction" = lm_pred, 
+     "polynomial model prediction" = poly_pred)
+
+
+```
+
+The `polynomial model` prediction does make sense, given the scatter plots of `price` and `package`! And, if this is a better model than the previous one, looking at the same data, you need to budget for these more expensive pumpkins!
+
+🏆 Well done! You created two regression models in one lesson. In the final section on regression, you will learn about logistic regression to determine categories.
+
+## **🚀Challenge**
+
+Test several different variables in this notebook to see how correlation corresponds to model accuracy.
+
+## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+
+## **Review & Self Study**
+
+In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+
+If you want to learn more about how to use the amazing Tidymodels framework, please check out the following resources:
+
+-   Tidymodels website: [Get started with Tidymodels](https://www.tidymodels.org/start/)
+
+-   Max Kuhn and Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*
+
+###### **THANK YOU TO:**
+
+[Allison Horst](https://twitter.com/allison_horst?lang=en) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).
diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
index f2ae6b7cf..ce2ee4098 100644
--- a/2-Regression/3-Linear/translations/README.id.md
+++ b/2-Regression/3-Linear/translations/README.id.md
@@ -2,7 +2,7 @@
 
 ![Infografik regresi linear vs polinomial](../images/linear-polynomial.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 ### Pembukaan
 
 Selama ini kamu telah menjelajahi apa regresi itu dengan data contoh yang dikumpulkan dari *dataset* harga labu yang kita akan gunakan terus sepanjang pelajaran ini. Kamu juga telah memvisualisasikannya dengan Matplotlib.
@@ -324,7 +324,7 @@ Itu sangat masuk akal dengan bagan sebelumnya! Selain itu, jika ini model lebih
 
 Coba-cobalah variabel-variabel yang lain di *notebook* ini untuk melihat bagaimana korelasi berhubungan dengan akurasi model.
 
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## Review & Pembelajaran Mandiri
 
diff --git a/2-Regression/3-Linear/translations/README.it.md b/2-Regression/3-Linear/translations/README.it.md
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+# Costruire un modello di regressione usando Scikit-learn: regressione in due modi
+
+![Infografica di regressione lineare e polinomiale](../images/linear-polynomial.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/?loc=it)
+
+### Introduzione
+
+Finora si è esplorato cos'è la regressione con dati di esempio raccolti dall'insieme di dati relativo ai prezzi della zucca, che verrà usato in questa lezione. Lo si è anche visualizzato usando Matplotlib.
+
+Ora si è  pronti per approfondire la regressione per machine learning. In questa lezione si imparerà di più su due tipi di regressione: _regressione lineare di base_ e _regressione polinomiale_, insieme ad alcuni dei calcoli alla base di queste tecniche.
+
+> In questo programma di studi, si assume una conoscenza minima della matematica, e si cerca di renderla accessibile agli studenti provenienti da altri campi, quindi si faccia attenzione a note, 🧮 didascalie, diagrammi e altri strumenti di apprendimento che aiutano la comprensione.
+
+### Prerequisito
+
+Si dovrebbe ormai avere familiarità con la struttura dei dati della zucca che si sta esaminando. Lo si può trovare precaricato e prepulito nel file _notebook.ipynb_ di questa lezione. Nel file, il prezzo della zucca viene visualizzato per bushel (staio) in un nuovo dataframe. Assicurasi di poter eseguire questi notebook nei kernel in Visual Studio Code.
+
+### Preparazione
+
+Come promemoria, si stanno caricando questi dati in modo da porre domande su di essi.
+
+- Qual è il momento migliore per comprare le zucche?
+- Che prezzo ci si può  aspettare da una cassa di zucche in miniatura?
+- Si devono acquistare in cestini da mezzo bushel o a scatola da 1 1/9 bushel? Si continua a scavare in questi dati.
+
+Nella lezione precedente, è stato creato un dataframe Pandas e si è popolato con parte dell'insieme di dati originale, standardizzando il prezzo per lo bushel. In questo modo, tuttavia, si sono potuti raccogliere solo circa 400 punti dati e solo per i mesi autunnali.
+
+Si dia  un'occhiata ai dati precaricati nel notebook di accompagnamento di questa lezione. I dati sono precaricati e viene tracciato un grafico a dispersione iniziale per mostrare i dati mensili. Forse si può ottenere qualche dettaglio in più sulla natura dei dati pulendoli ulteriormente.
+
+## Una linea di regressione lineare
+
+Come si è appreso nella lezione 1, l'obiettivo di un esercizio di regressione lineare è essere in grado di tracciare una linea per:
+
+- **Mostrare le relazioni tra variabili**.
+- **Fare previsioni**. Fare previsioni accurate su dove cadrebbe un nuovo punto dati in relazione a quella linea.
+
+È tipico della **Regressione dei Minimi Quadrati** disegnare questo tipo di linea. Il termine "minimi quadrati" significa che tutti i punti dati che circondano la linea di regressione sono elevati al quadrato e quindi sommati. Idealmente, quella somma finale è la più piccola possibile, perché si vuole un basso numero di errori, o `minimi quadrati`.
+
+Lo si fa perché si vuole modellare una linea che abbia la distanza cumulativa minima da tutti i  punti dati. Si esegue anche il quadrato dei termini prima di aggiungerli poiché  interessa la grandezza piuttosto che la direzione.
+
+> **🧮 Mostrami la matematica**
+>
+> Questa linea, chiamata _linea di miglior adattamento_ , può essere espressa da [un'equazione](https://en.wikipedia.org/wiki/Simple_linear_regression):
+>
+> ```
+> Y = a + bX
+> ```
+>
+> `X` è la "variabile esplicativa". `Y` è la "variabile dipendente". La pendenza della linea è `b` e `a` è l'intercetta di y, che si riferisce al valore di `Y` quando `X = 0`.
+>
+> ![calcolare la pendenza](../images/slope.png)
+>
+> Prima, calcolare la pendenza `b`. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+>
+> In altre parole, facendo riferimento alla domanda originale per i dati sulle zucche: "prevedere il prezzo di una zucca per bushel per mese", `X` si riferisce al prezzo e `Y` si riferirisce al mese di vendita.
+>
+> ![completare l'equazione](../images/calculation.png)
+>
+> Si calcola il valore di Y. Se si sta pagando circa $4, deve essere aprile! Infografica di [Jen Looper](https://twitter.com/jenlooper)
+>
+> La matematica che calcola la linea deve dimostrare la pendenza della linea, che dipende anche dall'intercetta, o dove `Y` si trova quando `X = 0`.
+>
+> Si può osservare il metodo di calcolo per questi valori sul sito web [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) . Si visiti anche [questo calcolatore dei minimi quadrati](https://www.mathsisfun.com/data/least-squares-calculator.html) per vedere come i valori dei numeri influiscono sulla linea.
+
+## Correlazione
+
+Un altro termine da comprendere è il **Coefficiente di Correlazione** tra determinate variabili X e Y. Utilizzando un grafico a dispersione, è possibile visualizzare rapidamente questo coefficiente. Un grafico con punti dati sparsi in una linea ordinata ha un'alta correlazione, ma un grafico con punti dati sparsi ovunque tra X e Y ha una bassa correlazione.
+
+Un buon modello di regressione lineare sarà quello che ha un Coefficiente di Correlazione alto (più vicino a 1 rispetto a 0) utilizzando il Metodo di Regressione dei Minimi Quadrati con una linea di regressione.
+
+✅ Eseguire il notebook che accompagna questa lezione e guardare il grafico a dispersione City to Price. I dati che associano la città al prezzo per le vendite di zucca sembrano avere una correlazione alta o bassa, secondo la propria interpretazione visiva del grafico a dispersione?
+
+
+## Preparare i dati per la regressione
+
+Ora che si ha una comprensione della matematica alla base di questo esercizio, si crea un modello di regressione per vedere se si può prevedere quale pacchetto di zucche avrà i migliori prezzi per zucca. Qualcuno che acquista zucche per una festa con tema un campo di zucche potrebbe desiderare che queste informazioni siano in grado di ottimizzare i propri acquisti di pacchetti di zucca per il campo.
+
+Dal momento che si utilizzerà Scikit-learn, non c'è motivo di farlo a mano (anche se si potrebbe!). Nel blocco di elaborazione dati principale del notebook della lezione, aggiungere una libreria da Scikit-learn per convertire automaticamente tutti i dati di tipo stringa in numeri:
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+Se si guarda ora il dataframe new_pumpkins, si vede che tutte le stringhe ora sono numeriche. Questo rende più difficile la lettura per un umano ma molto più comprensibile per Scikit-learn!
+Ora si possono prendere decisioni più consapevoli (non solo basate sull'osservazione di un grafico a dispersione) sui dati più adatti alla regressione.
+
+Si provi a trovare una buona correlazione tra due punti nei propri dati per costruire potenzialmente un buon modello predittivo. A quanto pare, c'è solo una debole correlazione tra la città e il prezzo:
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+Tuttavia, c'è una correlazione leggermente migliore tra il pacchetto e il suo prezzo. Ha senso, vero? Normalmente, più grande è la scatola dei prodotti, maggiore è il prezzo.
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+Una buona domanda da porre a questi dati sarà: "Che prezzo posso aspettarmi da un determinato pacchetto di zucca?"
+
+Si costruisce questo modello di regressione
+
+## Costruire un modello lineare
+
+Prima di costruire il  modello, si esegue un altro riordino  dei dati. Si eliminano tutti i dati nulli e si controlla ancora una volta che aspetto hanno i dati.
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+Quindi, si crea un nuovo dataframe da questo set minimo e lo si stampa:
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. Ora si possono  assegnare i dati delle coordinate X e y:
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+
+Cosa sta succedendo qui? Si sta usando [la notazione slice Python](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) per creare array per popolare `X` e `y`.
+
+2. Successivamente, si avvia le routine di creazione del modello di regressione:
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+   
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+   
+   pred = lin_reg.predict(X_test)
+   
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   Poiché la correlazione non è particolarmente buona, il modello prodotto non è molto accurato.
+
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+3. Si può visualizzare la linea tracciata nel processo:
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+   
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   
+   plt.show()
+   ```
+
+   ![Un grafico a dispersione che mostra il rapporto tra pacchetto e prezzo](../images/linear.png)
+
+4. Si testa il modello contro una varietà ipotetica:
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+
+   Il prezzo restituito per questa varietà mitologica è:
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+Quel numero ha senso, se la logica della linea di regressione è vera.
+
+🎃 Congratulazioni, si è appena creato un modello che può aiutare a prevedere il prezzo di alcune varietà di zucche. La zucca per le festività sarà bellissima. Ma probabilmente si può creare un modello migliore!
+
+## Regressione polinomiale
+
+Un altro tipo di regressione lineare è la regressione polinomiale. Mentre a volte c'è una relazione lineare tra le variabili - più grande è il volume della zucca, più alto è il prezzo - a volte queste relazioni non possono essere tracciate come un piano o una linea retta.
+
+✅ Ecco [alcuni altri esempi](https://online.stat.psu.edu/stat501/lesson/9/9.8) di dati che potrebbero utilizzare la regressione polinomiale
+
+Si dia un'altra occhiata alla relazione tra Varietà e Prezzo nel tracciato precedente. Questo grafico a dispersione deve essere necessariamente analizzato da una linea retta? Forse no. In questo caso, si può provare la regressione polinomiale.
+
+✅ I polinomi sono espressioni matematiche che possono essere costituite da una o più variabili e coefficienti
+
+La regressione polinomiale crea una linea curva per adattare meglio i dati non lineari.
+
+1. Viene ricreato un dataframe popolato con un segmento dei dati della zucca originale:
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+   
+   poly_pumpkins
+   ```
+
+Un buon modo per visualizzare le correlazioni tra i dati nei dataframe è visualizzarli in un grafico "coolwarm":
+
+2. Si usa il metodo `Background_gradient()` con `coolwarm` come valore dell'argomento:
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+
+   Questo codice crea una mappa di calore:
+   ![Una mappa di calore che mostra la correlazione dei dati](../images/heatmap.png)
+
+Guardando questo grafico, si può visualizzare la buona correlazione tra Pacchetto e Prezzo. Quindi si dovrebbe essere in grado di creare un modello un po' migliore dell'ultimo.
+
+### Creare una pipeline
+
+Scikit-learn include un'API utile per la creazione di modelli di regressione polinomiale: l'[API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline) `make_pipeline`. Viene creata una 'pipeline' che è una catena di stimatori. In questo caso, la pipeline include caratteristiche polinomiali o previsioni che formano un percorso non lineare.
+
+1. Si costruiscono le colonne X e y:
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. Si crea la pipeline chiamando il metodo `make_pipeline()` :
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+   
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+   
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   
+   pipeline.fit(np.array(X_train), y_train)
+   
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### Creare una sequenza
+
+A questo punto, è necessario creare un nuovo dataframe con dati _ordinati_ in modo che la pipeline possa creare una sequenza.
+
+Si aggiunge il seguente codice:
+
+```python
+df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+df.sort_values(by='x',inplace = True)
+points = pd.DataFrame(df).to_numpy()
+
+plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+plt.xlabel('Package')
+plt.ylabel('Price')
+plt.scatter(X,y, color="black")
+plt.show()
+```
+
+Si è  creato un nuovo dataframe chiamato `pd.DataFrame`. Quindi si sono ordinati i valori chiamando `sort_values()`. Alla fine si è  creato un grafico polinomiale:
+
+![Un grafico polinomiale che mostra la relazione tra pacchetto e prezzo](../images/polynomial.png)
+
+Si può vedere una linea curva che si adatta meglio ai  dati.
+
+Si verifica la precisione del modello:
+
+```python
+accuracy_score = pipeline.score(X_train,y_train)
+print('Model Accuracy: ', accuracy_score)
+```
+
+E voilà!
+
+```output
+Model Accuracy:  0.8537946517073784
+```
+
+Ecco, meglio! Si prova a prevedere un prezzo:
+
+### Fare una previsione
+
+E possibile inserire un nuovo valore e ottenere una previsione?
+
+Si chiami `predict()` per fare una previsione:
+
+```python
+pipeline.predict( np.array([ [2.75] ]) )
+```
+
+Viene data questa previsione:
+
+```output
+array([[46.34509342]])
+```
+
+Ha senso, visto il tracciato! Se questo è un modello migliore del precedente, guardando gli stessi dati, si deve preventivare queste zucche più costose!
+
+Ben fatto! Sono stati  creati due modelli di regressione in una lezione. Nella sezione finale sulla regressione, si imparerà a conoscere la regressione logistica per determinare le categorie.
+
+---
+
+## 🚀 Sfida
+
+Testare diverse variabili in questo notebook per vedere come la correlazione corrisponde all'accuratezza del modello.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+In questa lezione si è appreso della regressione lineare. Esistono altri tipi importanti di regressione. Leggere le tecniche Stepwise, Ridge, Lazo ed Elasticnet. Un buon corso per studiare per saperne di più è il [corso Stanford Statistical Learning](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+
+## Compito
+
+[Costruire un modello](assignment.it.md)
diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
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+# Scikit-learnを用いた回帰モデルの構築: 回帰を行う2つの方法
+
+![線形回帰 vs 多項式回帰 のインフォグラフィック](../images/linear-polynomial.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
+## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+### イントロダクション 
+
+これまで、このレッスンで使用するカボチャの価格データセットから集めたサンプルデータを使って、回帰とは何かを探ってきました。また、Matplotlibを使って可視化を行いました。
+
+これで、MLにおける回帰をより深く理解する準備が整いました。このレッスンでは、2種類の回帰について詳しく説明します。基本的な線形回帰 (_basic linear regression_)と多項式回帰 (_polynomial regression_)の2種類の回帰について、その基礎となる数学を学びます。
+
+> このカリキュラムでは、最低限の数学の知識を前提とし、他の分野の学生にも理解できるようにしていますので、理解を助けるためのメモ、🧮吹き出し、図などの学習ツールをご覧ください。
+
+### 事前確認
+
+ここでは、パンプキンデータの構造について説明しています。このレッスンの_notebook.ipynb_ファイルには、事前に読み込まれ、整形されたデータが入っています。このファイルでは、カボチャの価格がブッシェル単位で新しいデータフレームに表示されています。 これらのノートブックを、Visual Studio Codeのカーネルで実行できることを確認してください。
+
+### 準備
+
+忘れてはならないのは、データを読み込んだら問いかけを行うことです。
+
+- カボチャを買うのに最適な時期はいつですか？
+- ミニカボチャ1ケースの価格はどのくらいでしょうか？
+- 半ブッシェルのバスケットで買うべきか、1 1/9ブッシェルの箱で買うべきか。
+
+データを掘り下げていきましょう。
+
+前回のレッスンでは、Pandasのデータフレームを作成し、元のデータセットの一部を入力して、ブッシェル単位の価格を標準化しました。しかし、この方法では、約400のデータポイントしか集めることができず、しかもそれは秋の期間のものでした。
+
+このレッスンに付属するノートブックで、あらかじめ読み込んでおいたデータを見てみましょう。データが事前に読み込まれ、月毎のデータが散布図として表示されています。データをもっと綺麗にすることで、データの性質をもう少し知ることができるかもしれません。
+
+## 線形回帰
+
+レッスン1で学んだように、線形回帰の演習では、以下のような線を描けるようになることが目標です。
+
+- **変数間の関係を示す。**
+- **予測を行う。** 新しいデータポイントが、その線のどこに位置するかを正確に予測することができる。
+ 
+このような線を描くことは、**最小二乗回帰 (Least-Squares Regression)** の典型的な例です。「最小二乗」という言葉は、回帰線を囲むすべてのデータポイントとの距離が二乗され、その後加算されることを意味しています。理想的には、最終的な合計ができるだけ小さくなるようにします。これはエラーの数、つまり「最小二乗」の値を小さくするためです。
+
+これは、すべてのデータポイントからの累積距離が最小となる直線をモデル化したいためです。また、方向ではなく大きさに注目しているので、足す前に項を二乗します。
+
+> **🧮 Show me the math** 
+> 
+> この線は、_line of best fit_ と呼ばれ、[方程式](https://en.wikipedia.org/wiki/Simple_linear_regression) で表すことができます。
+> 
+> ```
+> Y = a + bX
+> ```
+>
+> `X`は「説明変数」です。`Y`は「目的変数」です。`a`は切片で`b`は直線の傾きを表します。`X=0`のとき、`Y`の値は切片`a`となります。
+>
+>![傾きの計算](../images/slope.png)
+>
+> はじめに、傾き`b`を計算してみます。[Jen Looper](https://twitter.com/jenlooper) によるインフォグラフィック。
+>
+> カボチャのデータに関する最初の質問である、「月毎のブッシェル単位でのカボチャの価格を予測してください」で言い換えてみると、`X`は価格を、`Y`は販売された月を表しています。
+>
+>![方程式の計算](../images/calculation.png)
+>
+> Yの値を計算してみましょう。$4前後払っているなら、4月に違いありません！[Jen Looper](https://twitter.com/jenlooper) によるインフォグラフィック。
+>
+> 直線を計算する数学は、直線の傾きを示す必要がありますが、これは切片、つまり「X = 0」のときに「Y」がどこに位置するかにも依存します。
+>
+> これらの値の計算方法は、[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) というサイトで見ることができます。また、[this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) では、値が線にどのような影響を与えるかを見ることができます。
+
+## 相関関係
+
+もう一つの理解すべき用語は、与えられたXとYの変数間の**相関係数 (Correlation Coefficient)** です。散布図を使えば、この係数をすぐに可視化することができます。データポイントがきれいな直線上に散らばっているプロットは、高い相関を持っていますが、データポイントがXとYの間のあらゆる場所に散らばっているプロットは、低い相関を持っています。
+
+良い線形回帰モデルとは、最小二乗法によって求めた回帰線が高い相関係数 (0よりも1に近い)を持つものです。
+
+✅ このレッスンのノートを開いて、「都市と価格」の散布図を見てみましょう。散布図の視覚的な解釈によると、カボチャの販売に関する「都市」と「価格」の関連データは、相関性が高いように見えますか、それとも低いように見えますか？
+
+## 回帰に用いるデータの準備
+
+この演習の背景にある数学を理解したので、回帰モデルを作成して、どのパッケージのカボチャの価格が最も高いかを予測できるかどうかを確認してください。休日のパンプキンパッチ用にパンプキンを購入する人は、パッチ用のパンプキンパッケージの購入を最適化するために、この情報を必要とするかもしれません。
+
+ここではScikit-learnを使用するので、手作業で行う必要はありません。レッスンノートのメインのデータ処理ブロックに、Scikit-learnのライブラリを追加して、すべての文字列データを自動的に数字に変換します。
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+new_pumpkinsデータフレームを見ると、すべての文字列が数値になっているのがわかります。これにより、人が読むのは難しくなりましたが、Scikit-learnにとってはとても分かりやすくなりました。
+これで、回帰に最も適したデータについて、（散布図を見ただけではなく）より高度な判断ができるようになりました。
+
+良い予測モデルを構築するために、データの2点間に良い相関関係を見つけようとします。その結果、「都市」と「価格」の間には弱い相関関係しかないことがわかりました。
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+しかし、パッケージと価格の間にはもう少し強い相関関係があります。これは理にかなっていると思いますか？通常、箱が大きければ大きいほど、価格は高くなります。
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+このデータに対する良い質問は、次のようになります。「あるカボチャのパッケージの価格はどのくらいになるか？」
+
+この回帰モデルを構築してみましょう！
+
+## 線形モデルの構築
+
+モデルを構築する前に、もう一度データの整理をしてみましょう。NULLデータを削除し、データがどのように見えるかをもう一度確認します。
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+そして、この最小セットから新しいデータフレームを作成し、それを出力します。
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. これで、XとYの座標データを割り当てることができます。
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+✅ ここでは何をしていますか？ Pythonの[スライス記法](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) を使って、`X`と`y`の配列を作成しています。
+
+2. 次に、回帰モデル構築のためのルーチンを開始します。
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+
+   pred = lin_reg.predict(X_test)
+
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   相関関係があまり良くないので、生成されたモデルもあまり正確ではありません。
+   
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+3. 今回の過程で描かれた線を可視化します。
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+
+   plt.show()
+   ```
+   ![パッケージと価格の関係を表す散布図](../images/linear.png)
+
+4. 架空の値に対してモデルをテストする。
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+   
+   この架空の値に対して、以下の価格が返されます。
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+回帰の線が正しく引かれていれば、その数字は理にかなっています。
+
+🎃 おめでとうございます！数種類のカボチャの価格を予測するモデルを作成しました。休日のパンプキンパッチは美しいものになるでしょう。でも、もっと良いモデルを作れるかもしれません。
+
+## 多項式回帰
+
+線形回帰のもう一つのタイプは、多項式回帰です。時には変数の間に直線的な関係 (カボチャの量が多いほど、価格は高くなる)があることもありますが、これらの関係は、平面や直線としてプロットできないこともあります。
+
+✅ 多項式回帰を使うことができる、[いくつかの例](https://online.stat.psu.edu/stat501/lesson/9/9.8) を示します。
+
+先ほどの散布図の「品種」と「価格」の関係をもう一度見てみましょう。この散布図は、必ずしも直線で分析しなければならないように見えますか？そうではないかもしれません。このような場合は、多項式回帰を試してみましょう。
+
+✅ 多項式とは、1つ以上の変数と係数で構成される数学的表現である。
+
+多項式回帰では、非線形データをよりよく適合させるために曲線を作成します。
+
+1. 元のカボチャのデータの一部を入力したデータフレームを作成してみましょう。
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+   poly_pumpkins
+   ```
+
+データフレーム内のデータ間の相関関係を視覚化するには、「coolwarm」チャートで表示するのが良いでしょう。
+
+2. `Background_gradient()` メソッドの引数に `coolwarm` を指定して使用します。
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+   
+　　このコードはヒートマップを作成します。
+   ![データの相関関係を示すヒートマップ](../images/heatmap.png)
+
+このチャートを見ると、「パッケージ」と「価格」の間に正の相関関係があることが視覚化されています。つまり、前回のモデルよりも多少良いモデルを作ることができるはずです。
+
+### パイプラインの作成
+
+Scikit-learnには、多項式回帰モデルを構築するための便利なAPIである`make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline) が用意されています。「パイプライン」は推定量の連鎖で作成されます。今回の場合、パイプラインには多項式の特徴量、非線形の経路を形成する予測値が含まれます。
+
+1. X列とy列を作ります。
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. `make_pipeline()` メソッドを呼び出してパイプラインを作成します。
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   pipeline.fit(np.array(X_train), y_train)
+
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### 系列の作成
+
+この時点で、パイプラインが系列を作成できるように、ソートされたデータで新しいデータフレームを作成する必要があります。
+
+以下のコードを追加します。
+
+   ```python
+   df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+   df.sort_values(by='x',inplace = True)
+   points = pd.DataFrame(df).to_numpy()
+
+   plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   plt.scatter(X,y, color="black")
+   plt.show()
+   ```
+
+`pd.DataFrame` を呼び出して新しいデータフレームを作成しました。次に`sort_values()` を呼び出して値をソートしました。最後に多項式のプロットを作成しました。
+
+![パッケージと価格の関係を示す多項式のプロット](../images/polynomial.png)
+
+よりデータにフィットした曲線を確認することができます。
+
+モデルの精度を確認してみましょう。
+
+   ```python
+   accuracy_score = pipeline.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   これで完成です！
+
+   ```output
+   Model Accuracy:  0.8537946517073784
+   ```
+
+いい感じです！価格を予測してみましょう。
+
+### 予測の実行
+
+新しい値を入力し、予測値を取得できますか？
+
+`predict()` メソッドを呼び出して、予測を行います。
+ 
+   ```python
+   pipeline.predict( np.array([ [2.75] ]) )
+   ```
+   以下の予測結果が得られます。
+
+   ```output
+   array([[46.34509342]])
+   ```
+
+プロットを見てみると、納得できそうです！そして、同じデータを見て、これが前のモデルよりも良いモデルであれば、より高価なカボチャのために予算を組む必要があります。
+
+🏆 お疲れ様でした！1つのレッスンで2つの回帰モデルを作成しました。回帰に関する最後のセクションでは、カテゴリーを決定するためのロジスティック回帰について学びます。
+
+---
+## 🚀チャレンジ
+
+このノートブックでいくつかの異なる変数をテストし、相関関係がモデルの精度にどのように影響するかを確認してみてください。
+
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+
+## レビュー & 自主学習
+
+このレッスンでは、線形回帰について学びました。回帰には他にも重要な種類があります。Stepwise、Ridge、Lasso、Elasticnetなどのテクニックをご覧ください。より詳しく学ぶには、[Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning) が良いでしょう。
+
+## 課題 
+
+[モデル構築](./assignment.ja.md)
diff --git a/2-Regression/3-Linear/translations/README.ko.md b/2-Regression/3-Linear/translations/README.ko.md
new file mode 100644
index 000000000..57ba3201d
--- /dev/null
+++ b/2-Regression/3-Linear/translations/README.ko.md
@@ -0,0 +1,338 @@
+# Scikit-learn을 사용한 regression 모델 만들기: regression 2가지 방식
+
+![Linear vs polynomial regression infographic](.././images/linear-polynomial.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+
+### 소개 
+
+지금까지 이 강의에서 사용할 호박 가격 데이터셋에서 모은 샘플 데이터로 regression이 무엇인지 찾아보았습니다. Matplotlib을 사용하여 시각화했습니다.
+
+이제 ML의 regression에 대하여 더 깊게 파고 들 준비가 되었습니다. 이 강의에서, 2가지 타입의 regression에 대해 배웁니다: 이 기술의 기반이 되는 수학의 일부와 함께, _basic linear regression_ 과 _polynomial regression_.
+
+
+> 이 커리큘럼 대부분에 걸쳐서, 수학에 대한 최소한의 지식을 가정하고, 다른 필드에서 온 학생들이 수학에 접근할 수 있도록 노력하므로, 이해를 돕기 위하여 노트, 🧮 callouts, 다이어그램과 기타 학습 도구를 찾아보세요.
+
+### 필요한 조건
+
+지금즈음 조사하고 있던 호박 데이터의 구조에 익숙해집니다. 이 강의의 _notebook.ipynb_ 파일에서 preloaded와 pre-cleaned된 것을 찾을 수 있습니다. 파일에서, 호박 가격은 새로운 데이터프레임에서 bushel per로 보여집니다. Visual Studio Code의 커널에서 이 노트북을 실행할 수 있는 지 확인합니다.
+
+### 준비하기
+
+참고하자면, 이러한 질문을 물어보기 위해서 이 데이터를 불러오고 있습니다.
+
+- 호박을 사기 좋은 시간은 언제인가요?
+- 작은 호박 케이스의 가격은 얼마인가요?
+- half-bushel 바구니 또는 1 1/9 bushel 박스로 사야 하나요?
+
+이 데이터로 계속 파봅시다.
+
+이전 강의에서, Pandas 데이터프레임을 만들고 원본 데이터셋의 일부를 채웠으며, bushel로 가격을 표준화했습니다. 그렇게 했지만, 가을에만 400개의 데이터 포인트를 모을 수 있었습니다.
+
+이 강의에 첨부된 notebook에서 미리 불러온 데이터를 봅시다. 데이터를 미리 불러오고 초기 scatterplot(산점도)이 월 데이터를 보여주도록 차트로 만듭니다. 더 정리하면 데이터의 특성에 대하여 조금 더 자세히 알 수 있습니다.
+
+## Linear regression 라인
+
+1 강의에서 배웠던 것처럼, linear regression 연습의 목표는 라인을 그릴 수 있어야 합니다:
+
+- **변수 관계 보이기**. 변수 사이 관게 보이기
+- **예상하기**. 새로운 데이터 포인트가 라인과 관련해서 어디에 있는지 정확하게 예측합니다.
+ 
+이런 타입의 선을 그리는 것은 **Least-Squares Regression** 의 전형적입니다. 'least-squares'이라는 말은 regression 라인을 두른 모든 데이터 포인트가 제곱된 다음에 더하는 것을 의미합니다. 이상적으로, 적은 수의 오류, 또는 `least-squares`를 원하기 때문에, 최종 합계는 가능한 작아야 합니다.
+
+모든 데이터 포인트에서 누적 거리가 가장 짧은 라인을 모델링하기 원합니다. 방향보다 크기에 관심있어서 항을 더하기 전에 제곱합니다.
+
+> **🧮 Show me the math** 
+> 
+> _line of best fit_ 이라고 불리는 이 선은, [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression)으로 표현할 수 있습니다:
+> 
+> ```
+> Y = a + bX
+> ```
+>
+> `X` 는 '독립(설명) 변수'입니다. `Y`는 '종속 변수'입니다. 라인의 기울기는 `b`이고 `a`는 y-절편이며, `X = 0`일 떄 `Y`의 값을 나타냅니다.
+>
+>![calculate the slope](../images/slope.png)
+>
+> 우선, 기울기 `b`를 구합니다. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+>
+> 즉, 호박의 원본 질문을 참조해봅니다 : "predict the price of a pumpkin per bushel by month", `X`는 가격을 나타내고 `Y`는 판매한 달을 나타냅니다.
+>
+>![complete the equation](../images/calculation.png)
+>
+> Y의 값을 구합니다. 만약 4달러 정도 준다면, 4월만 가능합니다! Infographic by [Jen Looper](https://twitter.com/jenlooper)
+>
+> 라인을 구하는 수학은 절편, 또는 `X = 0`일 때 `Y`가 위치한 곳에 따라, 달라지는 라인의 기울기를 볼 수 있어야 합니다.
+>
+> [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) 웹사이트에서 값을 구하는 방식을 지켜볼 수 있습니다. 그리고 [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html)를 찾아가서 숫자 값이 라인에 어떤 영향을 주는 지 볼 수 있습니다.
+
+## 상관 관계
+
+이해할 하나의 용어는 주어진 X와 Y 변수 사이의 **Correlation Coefficient**입니다. scatterplot(산점도)를 사용해서, 이 계수를 빠르게 시각화할 수 있습니다. 데이터 포인트를 깔끔한 라인으로 흩어 둔 plot은 상관 관계가 높지만, 데이터 포인트가  X와 Y 사이 어디에나 흩어진 plot은 상관 관계가 낮습니다.
+
+좋은 linear regression 모델은 regression 라인과 같이 Least-Squares Regression 방식을 사용하여 (0 보다 1에 가까운) 높은 상관 계수를 가집니다.
+
+✅ 이 강위에서 같이 주는 노트북을 실행하고 City to Price의 scatterplot (산점도)를 봅니다. scatterplot (산점도)의 시각적 해석에 따르면, 호박 판매를 도시와 가격에 연관지으면 데이터가 높거나 낮은 상관 관계를 보이는 것 같나요?
+
+
+## Regression를 위한 데이터 준비하기
+
+지금부터 연습에 기반한 수학을 이해했으므로, Regression 모델을 만들어서 호박 가격이 괜찮은 호박 패키지를 예측할 수 있는 지 봅니다. holiday pumpkin patch를 위해서 호박을 사는 사람은 이 정보로 패치용 호박 패키지를 최적으로 사고 싶습니다.
+
+Scikit-learn을 사용할 예정이기 때문에, (할 수 있지만) 손으로 직접 할 필요가 없습니다. 수업 노트북의 주 데이터-처리 블록에서, Scikit-learn의 라이브러리를 추가하여 모든 문자열 데이터를 숫자로 자동 변환합니다:
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+new_pumpkins 데이터프레임을 보면, 모든 문자열은 이제 숫자로 보입니다. 직접 읽기는 힘들지만 Scikit-learn은 더욱 더 이해하기 쉽습니다!
+지금부터 regression에 잘 맞는 데이터에 대하여 (scatterplot(산점도) 지켜보는 것 말고도) 교육적인 결정을 할 수 있습니다.
+
+잠재적으로 좋은 예측 모델을 만드려면 데이터의 두 포인트 사이 좋은 상관 관계를 찾아야 합니다. 도시와 가격 사이에는 약한 상관 관계만 있다는, 사실이 밝혀졌습니다:
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+하지만 패키지와 가격 사이에는 조금 더 큰 상관 관계가 있습니다. 이해가 되나요? 일반적으로, 농산물 박스가 클수록, 가격도 높습니다.
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+데이터에 물어보기 좋은 질문은 이렇습니다: 'What price can I expect of a given pumpkin package?'
+
+regression 모델을 만들어봅니다
+
+## linear 모델 만들기
+
+모델을 만들기 전에, 데이터를 다시 정리합니다. Null 데이터를 드랍하고 데이터가 어떻게 보이는 지 다시 확인합니다.
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+그러면, 최소 셋에서 새로운 데이터프레임을 만들고 출력합니다:
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. 이제 X와 Y 좌표 데이터를 대입합니다:
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+✅ 어떤 일이 생기나요? [Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295)으로 `X` 와 `y`를 채울 배열을 생성합니다.
+
+2. 다음으로, regression model-building 루틴을 시작합니다:
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+
+   pred = lin_reg.predict(X_test)
+
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   상관 관계가 좋지 못해서, 만들어진 모델은 딱히 정확하지 않습니다.
+
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+3. 프로세스에서 그려진 라인을 시각화할 수 있습니다:
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+
+   plt.show()
+   ```
+   ![A scatterplot showing package to price relationship](.././images/linear.png)
+
+4. 가상의 Variety에 대하여 모델을 테스트합니다:
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+   
+   전설적 Variety의 반품된 가격입니다:
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+regression 라인의 로직이 사실이라면, 숫자는 의미가 있습니다.
+
+🎃 축하드립니다. 방금 전에 몇 호박 종의 가격 예측하는 모델을 만들었습니다. holiday pumpkin patch는 아릅답습니다. 하지만 더 좋은 모델을 만들 수 있습니다!
+
+## Polynomial regression
+
+linear regression의 또 다른 타입은 polynomial regression 입니다. 때때로 변수 사이 linear 관계가 있지만 - 호박 볼륨이 클수록, 가격이 높아지는 - 이런 관계를 평면 또는 직선으로 그릴 수 없습니다.
+
+✅ polynomial regression을 사용할 수 있는 데이터의 [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8)입니다.
+
+이전 plot에서 다양성과 가격 사이 관계를 봅니다. scatterplot(산점도)이 반드시 직선으로 분석되어야 하는 것처럼 보이나요? 아마 아닐겁니다. 이 케이스에서, polynomial regression을 시도할 수 있습니다.
+
+✅ Polynomials는 하나 또는 더 많은 변수와 계수로 이루어 질 수 있는 수학적 표현식입니다.
+
+Polynomial regression은 nonlinear 데이터에 더 맞는 곡선을 만듭니다.
+
+1. 원본 호박 데이터의 세그먼트로 채워진 데이터프레임을 다시 만듭니다:
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+   poly_pumpkins
+   ```
+
+데이터프레임의 데이터 사이 상관 관계를 시각화하는 좋은 방식은 'coolwarm' 차트에 보여주는 것입니다:
+
+2. 인수 값으로 `coolwarm`을 `Background_gradient()` 메소드에 사용합니다:
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+   이 코드로 heatmap을 만듭니다:
+   ![A heatmap showing data correlation](.././images/heatmap.png)
+
+이 차트를 보고 있으면, 패키지와 가격 사이 좋은 상관 관계를 시각화할 수 있습니다. 그래서 이전의 모델보다 약간 좋게 만들 수 있어야 합니다.
+
+### 파이프라인 만들기
+
+Scikit-learn에는 polynomial regression 모델을 만들 때 도움을 받을 수 있는 API가 포함되어 있습니다 - the `make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline). 추정량의 체인인 'pipeline'이 만들어집니다. 이 케이스는, 파이프라인에 polynomial features, 또는 nonlinear 경로를 만들 예측이 포함됩니다.
+
+1. X 와 y 열을 작성합니다:
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. `make_pipeline()` 메소드를 불러서 파이프라인을 만듭니다:
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   pipeline.fit(np.array(X_train), y_train)
+
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### 시퀀스 만들기
+
+이 지점에서, 파이프라인이 시퀀스를 만들 수 있도록 _sorted_ 데이터로 새로운 데이터프레임을 만들 필요가 있습니다.
+
+해당 코드를 추가합니다:
+
+   ```python
+   df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+   df.sort_values(by='x',inplace = True)
+   points = pd.DataFrame(df).to_numpy()
+
+   plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   plt.scatter(X,y, color="black")
+   plt.show()
+   ```
+
+`pd.DataFrame`을 불러서 새로운 데이터프레임을 만듭니다. 그러면 `sort_values()`도 불러서 값을 정렬합니다. 마지막으로 polynomial plot을 만듭니다:
+
+![A polynomial plot showing package to price relationship](.././images/polynomial.png)
+
+데이터에 더 맞는 곡선을 볼 수 있습니다.
+
+모델의 정확도를 확인합시다:
+
+   ```python
+   accuracy_score = pipeline.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   그리고 짠!
+
+   ```output
+   Model Accuracy:  0.8537946517073784
+   ```
+
+더 좋습니다! 가격을 예측해봅시다:
+
+### 예측하기
+
+새로운 값을 넣고 예측할 수 있나요?
+
+`predict()`를 불러서 예측합니다:
+ 
+   ```python
+   pipeline.predict( np.array([ [2.75] ]) )
+   ```
+
+   이렇게 예측됩니다:
+
+   ```output
+   array([[46.34509342]])
+   ```
+
+주어진 plot에서, 의미가 있습니다! 그리고, 이전보다 모델이 더 좋아졌다면, 같은 데이터를 보고, 더 비싼 호박을 위한 예산이 필요합니다!
+
+🏆 좋습니다! 이 강의에서 두가지 regression 모델을 만들었습니다. regression의 마지막 섹션에서, 카테고리를 결정하기 위한 logistic regression에 대하여 배우게 됩니다.
+
+---
+## 🚀 도전
+
+노트북에서 다른 변수를 테스트하면서 상관 관계가 모델 정확도에 어떻게 대응되는 지 봅니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+
+## 검토 & 자기주도 학습
+
+이 강의에서 Linear Regression에 대하여 배웠습니다. Regression의 다른 중요 타입이 있습니다. Stepwise, Ridge, Lasso 와 Elasticnet 기술에 대하여 읽어봅니다. 더 배우기 위해서 공부하기 좋은 코스는 [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)입니다.
+
+## 과제 
+
+[Build a Model](../assignment.md)
\ No newline at end of file
diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index 55c4d2939..bffa0e22d 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -1,19 +1,21 @@
-# 使用Scikit-learn构建回归模型：两种方式的回归
+# 使用 Scikit-learn 构建回归模型：两种方式的回归
 
 ![线性与多项式回归信息图](../images/linear-polynomial.png)
-> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
-### 介绍 
+> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-到目前为止，你已经通过从我们将在本课程中使用的南瓜定价数据集收集的样本数据探索了什么是回归。你还使用Matplotlib对其进行了可视化。
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 
-现在你已准备好深入研究ML的回归。 在本课中，你将详细了解两种类型的回归：_基本线性回归_和_多项式回归_，以及这些技术背后的一些数学知识。 
+### 介绍
+
+到目前为止，你已经通过从我们将在本课程中使用的南瓜定价数据集收集的样本数据探索了什么是回归。你还使用 Matplotlib 对其进行了可视化。
+
+现在你已准备好深入研究 ML 的回归。 在本课中，你将详细了解两种类型的回归：_基本线性回归_ 和 _多项式回归_，以及这些技术背后的一些数学知识。
 
 > 在整个课程中，我们假设数学知识最少，并试图让来自其他领域的学生也能接触到它，因此请使用笔记、🧮标注、图表和其他学习工具以帮助理解。
 
 ### 前提
 
-你现在应该熟悉我们正在检查的南瓜数据的结构。你可以在本课的_notebook.ipynb_文件中找到它。 在这个文件中，南瓜的价格显示在一个新的dataframe 中。确保可以在Visual Studio Code代码的内核中运行这些notebooks。
+你现在应该熟悉我们正在检查的南瓜数据的结构。你可以在本课的 _notebook.ipynb_ 文件中找到它。 在这个文件中，南瓜的价格显示在一个新的 dataframe 中。确保可以在 Visual Studio Code 代码的内核中运行这些 notebooks。
 
 ### 准备
 
@@ -21,61 +23,62 @@
 
 - 什么时候买南瓜最好？
 - 一箱微型南瓜的价格是多少？
-- 我应该买半蒲式耳还是1 1/9蒲式耳？
+- 我应该买半蒲式耳还是 1 1/9 蒲式耳？
+
 让我们继续深入研究这些数据。
 
-在上一课中，你创建了一个Pandas dataframe并用原始数据集的一部分填充它，按蒲式耳标准化定价。但是，通过这样做，你只能收集大约400个数据点，而且只能收集秋季月份的数据。
+在上一课中，你创建了一个 Pandas dataframe 并用原始数据集的一部分填充它，按蒲式耳标准化定价。但是，通过这样做，你只能收集大约 400 个数据点，而且只能收集秋季月份的数据。
 
-看看我们在本课随附的notebook中预加载的数据。数据已预加载，并绘制了初始散点图以显示月份数据。也许我们可以通过更多地清理数据来获得更多关于数据性质的细节。 
+看看我们在本课随附的 notebook 中预加载的数据。数据已预加载，并绘制了初始散点图以显示月份数据。也许我们可以通过更多地清理数据来获得更多关于数据性质的细节。
 
 ## 线性回归线
 
-正如你在第1课中学到的，线性回归练习的目标是能够绘制一条线以便：
+正如你在第 1 课中学到的，线性回归练习的目标是能够绘制一条线以便：
 
 - **显示变量关系**。 显示变量之间的关系
 - **作出预测**。 准确预测新数据点与该线的关系。
- 
+
 绘制这种类型的线是**最小二乘回归**的典型做法。术语“最小二乘法”意味着将回归线周围的所有数据点平方，然后相加。理想情况下，最终和尽可能小，因为我们希望错误数量较少，或“最小二乘法”。
 
 我们这样做是因为我们想要对一条与所有数据点的累积距离最小的线进行建模。我们还在添加它们之前对这些项进行平方，因为我们关心的是它的大小而不是它的方向。
 
-> **🧮 数学知识** 
-> 
-> 这条线称为_最佳拟合线_，可以用[一个等式](https://en.wikipedia.org/wiki/Simple_linear_regression)表示：
-> 
+> **🧮 数学知识**
+>
+> 这条线称为 _最佳拟合线_，可以用[一个等式](https://en.wikipedia.org/wiki/Simple_linear_regression)表示：
+>
 > ```
 > Y = a + bX
 > ```
 >
-> `X`是“解释变量”。`Y`是“因变量”。直线的斜率是`b`，`a`是y轴截距，指的是`X = 0`时`Y`的值。
+> `X` 是“解释变量”。`Y` 是“因变量”。直线的斜率是 `b`，`a` 是 y 轴截距，指的是 `X = 0` 时 `Y` 的值。
 >
 >![计算斜率](../images/slope.png)
 >
-> 首先，计算斜率`b`。作者[Jen Looper](https://twitter.com/jenlooper)
+> 首先，计算斜率 `b`。作者 [Jen Looper](https://twitter.com/jenlooper)
 >
-> 换句话说，参考我们的南瓜数据的原始问题：“按月预测每蒲式耳南瓜的价格”，`X`指的是价格，`Y`指的是销售月份。
+> 换句话说，参考我们的南瓜数据的原始问题：“按月预测每蒲式耳南瓜的价格”，`X` 指的是价格，`Y` 指的是销售月份。
 >
->![完成等式](../images/calculation.png)
+> ![完成等式](../images/calculation.png)
 >
-> 计算Y的值。如果你支付大约4美元，那一定是四月！作者[Jen Looper](https://twitter.com/jenlooper)
+> 计算 Y 的值。如果你支付大约 4 美元，那一定是四月！作者 [Jen Looper](https://twitter.com/jenlooper)
 >
-> 计算直线的数学必须证明直线的斜率，这也取决于截距，或者当`X = 0`时`Y`所在的位置。
+> 计算直线的数学必须证明直线的斜率，这也取决于截距，或者当 `X = 0` 时 `Y` 所在的位置。
 >
-> 你可以在[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html)网站上观察这些值的计算方法。另请访问[这个最小二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影响直线。
+> 你可以在 [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) 网站上观察这些值的计算方法。另请访问[这个最小二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影响直线。
 
 ## 相关性
 
-另一个需要理解的术语是给定X和Y变量之间的**相关系数**。使用散点图，你可以快速可视化该系数。数据点散布在一条直线上的图具有高相关性，但数据点散布在X和Y之间的图具有低相关性。
+另一个需要理解的术语是给定 X 和 Y 变量之间的**相关系数**。使用散点图，你可以快速可视化该系数。数据点散布在一条直线上的图具有高相关性，但数据点散布在 X 和 Y 之间的图具有低相关性。
 
-一个好的线性回归模型将是一个用最小二乘回归法与直线回归得到的高（更接近于1）相关系数的模型。
+一个好的线性回归模型将是一个用最小二乘回归法与直线回归得到的高（更接近于 1）相关系数的模型。
 
-✅ 运行本课随附的notebook并查看City to Price散点图。根据你对散点图的视觉解释，将南瓜销售的城市与价格相关联的数据似乎具有高相关性或低相关性？
+✅ 运行本课随附的 notebook 并查看 City to Price 散点图。根据你对散点图的视觉解释，将南瓜销售的城市与价格相关联的数据似乎具有高相关性或低相关性？
 
 ## 为回归准备数据
 
 现在你已经了解了本练习背后的数学原理，可以创建一个回归模型，看看你是否可以预测哪个南瓜包装的南瓜价格最优惠。为节日购买南瓜的人可能希望此信息能够优化他们如何购买南瓜包装。
 
-由于你将使用Scikit-learn，因此没有理由手动执行此操作（尽管你可以！）。在课程notebook的主要数据处理块中，从Scikit-learn添加一个库以自动将所有字符串数据转换为数字：
+由于你将使用 Scikit-learn，因此没有理由手动执行此操作（尽管你可以！）。在课程 notebook 的主要数据处理块中，从 Scikit-learn 添加一个库以自动将所有字符串数据转换为数字：
 
 ```python
 from sklearn.preprocessing import LabelEncoder
@@ -83,7 +86,7 @@ from sklearn.preprocessing import LabelEncoder
 new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
 ```
 
-如果你现在查看new_pumpkins dataframe，你会看到所有字符串现在都是数字。这让你更难阅读，但对Scikit-learn来说更容易理解！
+如果你现在查看 new_pumpkins dataframe，你会看到所有字符串现在都是数字。这让你更难阅读，但对 Scikit-learn 来说更容易理解！
 
 现在，你可以对最适合回归的数据做出更有根据的决策（不仅仅是基于观察散点图）。
 
@@ -103,7 +106,7 @@ print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
 
 对这些数据提出的一个很好的问题是：“我可以期望给定的南瓜包装的价格是多少？”
 
-让我们建立这个回归模型 
+让我们建立这个回归模型
 
 ## 建立线性模型
 
@@ -114,7 +117,7 @@ new_pumpkins.dropna(inplace=True)
 new_pumpkins.info()
 ```
 
-然后，从这个最小集合创建一个新的dataframe并将其打印出来：
+然后，从这个最小集合创建一个新的 dataframe 并将其打印出来：
 
 ```python
 new_columns = ['Package', 'Price']
@@ -139,13 +142,14 @@ lin_pumpkins
 415 rows × 2 columns
 ```
 
-1. 现在你可以分配X和y坐标数据：
+1. 现在你可以分配 X 和 y 坐标数据：
 
    ```python
    X = lin_pumpkins.values[:, :1]
    y = lin_pumpkins.values[:, 1:2]
    ```
-✅ 这里发生了什么？你正在使用[Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295)来创建数组来填充`X`和`y`。
+
+✅ 这里发生了什么？你正在使用 [Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) 来创建数组来填充 `X` 和 `y`。
 
 2. 接下来，开始回归模型构建例程：
 
@@ -181,6 +185,7 @@ lin_pumpkins
 
    plt.show()
    ```
+
    ![散点图显示包装与价格的关系](../images/linear.png)
 
 4. 针对假设的品种测试模型：
@@ -188,7 +193,7 @@ lin_pumpkins
    ```python
    lin_reg.predict( np.array([ [2.75] ]) )
    ```
-   
+
    这个神话般的品种的价格是：
 
    ```output
@@ -211,7 +216,8 @@ lin_pumpkins
 
 多项式回归创建一条曲线以更好地拟合非线性数据。
 
-1. 让我们重新创建一个填充了原始南瓜数据片段的dataframe：
+1. 让我们重新创建一个填充了原始南瓜数据片段的 dataframe：
+
    ```python
    new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
    poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
@@ -219,31 +225,32 @@ lin_pumpkins
    poly_pumpkins
    ```
 
-可视化dataframe中数据之间相关性的一种好方法是将其显示在“coolwarm”图表中：
+可视化 dataframe 中数据之间相关性的一种好方法是将其显示在“coolwarm”图表中：
 
-2. 使用`Background_gradient()`方法和`coolwarm`作为其参数值：
+2. 使用 `Background_gradient()` 方法和 `coolwarm` 作为其参数值：
 
    ```python
    corr = poly_pumpkins.corr()
    corr.style.background_gradient(cmap='coolwarm')
    ```
+
    这段代码创建了一个热图:
    ![显示数据相关性的热图](../images/heatmap.png)
 
-查看此图表，你可以直观地看到Package和Price之间的良好相关性。所以你应该能够创建一个比上一个更好的模型。
+查看此图表，你可以直观地看到 Package 和 Price 之间的良好相关性。所以你应该能够创建一个比上一个更好的模型。
 
 ### 创建管道
 
-Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline)。 创建了一个“管道”，它是一个估计器链。 在这种情况下，管道包括多项式特征或形成非线性路径的预测。
+Scikit-learn 包含一个用于构建多项式回归模型的有用 API - `make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline)。 创建了一个“管道”，它是一个估计器链。在这种情况下，管道包括多项式特征或形成非线性路径的预测。
 
-1. 构建X和y列：
+1. 构建 X 和 y 列：
 
    ```python
    X=poly_pumpkins.iloc[:,3:4].values
    y=poly_pumpkins.iloc[:,4:5].values
    ```
 
-2. 通过调用`make_pipeline()`方法创建管道：
+2. 通过调用 `make_pipeline()` 方法创建管道：
 
    ```python
    from sklearn.preprocessing import PolynomialFeatures
@@ -260,7 +267,7 @@ Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pi
 
 ### 创建序列
 
-此时，你需要使用_排序好的_数据创建一个新的dataframe ，以便管道可以创建序列。
+此时，你需要使用_排序好的_数据创建一个新的 dataframe ，以便管道可以创建序列。
 
 添加以下代码：
 
@@ -276,7 +283,7 @@ Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pi
    plt.show()
    ```
 
-你通过调用`pd.DataFrame`创建了一个新的dataframe。然后通过调用`sort_values()`对值进行排序。最后你创建了一个多项式图：
+你通过调用 `pd.DataFrame` 创建了一个新的 dataframe。然后通过调用 `sort_values()` 对值进行排序。最后你创建了一个多项式图：
 
 ![显示包装与价格关系的多项式图](../images/polynomial.png)
 
@@ -301,11 +308,12 @@ Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pi
 
 我们可以输入一个新值并得到一个预测吗？
 
-调用`predict()`进行预测：
- 
+调用 `predict()` 进行预测：
+
    ```python
    pipeline.predict( np.array([ [2.75] ]) )
    ```
+
    你会得到这样的预测：
 
    ```output
@@ -314,19 +322,20 @@ Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pi
 
 参照图像，这确实有道理！而且，如果这是一个比前一个更好的模型，看同样的数据，你需要为这些更昂贵的南瓜做好预算！
 
-🏆 干得不错！你在一节课中创建了两个回归模型。在回归的最后一节中，你将了解逻辑回归以确定类别。 
+🏆 干得不错！你在一节课中创建了两个回归模型。在回归的最后一节中，你将了解逻辑回归以确定类别。
 
 ---
+
 ## 🚀挑战
 
-在此notebook中测试几个不同的变量，以查看相关性与模型准确性的对应关系。
+在此 notebook 中测试几个不同的变量，以查看相关性与模型准确性的对应关系。
 
-## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## 复习与自学
 
-在本课中，我们学习了线性回归。还有其他重要的回归类型。了解Stepwise、Ridge、Lasso和Elasticnet技术。学习更多信息的好课程是[斯坦福统计学习课程](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+在本课中，我们学习了线性回归。还有其他重要的回归类型。了解 Stepwise、Ridge、Lasso 和 Elasticnet 技术。学习更多信息的好课程是 [斯坦福统计学习课程](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
 
-## 任务 
+## 任务
 
-[构建模型](../assignment.md)
+[构建模型](./assignment.zh-cn.md)
diff --git a/2-Regression/3-Linear/translations/assignment.it.md b/2-Regression/3-Linear/translations/assignment.it.md
new file mode 100644
index 000000000..e5aaaa77e
--- /dev/null
+++ b/2-Regression/3-Linear/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Creare un Modello di Regressione
+
+## Istruzioni
+
+In questa lezione è stato mostrato come costruire un modello utilizzando sia la Regressione Lineare che Polinomiale. Usando questa conoscenza, trovare un insieme di dati o utilizzare uno degli insiemi integrati di Scikit-Learn per costruire un modello nuovo. Spiegare nel proprio notebook perché si è scelto una determinata tecnica e dimostrare la precisione del modello. Se non è accurato, spiegare perché.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
+|          | presenta un notebook completo con una soluzione ben documentata | La soluzione è incompleta | La soluzione è difettosa o contiene bug |
diff --git a/2-Regression/3-Linear/translations/assignment.ja.md b/2-Regression/3-Linear/translations/assignment.ja.md
new file mode 100644
index 000000000..d0f8a4c50
--- /dev/null
+++ b/2-Regression/3-Linear/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 回帰モデルの作成
+
+## 課題の指示
+
+このレッスンでは、線形回帰と多項式回帰の両方を使ってモデルを構築する方法を紹介しました。この知識をもとに、自分でデータセットを探すか、Scikit-learnのビルトインセットの1つを使用して、新しいモデルを構築してください。手法を選んだ理由をノートブックに書き、モデルの精度を示してください。精度が十分でない場合は、その理由も説明してください。
+
+## ルーブリック
+
+| 指標 | 模範的                                                    | 適切                   | 要改善 |
+| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
+|          | ドキュメント化されたソリューションを含む完全なノートブックを提示する。 | 解決策が不完全である。 | 解決策に欠陥またはバグがある。 |
diff --git a/2-Regression/3-Linear/translations/assignment.zh-cn.md b/2-Regression/3-Linear/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..e9c476c36
--- /dev/null
+++ b/2-Regression/3-Linear/translations/assignment.zh-cn.md
@@ -0,0 +1,12 @@
+# 创建自己的回归模型
+
+## 说明
+
+在这节课中你学到了如何用线性回归和多项式回归建立一个模型。利用这些只是，找到一个你感兴趣的数据集或者是 Scikit-learn 内置的数据集来建立一个全新的模型。用你的 notebook 来解释为什么用了这种技术来对这个数据集进行建模，并且证明出你的模型的准确度。如果它没你想象中准确，请思考一下并解释一下原因。
+
+## 评判标准
+
+| 标准 | 优秀                                                    | 中规中矩                   | 仍需努力               |
+| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
+|          | 提交了一个完整的 notebook 工程文件，其中包含了解集，并且可读性良好 | 不完整的解集 | 解集是有缺陷或者有错误的 |
+
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index a4488c11e..9ff52164e 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -2,7 +2,9 @@
 
 ![Logistic vs. linear regression infographic](./images/logistic-linear.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+
+> ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb)
 
 ## Introduction
 
@@ -140,7 +142,7 @@ Now that we have an idea of the relationship between the binary categories of co
 
 > **🧮 Show Me The Math**
 >
-> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
+> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:
 >
 > ![logistic function](images/sigmoid.png)
 >
@@ -206,7 +208,7 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo
 
 > 🎓 A '[confusion matrix](https://wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
 
-1. To use a confusion metrics, call `confusin_matrix()`:
+1. To use a confusion metrics, call `confusion_matrix()`:
 
     ```python
     from sklearn.metrics import confusion_matrix
@@ -220,26 +222,35 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo
            [ 33,   0]])
     ```
 
-What's going on here? Let's say our model is asked to classify items between two binary categories, category 'pumpkin' and category 'not-a-pumpkin'.
+In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns (axis 1) are predicted labels.
 
-- If your model predicts something as a pumpkin and it belongs to category 'pumpkin' in reality we call it a true positive, shown by the top left number. 
-- If your model predicts something as not a pumpkin and it belongs to category 'pumpkin' in reality we call it a false positive, shown by the top right number. 
-- If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number. 
-- If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number.
+|       |   0   |   1   |
+| :---: | :---: | :---: |
+|   0   |  TN   |  FP   |
+|   1   |  FN   |  TP   |
 
-![Confusion Matrix](images/confusion-matrix.png)
+What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'.
 
-> Infographic by [Jen Looper](https://twitter.com/jenlooper)
+- If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a true negative, shown by the top left number.
+- If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a false negative, shown by the bottom left number. 
+- If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a false positive, shown by the top right number. 
+- If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a true positive, shown by the bottom right number.
 
 As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
 
-✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true positives but also several false negatives. 
+How does the confusion matrix relate to precision and recall? Remember, the classification report printed above showed precision (0.83) and recall (0.98).
+
+Precision = tp / (tp + fp) = 162 / (162 + 33) = 0.8307692307692308
+
+Recall = tp / (tp + fn) = 162 / (162 + 4) = 0.9759036144578314
+
+✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true negatives but also several false negatives. 
 
 Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
 
-🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
+🎓 Precision: TP/(TP + FP) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
 
-🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
+🎓 Recall: TP/(TP + FN) The fraction of relevant instances that were retrieved, whether well-labeled or not
 
 🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
 
@@ -252,6 +263,7 @@ Let's revisit the terms we saw earlier with the help of the confusion matrix's m
 🎓 Weighted Avg: The calculation of the mean metrics for each label, taking label imbalance into account by weighting them by their support (the number of true instances for each label).
 
 ✅ Can you think which metric you should watch if you want your model to reduce the number of false negatives?
+
 ## Visualize the ROC curve of this model
 
 This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.
@@ -284,8 +296,9 @@ In future lessons on classifications, you will learn how to iterate to improve y
 ---
 ## 🚀Challenge
 
-There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets.
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
+
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/4-Logistic/images/r_learners_sm.jpeg b/2-Regression/4-Logistic/images/r_learners_sm.jpeg
new file mode 100644
index 000000000..ff8d2945d
Binary files /dev/null and b/2-Regression/4-Logistic/images/r_learners_sm.jpeg differ
diff --git a/2-Regression/4-Logistic/solution/Julia/README.md b/2-Regression/4-Logistic/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/2-Regression/4-Logistic/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
new file mode 100644
index 000000000..80be925dc
--- /dev/null
+++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -0,0 +1,751 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "Untitled10.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: logistic regression\n",
+        "<br>\n"
+      ],
+      "metadata": {
+        "id": "fVfEucLYkV9T"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Build a logistic regression model - Lesson 4\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/logistic-linear.png\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/logistic-linear.png){width=\"600\"}-->"
+      ],
+      "metadata": {
+        "id": "QizKKpzakfx2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**\n",
+        "\n",
+        "####  Introduction\n",
+        "\n",
+        "In this final lesson on Regression, one of the basic *classic* ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict `binary` `categories`. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not?\n",
+        "\n",
+        "In this lesson, you will learn:\n",
+        "\n",
+        "-   Techniques for logistic regression\n",
+        "\n",
+        "✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)\n",
+        "\n",
+        "#### **Prerequisite**\n",
+        "\n",
+        "Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.\n",
+        "\n",
+        "Let's build a logistic regression model to predict that, given some variables, *what color a given pumpkin is likely to be* (orange 🎃 or white 👻).\n",
+        "\n",
+        "> Why are we talking about binary classification in a lesson grouping about regression? Only for linguistic convenience, as logistic regression is [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), albeit a linear-based one. Learn about other ways to classify data in the next lesson group.\n",
+        "\n",
+        "For this lesson, we'll require the following packages:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.\n",
+        "\n",
+        "-   `ggbeeswarm`: The [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) provides methods to create beeswarm-style plots using ggplot2.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+        "\n",
+        "Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ],
+      "metadata": {
+        "id": "KPmut75XkmXY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+        "\n",
+        "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "dnIGNNttkx_O"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ** Define the question**\n",
+        "\n",
+        "For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.\n",
+        "\n",
+        "> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking!\n",
+        "\n",
+        "## **About logistic regression**\n",
+        "\n",
+        "Logistic regression differs from linear regression, which you learned about previously, in a few important ways.\n",
+        "\n",
+        "#### **Binary classification**\n",
+        "\n",
+        "Logistic regression does not offer the same features as linear regression. The former offers a prediction about a `binary category` (\"orange or not orange\") whereas the latter is capable of predicting `continual values`, for example given the origin of a pumpkin and the time of harvest, *how much its price will rise*.\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/pumpkin-classifier.png\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/pumpkin-classifier.png){width=\"600\"}-->"
+      ],
+      "metadata": {
+        "id": "ws-hP_SXk2O6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Other classifications**\n",
+        "\n",
+        "There are other types of logistic regression, including multinomial and ordinal:\n",
+        "\n",
+        "-   **Multinomial**, which involves having more than one category - \"Orange, White, and Striped\".\n",
+        "\n",
+        "-   **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/multinomial-ordinal.png\"\n",
+        "   width=\"700\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "<!--![Infographic by Dasani Madipalli](../../images/multinomial-ordinal.png){width=\"600\"}-->"
+      ],
+      "metadata": {
+        "id": "LkLN-ZgDlBEc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**It's still linear**\n",
+        "\n",
+        "Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not.\n",
+        "\n",
+        "#### **Variables DO NOT have to correlate**\n",
+        "\n",
+        "Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.\n",
+        "\n",
+        "#### **You need a lot of clean data**\n",
+        "\n",
+        "Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.\n",
+        "\n",
+        "✅ Think about the types of data that would lend themselves well to logistic regression\n"
+      ],
+      "metadata": {
+        "id": "D8_JoVZtlHUt"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Tidy the data\n",
+        "\n",
+        "Now, the fun begins! Let's start by importing the data, cleaning the data a bit, dropping rows containing missing values and selecting only some of the columns:"
+      ],
+      "metadata": {
+        "id": "LPj8Ib1AlIua"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the core tidyverse packages\n",
+        "library(tidyverse)\n",
+        "\n",
+        "# Import the data and clean column names\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+        "  clean_names()\n",
+        "\n",
+        "# Select desired columns\n",
+        "pumpkins_select <- pumpkins %>% \n",
+        "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+        "\n",
+        "# Drop rows containing missing values and encode color as factor (category)\n",
+        "pumpkins_select <- pumpkins_select %>% \n",
+        "  drop_na() %>% \n",
+        "  mutate(color = factor(color))\n",
+        "\n",
+        "# View the first few rows\n",
+        "pumpkins_select %>% \n",
+        "  slice_head(n = 5)\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Q8oKJ8PAlLM0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Sometimes, we may want some little more information on our data. We can have a look at the `data`, `its structure` and the `data type` of its features by using the [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) function as below:"
+      ],
+      "metadata": {
+        "id": "tKY5eN8alPNn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "pumpkins_select %>% \n",
+        "  glimpse()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "wDpatL1WlShu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Wow! Seems that all our columns are all of type *character*, further alluding that they are all categorical.\n",
+        "\n",
+        "Let's confirm that we will actually be doing a binary classification problem:"
+      ],
+      "metadata": {
+        "id": "QbdC2b0JlU2G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Subset distinct observations in outcome column\n",
+        "pumpkins_select %>% \n",
+        "  distinct(color)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Gys-Q18rlZpE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🥳🥳 That went down well!\n",
+        "\n",
+        "## 2. Explore the data\n",
+        "\n",
+        "The goal of data exploration is to try to understand the `relationships` between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. One way of doing this is by using data visualization.\n",
+        "\n",
+        "Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3-R.ipynb).\n",
+        "\n",
+        "Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.\n",
+        "\n",
+        "> Normally, recipes is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case it is **highly recommend** that you use a `workflow()` instead of manually estimating a recipe using prep and bake. We'll see all this in just a moment.\n",
+        ">\n",
+        "> However for now, we are using recipes + prep + bake to specify what steps should be applied to a data set in order to get it ready for data analysis and then extract the preprocessed data with the steps applied."
+      ],
+      "metadata": {
+        "id": "kn_20wSPldVH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Preprocess and extract data to allow some data analysis\n",
+        "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>% \n",
+        "  # Encode all columns to a set of integers\n",
+        "  step_integer(all_predictors(), zero_based = T) %>% \n",
+        "  prep() %>% \n",
+        "  bake(new_data = NULL)\n",
+        "\n",
+        "\n",
+        "# Display the first few rows of preprocessed data\n",
+        "baked_pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "syaCgFQ_lijg"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`."
+      ],
+      "metadata": {
+        "id": "RlkOZ_C5lldq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Pivot data to long format\n",
+        "baked_pumpkins_long <- baked_pumpkins %>% \n",
+        "  pivot_longer(!color, names_to = \"features\", values_to = \"values\")\n",
+        "\n",
+        "\n",
+        "# Print out restructured data\n",
+        "baked_pumpkins_long %>% \n",
+        "  slice_head(n = 10)\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "putq8DagltUQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's make some boxplots showing the distribution of the predictors with respect to the outcome color."
+      ],
+      "metadata": {
+        "id": "-RHm-12zlt-B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "theme_set(theme_light())\n",
+        "#Make a box plot for each predictor feature\n",
+        "baked_pumpkins_long %>% \n",
+        "  mutate(color = factor(color)) %>% \n",
+        "  ggplot(mapping = aes(x = color, y = values, fill = features)) +\n",
+        "  geom_boxplot() + \n",
+        "  facet_wrap(~ features, scales = \"free\", ncol = 3) +\n",
+        "  scale_color_viridis_d(option = \"cividis\", end = .8) +\n",
+        "  theme(legend.position = \"none\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "3Py4i1p1l3hP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Amazing🤩! For some of the features, there's a noticeable difference in the distribution for each color label. For instance, it seems the white pumpkins can be found in smaller packages and in some particular varieties of pumpkins. The *item_size* category also seems to make a difference in the color distribution. These features may help predict the color of a pumpkin.\n",
+        "\n",
+        "#### **Use a swarm plot**\n",
+        "\n",
+        "Color is a binary category (Orange or Not), it's called `categorical data`. There are other various ways of [visualizing categorical data](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar).\n",
+        "\n",
+        "Try a `swarm plot` to show the distribution of color with respect to the item_size.\n",
+        "\n",
+        "We'll use the [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) which provides methods to create beeswarm-style plots using ggplot2. Beeswarm plots are a way of plotting points that would ordinarily overlap so that they fall next to each other instead."
+      ],
+      "metadata": {
+        "id": "2LSj6_LCl68V"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create beeswarm plots of color and item_size\n",
+        "baked_pumpkins %>% \n",
+        "  mutate(color = factor(color)) %>% \n",
+        "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+        "  geom_quasirandom() +\n",
+        "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+        "  theme(legend.position = \"none\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "hGKeRgUemMTb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Violin plot**\n",
+        "\n",
+        "A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. [`Violin plots`](https://en.wikipedia.org/wiki/Violin_plot) are similar to box plots, except that they also show the probability density of the data at different values. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'."
+      ],
+      "metadata": {
+        "id": "_9wdZJH5mOvN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create a violin plot of color and item_size\n",
+        "baked_pumpkins %>%\n",
+        "  mutate(color = factor(color)) %>% \n",
+        "  ggplot(mapping = aes(x = color, y = item_size, fill = color)) +\n",
+        "  geom_violin() +\n",
+        "  geom_boxplot(color = \"black\", fill = \"white\", width = 0.02) +\n",
+        "  scale_fill_brewer(palette = \"Dark2\", direction = -1) +\n",
+        "  theme(legend.position = \"none\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "LFFFymujmTAZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.\n",
+        "\n",
+        "## 3. Build your logistic regression model\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/logistic-linear.png\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
+        "\n",
+        "> **🧮 Show Me The Math**\n",
+        ">\n",
+        "> Remember how `linear regression` often used `ordinary least squares` to arrive at a value? `Logistic regression` relies on the concept of 'maximum likelihood' using [`sigmoid functions`](https://wikipedia.org/wiki/Sigmoid_function). A Sigmoid Function on a plot looks like an `S shape`. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:\n",
+        ">\n",
+        "> \n",
+        "<p >\n",
+        "   <img src=\"../../images/sigmoid.png\">\n",
+        "\n",
+        "\n",
+        "> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class 1 of the binary choice. If not, it will be classified as 0.\n",
+        "\n",
+        "Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value.\n",
+        "\n",
+        "It is best practice to hold out some of your data for **testing** in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set. [rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:"
+      ],
+      "metadata": {
+        "id": "RA_bnMS9mVo8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Split data into 80% for training and 20% for testing\n",
+        "set.seed(2056)\n",
+        "pumpkins_split <- pumpkins_select %>% \n",
+        "  initial_split(prop = 0.8)\n",
+        "\n",
+        "# Extract the data in each split\n",
+        "pumpkins_train <- training(pumpkins_split)\n",
+        "pumpkins_test <- testing(pumpkins_split)\n",
+        "\n",
+        "# Print out the first 5 rows of the training set\n",
+        "pumpkins_train %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "PQdpEYYPmdGW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🙌 We are now ready to train a model by fitting the training features to the training label (color).\n",
+        "\n",
+        "We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers.\n",
+        "\n",
+        "There are quite a number of ways to specify a logistic regression model in Tidymodels. See `?logistic_reg()` For now, we'll specify a logistic regression model via the default `stats::glm()` engine."
+      ],
+      "metadata": {
+        "id": "MX9LipSimhn0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create a recipe that specifies preprocessing steps for modelling\n",
+        "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+        "  step_integer(all_predictors(), zero_based = TRUE)\n",
+        "\n",
+        "\n",
+        "# Create a logistic model specification\n",
+        "log_reg <- logistic_reg() %>% \n",
+        "  set_engine(\"glm\") %>% \n",
+        "  set_mode(\"classification\")\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "0Eo5-SbSmm2-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities.\n",
+        "\n",
+        "In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components."
+      ],
+      "metadata": {
+        "id": "G599GKhXmqWf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Bundle modelling components in a workflow\n",
+        "log_reg_wf <- workflow() %>% \n",
+        "  add_recipe(pumpkins_recipe) %>% \n",
+        "  add_model(log_reg)\n",
+        "\n",
+        "# Print out the workflow\n",
+        "log_reg_wf\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "cRoU0tpbmu1T"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake."
+      ],
+      "metadata": {
+        "id": "JnRXKmREnEpd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Train the model\n",
+        "wf_fit <- log_reg_wf %>% \n",
+        "  fit(data = pumpkins_train)\n",
+        "\n",
+        "# Print the trained workflow\n",
+        "wf_fit"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ehFwfkjWnNCb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The model print out shows the coefficients learned during training.\n",
+        "\n",
+        "Now we've trained the model using the training data, we can make predictions on the test data using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Let's start by using the model to predict labels for our test set and the probabilities for each label. When the probability is more than 0.5, the predict class is `ORANGE` else `WHITE`."
+      ],
+      "metadata": {
+        "id": "w01dGNZjnOJQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make predictions for color and corresponding probabilities\n",
+        "results <- pumpkins_test %>% select(color) %>% \n",
+        "  bind_cols(wf_fit %>% \n",
+        "              predict(new_data = pumpkins_test)) %>%\n",
+        "  bind_cols(wf_fit %>%\n",
+        "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+        "\n",
+        "# Compare predictions\n",
+        "results %>% \n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "K8PNjPfTnak2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Very nice! This provides some more insights into how logistic regression works.\n",
+        "\n",
+        "Comparing each prediction with its corresponding \"ground truth\" actual value isn't a very efficient way to determine how well the model is predicting. Fortunately, Tidymodels has a few more tricks up its sleeve: [`yardstick`](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics.\n",
+        "\n",
+        "One performance metric associated with classification problems is the [`confusion matrix`](https://wikipedia.org/wiki/Confusion_matrix). A confusion matrix describes how well a classification model performs. A confusion matrix tabulates how many examples in each class were correctly classified by a model. In our case, it will show you how many orange pumpkins were classified as orange and how many white pumpkins were classified as white; the confusion matrix also shows you how many were classified into the **wrong** categories.\n",
+        "\n",
+        "The [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) function from yardstick calculates this cross-tabulation of observed and predicted classes."
+      ],
+      "metadata": {
+        "id": "N3J-yW0wngKo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Confusion matrix for prediction results\n",
+        "conf_mat(data = results, truth = color, estimate = .pred_class)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "0RD77Dq1nl2j"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's interpret the confusion matrix. Our model is asked to classify pumpkins between two binary categories, category `orange` and category `not-orange`\n",
+        "\n",
+        "-   If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a `true positive`, shown by the top left number.\n",
+        "\n",
+        "-   If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a `false negative`, shown by the bottom left number.\n",
+        "\n",
+        "-   If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a `false positive`, shown by the top right number.\n",
+        "\n",
+        "-   If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a `true negative`, shown by the bottom right number.\n",
+        "\n",
+        "\n",
+        "| **Truth** |\n",
+        "|:-----:|\n",
+        "\n",
+        "\n",
+        "|               |        |       |\n",
+        "|---------------|--------|-------|\n",
+        "| **Predicted** | ORANGE | WHITE |\n",
+        "| ORANGE        | TP     | FP    |\n",
+        "| WHITE         | FN     | TN    |"
+      ],
+      "metadata": {
+        "id": "H61sFwdOnoiO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.\n",
+        "\n",
+        "The confusion matrix is helpful since it gives rise to other metrics that can help us better evaluate the performance of a classification model. Let's go through some of them:\n",
+        "\n",
+        "🎓 Precision: `TP/(TP + FP)` defined as the proportion of predicted positives that are actually positive. Also called [positive predictive value](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\")\n",
+        "\n",
+        "🎓 Recall: `TP/(TP + FN)` defined as the proportion of positive results out of the number of samples which were actually positive. Also known as `sensitivity`.\n",
+        "\n",
+        "🎓 Specificity: `TN/(TN + FP)` defined as the proportion of negative results out of the number of samples which were actually negative.\n",
+        "\n",
+        "🎓 Accuracy: `TP + TN/(TP + TN + FP + FN)` The percentage of labels predicted accurately for a sample.\n",
+        "\n",
+        "🎓 F Measure: A weighted average of the precision and recall, with best being 1 and worst being 0.\n",
+        "\n",
+        "Let's calculate these metrics!"
+      ],
+      "metadata": {
+        "id": "Yc6QUie2oQUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Combine metric functions and calculate them all at once\n",
+        "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+        "eval_metrics(data = results, truth = color, estimate = .pred_class)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "p6rXx_T3oVxX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Visualize the ROC curve of this model**\n",
+        "\n",
+        "For a start, this is not a bad model; its precision, recall, F measure and accuracy are in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables. It also seems that our model was not really able to identify the white pumpkins 🧐. Could you guess why? One reason could be because of the high prevalence of ORANGE pumpkins in our training set making our model more inclined to predict the majority class.\n",
+        "\n",
+        "Let's do one more visualization to see the so-called [`ROC score`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):"
+      ],
+      "metadata": {
+        "id": "JcenzZo1oaKR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a roc_curve\n",
+        "results %>% \n",
+        "  roc_curve(color, .pred_ORANGE) %>% \n",
+        "  autoplot()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "BcmkHHHwogRB"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. ROC curves typically feature `True Positive Rate`/Sensitivity on the Y axis, and `False Positive Rate`/1-Specificity on the X axis. Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly.\n",
+        "\n",
+        "Finally, let's use `yardstick::roc_auc()` to calculate the actual Area Under the Curve. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example."
+      ],
+      "metadata": {
+        "id": "P_an3vc1oqjI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Calculate area under curve\n",
+        "results %>% \n",
+        "  roc_auc(color, .pred_ORANGE)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "SZyy5BT8ovew"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The result is around `0.67053`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is *pretty good*.\n",
+        "\n",
+        "In future lessons on classifications, you will learn how to improve your model's scores (such as dealing with imbalanced data in this case).\n",
+        "\n",
+        "But for now, congratulations 🎉🎉🎉! You've completed these regression lessons!\n",
+        "\n",
+        "You R awesome!\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "<!--![Artwork by @allison_horst](../../images/r_learners_sm.jpeg)-->\n"
+      ],
+      "metadata": {
+        "id": "5jtVKLTVoy6u"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
new file mode 100644
index 000000000..a3d332e7e
--- /dev/null
+++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
@@ -0,0 +1,430 @@
+---
+title: 'Build a regression model: logistic regression'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Build a logistic regression model - Lesson 4
+
+![Infographic by Dasani Madipalli](../../images/logistic-linear.png){width="600"}
+
+#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**
+
+####  Introduction
+
+In this final lesson on Regression, one of the basic *classic* ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict `binary` `categories`. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not?
+
+In this lesson, you will learn:
+
+-   Techniques for logistic regression
+
+✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
+
+#### **Prerequisite**
+
+Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.
+
+Let's build a logistic regression model to predict that, given some variables, *what color a given pumpkin is likely to be* (orange 🎃 or white 👻).
+
+> Why are we talking about binary classification in a lesson grouping about regression? Only for linguistic convenience, as logistic regression is [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), albeit a linear-based one. Learn about other ways to classify data in the next lesson group.
+
+For this lesson, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.
+
+-   `ggbeeswarm`: The [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) provides methods to create beeswarm-style plots using ggplot2.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "janitor", "ggbeeswarm"))`
+
+Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman"))install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)
+```
+
+## ** Define the question**
+
+For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.
+
+> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking!
+
+## **About logistic regression**
+
+Logistic regression differs from linear regression, which you learned about previously, in a few important ways.
+
+#### **Binary classification**
+
+Logistic regression does not offer the same features as linear regression. The former offers a prediction about a `binary category` ("orange or not orange") whereas the latter is capable of predicting `continual values`, for example given the origin of a pumpkin and the time of harvest, *how much its price will rise*.
+
+![Infographic by Dasani Madipalli](../../images/pumpkin-classifier.png){width="600"}
+
+#### **Other classifications**
+
+There are other types of logistic regression, including multinomial and ordinal:
+
+-   **Multinomial**, which involves having more than one category - "Orange, White, and Striped".
+
+-   **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
+
+![Infographic by Dasani Madipalli](../../images/multinomial-ordinal.png){width="600"}
+
+\
+**It's still linear**
+
+Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not.
+
+#### **Variables DO NOT have to correlate**
+
+Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.
+
+#### **You need a lot of clean data**
+
+Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.
+
+✅ Think about the types of data that would lend themselves well to logistic regression
+
+## 1. Tidy the data
+
+Now, the fun begins! Let's start by importing the data, cleaning the data a bit, dropping rows containing missing values and selecting only some of the columns:
+
+```{r, tidyr, message=F, warning=F}
+# Load the core tidyverse packages
+library(tidyverse)
+
+# Import the data and clean column names
+pumpkins <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv") %>% 
+  clean_names()
+
+# Select desired columns
+pumpkins_select <- pumpkins %>% 
+  select(c(city_name, package, variety, origin, item_size, color)) 
+
+# Drop rows containing missing values and encode color as factor (category)
+pumpkins_select <- pumpkins_select %>% 
+  drop_na() %>% 
+  mutate(color = factor(color))
+
+# View the first few rows
+pumpkins_select %>% 
+  slice_head(n = 5)
+
+```
+
+Sometimes, we may want some little more information on our data. We can have a look at the `data`, `its structure` and the `data type` of its features by using the [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) function as below:
+
+```{r glimpse}
+pumpkins_select %>% 
+  glimpse()
+```
+
+Wow! Seems that all our columns are all of type *character*, further alluding that they are all categorical.
+
+Let's confirm that we will actually be doing a binary classification problem:
+
+```{r distinct color}
+# Subset distinct observations in outcome column
+pumpkins_select %>% 
+  distinct(color)
+
+```
+
+🥳🥳 That went down well!
+
+## 2. Explore the data
+
+The goal of data exploration is to try to understand the `relationships` between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. One way of doing this is by using data visualization.
+
+Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3-R.ipynb).
+
+Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.
+
+> Normally, recipes is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case it is **highly recommend** that you use a `workflow()` instead of manually estimating a recipe using prep and bake. We'll see all this in just a moment.
+>
+> However for now, we are using recipes + prep + bake to specify what steps should be applied to a data set in order to get it ready for data analysis and then extract the preprocessed data with the steps applied.
+
+```{r recipe_prep_bake}
+# Preprocess and extract data to allow some data analysis
+baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>% 
+  # Encode all columns to a set of integers
+  step_integer(all_predictors(), zero_based = T) %>% 
+  prep() %>% 
+  bake(new_data = NULL)
+
+
+# Display the first few rows of preprocessed data
+baked_pumpkins %>% 
+  slice_head(n = 5)
+
+```
+
+Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`.
+
+```{r pivot}
+# Pivot data to long format
+baked_pumpkins_long <- baked_pumpkins %>% 
+  pivot_longer(!color, names_to = "features", values_to = "values")
+
+
+# Print out restructured data
+baked_pumpkins_long %>% 
+  slice_head(n = 10)
+
+```
+
+
+Now, let's make some boxplots showing the distribution of the predictors with respect to the outcome color!
+
+```{r boxplots}
+theme_set(theme_light())
+#Make a box plot for each predictor feature
+baked_pumpkins_long %>% 
+  mutate(color = factor(color)) %>% 
+  ggplot(mapping = aes(x = color, y = values, fill = features)) +
+  geom_boxplot() + 
+  facet_wrap(~ features, scales = "free", ncol = 3) +
+  scale_color_viridis_d(option = "cividis", end = .8) +
+  theme(legend.position = "none")
+```
+
+Amazing🤩! For some of the features, there's a noticeable difference in the distribution for each color label. For instance, it seems the white pumpkins can be found in smaller packages and in some particular varieties of pumpkins. The *item_size* category also seems to make a difference in the color distribution. These features may help predict the color of a pumpkin.
+
+#### **Use a swarm plot**
+
+Color is a binary category (Orange or Not), it's called `categorical data`. There are other various ways of [visualizing categorical data](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar).
+
+Try a `swarm plot` to show the distribution of color with respect to the item_size.
+
+We'll use the [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) which provides methods to create beeswarm-style plots using ggplot2. Beeswarm plots are a way of plotting points that would ordinarily overlap so that they fall next to each other instead.
+
+```{r bee_swarm plot}
+# Create beeswarm plots of color and item_size
+baked_pumpkins %>% 
+  mutate(color = factor(color)) %>% 
+  ggplot(mapping = aes(x = color, y = item_size, color = color)) +
+  geom_quasirandom() +
+  scale_color_brewer(palette = "Dark2", direction = -1) +
+  theme(legend.position = "none")
+```
+
+#### **Violin plot**
+
+A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. [`Violin plots`](https://en.wikipedia.org/wiki/Violin_plot) are similar to box plots, except that they also show the probability density of the data at different values. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'.
+
+```{r violin_plot}
+# Create a violin plot of color and item_size
+baked_pumpkins %>%
+  mutate(color = factor(color)) %>% 
+  ggplot(mapping = aes(x = color, y = item_size, fill = color)) +
+  geom_violin() +
+  geom_boxplot(color = "black", fill = "white", width = 0.02) +
+  scale_fill_brewer(palette = "Dark2", direction = -1) +
+  theme(legend.position = "none")
+
+```
+
+Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.
+
+## 3. Build your model
+
+> **🧮 Show Me The Math**
+>
+> Remember how `linear regression` often used `ordinary least squares` to arrive at a value? `Logistic regression` relies on the concept of 'maximum likelihood' using [`sigmoid functions`](https://wikipedia.org/wiki/Sigmoid_function). A Sigmoid Function on a plot looks like an `S shape`. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:
+>
+> ![](../../images/sigmoid.png)
+>
+> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class 1 of the binary choice. If not, it will be classified as 0.
+
+Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value.
+
+It is best practice to hold out some of your data for **testing** in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set. [rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:
+
+```{r split_data}
+# Split data into 80% for training and 20% for testing
+set.seed(2056)
+pumpkins_split <- pumpkins_select %>% 
+  initial_split(prop = 0.8)
+
+# Extract the data in each split
+pumpkins_train <- training(pumpkins_split)
+pumpkins_test <- testing(pumpkins_split)
+
+# Print out the first 5 rows of the training set
+pumpkins_train %>% 
+  slice_head(n = 5)
+
+
+```
+
+🙌 We are now ready to train a model by fitting the training features to the training label (color).
+
+We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers.
+
+There are quite a number of ways to specify a logistic regression model in Tidymodels. See `?logistic_reg()` For now, we'll specify a logistic regression model via the default `stats::glm()` engine.
+
+```{r log_reg}
+# Create a recipe that specifies preprocessing steps for modelling
+pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% 
+  step_integer(all_predictors(), zero_based = TRUE)
+
+
+# Create a logistic model specification
+log_reg <- logistic_reg() %>% 
+  set_engine("glm") %>% 
+  set_mode("classification")
+
+
+```
+
+Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities.
+
+In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components.
+
+```{r workflow}
+# Bundle modelling components in a workflow
+log_reg_wf <- workflow() %>% 
+  add_recipe(pumpkins_recipe) %>% 
+  add_model(log_reg)
+
+# Print out the workflow
+log_reg_wf
+
+
+```
+
+After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake.
+
+```{r train}
+# Train the model
+wf_fit <- log_reg_wf %>% 
+  fit(data = pumpkins_train)
+
+# Print the trained workflow
+wf_fit
+
+```
+
+The model print out shows the coefficients learned during training.
+
+Now we've trained the model using the training data, we can make predictions on the test data using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Let's start by using the model to predict labels for our test set and the probabilities for each label. When the probability is more than 0.5, the predict class is `ORANGE` else `WHITE`.
+
+```{r test_pred}
+# Make predictions for color and corresponding probabilities
+results <- pumpkins_test %>% select(color) %>% 
+  bind_cols(wf_fit %>% 
+              predict(new_data = pumpkins_test)) %>%
+  bind_cols(wf_fit %>%
+              predict(new_data = pumpkins_test, type = "prob"))
+
+# Compare predictions
+results %>% 
+  slice_head(n = 10)
+
+```
+
+Very nice! This provides some more insights into how logistic regression works.
+
+Comparing each prediction with its corresponding "ground truth" actual value isn't a very efficient way to determine how well the model is predicting. Fortunately, Tidymodels has a few more tricks up its sleeve: [`yardstick`](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics.
+
+One performance metric associated with classification problems is the [`confusion matrix`](https://wikipedia.org/wiki/Confusion_matrix). A confusion matrix describes how well a classification model performs. A confusion matrix tabulates how many examples in each class were correctly classified by a model. In our case, it will show you how many orange pumpkins were classified as orange and how many white pumpkins were classified as white; the confusion matrix also shows you how many were classified into the **wrong** categories.
+
+The [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) function from yardstick calculates this cross-tabulation of observed and predicted classes.
+
+```{r conf_mat}
+# Confusion matrix for prediction results
+conf_mat(data = results, truth = color, estimate = .pred_class)
+
+
+```
+
+Let's interpret the confusion matrix. Our model is asked to classify pumpkins between two binary categories, category `orange` and category `not-orange`
+
+-   If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a `true positive`, shown by the top left number.
+
+-   If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a `false negative`, shown by the bottom left number.
+
+-   If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a `false positive`, shown by the top right number.
+
+-   If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a `true negative`, shown by the bottom right number.
+
+| Truth |
+|:-----:|
+
+
+|               |        |       |
+|---------------|--------|-------|
+| **Predicted** | ORANGE | WHITE |
+| ORANGE        | TP     | FP    |
+| WHITE         | FN     | TN    |
+
+As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
+
+The confusion matrix is helpful since it gives rise to other metrics that can help us better evaluate the performance of a classification model. Let's go through some of them:
+
+🎓 Precision: `TP/(TP + FP)` defined as the proportion of predicted positives that are actually positive. Also called [positive predictive value](https://en.wikipedia.org/wiki/Positive_predictive_value "Positive predictive value")
+
+🎓 Recall: `TP/(TP + FN)` defined as the proportion of positive results out of the number of samples which were actually positive. Also known as `sensitivity`.
+
+🎓 Specificity: `TN/(TN + FP)` defined as the proportion of negative results out of the number of samples which were actually negative.
+
+🎓 Accuracy: `TP + TN/(TP + TN + FP + FN)` The percentage of labels predicted accurately for a sample.
+
+🎓 F Measure: A weighted average of the precision and recall, with best being 1 and worst being 0.
+
+Let's calculate these metrics!
+
+```{r metric_set}
+# Combine metric functions and calculate them all at once
+eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)
+eval_metrics(data = results, truth = color, estimate = .pred_class)
+```
+
+#### **Visualize the ROC curve of this model**
+
+For a start, this is not a bad model; its precision, recall, F measure and accuracy are in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables. It also seems that our model was not really able to identify the white pumpkins 🧐. Could you guess why? One reason could be because of the high prevalence of ORANGE pumpkins in our training set making our model more inclined to predict the majority class.
+
+Let's do one more visualization to see the so-called [`ROC score`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):
+
+```{r roc_curve}
+# Make a roc_curve
+results %>% 
+  roc_curve(color, .pred_ORANGE) %>% 
+  autoplot()
+
+```
+
+ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. ROC curves typically feature `True Positive Rate`/Sensitivity on the Y axis, and `False Positive Rate`/1-Specificity on the X axis. Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly.
+
+Finally, let's use `yardstick::roc_auc()` to calculate the actual Area Under the Curve. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example.
+
+```{r roc_aoc}
+# Calculate area under curve
+results %>% 
+  roc_auc(color, .pred_ORANGE)
+
+```
+
+The result is around `0.67053`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is *pretty good*.
+
+In future lessons on classifications, you will learn how to improve your model's scores (such as dealing with imbalanced data in this case).
+
+But for now, congratulations 🎉🎉🎉! You've completed these regression lessons!
+
+You R awesome!
+
+![Artwork by \@allison_horst](../../images/r_learners_sm.jpeg)
+
+
diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index ac5a3a98a..553205d71 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -3,7 +3,7 @@
 ![Infografik regresi logistik vs. linear](../images/logistic-linear.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## Pembukaan
 
@@ -230,10 +230,6 @@ Apa yang sedang terjadi di sini? Mari kita asumsi dulu bahwa model kita ditanyak
 - Kalau modelmu memprediksi sesuati sebagai sebuah labu tetapi sebenarnya bukan sebuah labu, itu disebut negatif palsu yang diindikasi angka di pojok kiri bawah.
 - Kalau modelmu memprediksi sesuati sebagai bukan sebuah labu dan memang benar sesuatu itu bukan sebuah labu, itu disebut negatif benar yang diindikasi angka di pojok kanan bawah.
 
-![Matriks Kebingungan](../images/confusion-matrix.png)
-
-> Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
-
 Sebagaimana kamu mungkin sudah pikirkan, lebih baik dapat banyak positif benar dan negatif benar dan sedikit positif palsu dan negatif palsu. Implikasinya adalah performa modelnya bagus.
 
 ✅ Pertanyaan: Berdasarkan matriks kebingungan, modelnya baik tidak? Jawaban: Tidak buruk; ada banyak positif benar dan sedikit negatif palsu.
@@ -245,9 +241,9 @@ Mari kita lihat kembali istilah-istilah yang kita lihat tadi dengan bantuan matr
 > NB: Negatif benar
 > NP: Negatif palsu
 
-🎓 Presisi: PB/(PB + NP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
+🎓 Presisi: PB/(PB + PP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
 
-🎓 *Recall*: PB/(PB + PP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
+🎓 *Recall*: PB/(PB + NP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
 
 🎓 *f1-score*: (2 * Presisi * *Recall*)/(Presisi + *Recall*) Sebuah rata-rata tertimbang antara presisi dan *recall*. 1 itu baik dan 0 itu buruk.
 
@@ -295,7 +291,7 @@ Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaim
 
 Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik.
 
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## Review & Pembelajaran mandiri
 
diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
new file mode 100644
index 000000000..943a68c78
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/README.it.md
@@ -0,0 +1,295 @@
+# Regressione logistica per prevedere le categorie
+
+![Infografica di regressione lineare e logistica](../images/logistic-linear.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/?loc=it)
+
+## Introduzione
+
+In questa lezione finale sulla Regressione, una delle tecniche _classiche_ di base di machine learning, si darà un'occhiata alla Regressione Logistica. Si dovrebbe utilizzare questa tecnica per scoprire modelli per prevedere le categorie binarie. Questa caramella è al cioccolato o no? Questa malattia è contagiosa o no? Questo cliente sceglierà questo prodotto o no?
+
+In questa lezione, si imparerà:
+
+- Una nuova libreria per la visualizzazione dei dati
+- Tecniche per la regressione logistica
+
+✅ Con questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) si potrà approfondire la comprensione del lavoro con questo tipo di regressione
+## Prerequisito
+
+Avendo lavorato con i dati della zucca, ora si ha abbastanza familiarità con essi per rendersi conto che esiste una categoria binaria con cui è possibile lavorare: `Color` (Colore).
+
+Si costruisce un modello di regressione logistica per prevedere, date alcune variabili, di _che colore sarà probabilmente una data zucca_ (arancione 🎃 o bianca 👻).
+
+> Perché si parla di classificazione binaria in un gruppo di lezioni sulla regressione? Solo per comodità linguistica, poiché la regressione logistica è in [realtà un metodo di classificazione](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), anche se lineare. Si scopriranno altri modi per classificare i dati nel prossimo gruppo di lezioni.
+
+## Definire la domanda
+
+Allo scopo, verrà espressa come binaria: 'Arancio' o 'Non Arancio'. C'è anche una categoria "striped" (a strisce) nell'insieme di dati, ma ci sono pochi casi, quindi non verrà presa in considerazione. Comunque scompare una volta rimossi i valori null dall'insieme di dati.
+
+> 🎃 Fatto divertente, a volte le zucche bianche vengono chiamate zucche "fantasma" Non sono molto facili da intagliare, quindi non sono così popolari come quelle arancioni ma hanno un bell'aspetto!
+
+## Informazioni sulla regressione logistica
+
+La regressione logistica differisce dalla regressione lineare, che si è appresa in precedenza, in alcuni importanti modi.
+
+### Classificazione Binaria
+
+La regressione logistica non offre le stesse caratteristiche della regressione lineare. La prima offre una previsione su una categoria binaria ("arancione o non arancione") mentre la seconda è in grado di prevedere valori continui, ad esempio data l'origine di una zucca e il momento del raccolto, di _quanto aumenterà il suo prezzo_.
+
+![Modello di classificazione della zucca](../images/pumpkin-classifier.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+### Altre classificazioni:
+
+Esistono altri tipi di regressione logistica, inclusi multinomiale e ordinale:
+
+- **Multinomiale**, che implica avere più di una categoria: "arancione, bianco e a strisce".
+- **Ordinale**, che coinvolge categorie ordinate, utile se si volessero ordinare i risultati in modo logico, come le zucche che sono ordinate per un numero finito di dimensioni (mini,sm,med,lg,xl,xxl).
+
+![Regressione multinomiale contro ordinale](../images/multinomial-ordinal.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+### È ancora lineare
+
+Anche se questo tipo di Regressione riguarda le "previsioni di categoria", funziona ancora meglio quando esiste una chiara relazione lineare tra la variabile dipendente (colore) e le altre variabili indipendenti (il resto dell'insieme di dati, come il nome della città e le dimensioni) . È bene avere un'idea se c'è qualche linearità che divide queste variabili o meno.
+
+### Le variabili NON devono essere correlate
+
+Si ricorda come la regressione lineare ha funzionato meglio con più variabili correlate? La regressione logistica è l'opposto: le variabili non devono essere allineate. Funziona per questi dati che hanno correlazioni alquanto deboli.
+
+### Servono molti dati puliti
+
+La regressione logistica fornirà risultati più accurati se si utilizzano più dati; quindi si tenga a mente che, essendo l'insieme di dati sulla zucca piccolo, non è ottimale per questo compito
+
+✅ Si pensi ai tipi di dati che si prestano bene alla regressione logistica
+
+## Esercizio: riordinare i dati
+
+Innanzitutto, si puliscono un po 'i dati, eliminando i valori null e selezionando solo alcune delle colonne:
+
+1. Aggiungere il seguente codice:
+
+   ```python
+   from sklearn.preprocessing import LabelEncoder
+
+   new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+
+   new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+
+   new_pumpkins.dropna(inplace=True)
+
+   new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+   ```
+
+   Si può sempre dare un'occhiata al nuovo dataframe:
+
+   ```python
+   new_pumpkins.info
+   ```
+
+### Visualizzazione - griglia affiancata
+
+A questo punto si è caricato di nuovo il [notebook iniziale](../notebook.ipynb) con i dati della zucca e lo si è pulito in modo da preservare un insieme di dati contenente alcune variabili, incluso `Color`. Si visualizza il dataframe nel notebook utilizzando una libreria diversa: [Seaborn](https://seaborn.pydata.org/index.html), che è costruita su Matplotlib, usata in precedenza.
+
+Seaborn offre alcuni modi accurati per visualizzare i dati. Ad esempio, si possono confrontare le distribuzioni dei dati per ogni punto in una griglia affiancata.
+
+1. Si crea una griglia di questo tipo istanziando `PairGrid`, usando i dati della zucca `new_pumpkins`, poi chiamando `map()`:
+
+   ```python
+   import seaborn as sns
+
+   g = sns.PairGrid(new_pumpkins)
+   g.map(sns.scatterplot)
+   ```
+
+   ![Una griglia di dati visualizzati](../images/grid.png)
+
+   Osservando i dati fianco a fianco, si può vedere come i dati di Color si riferiscono alle altre colonne.
+
+   ✅ Data questa griglia del grafico a dispersione, quali sono alcune esplorazioni interessanti che si possono immaginare?
+
+### Usare un grafico a sciame
+
+Poiché Color è una categoria binaria (arancione o no), viene chiamata "dati categoriali" e richiede "un [approccio più specializzato](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) alla visualizzazione". Esistono altri modi per visualizzare la relazione di questa categoria con altre variabili.
+
+È possibile visualizzare le variabili fianco a fianco con i grafici di Seaborn.
+
+1. Si provi un grafico a "sciame" per mostrare la distribuzione dei valori:
+
+   ```python
+   sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+   ```
+
+   ![Uno sciame di dati visualizzati](../images/swarm.png)
+
+### Grafico violino
+
+Un grafico di tipo "violino" è utile in quanto è possibile visualizzare facilmente il modo in cui sono distribuiti i dati nelle due categorie. I grafici di tipo violino non funzionano così bene con insieme di dati più piccoli poiché la distribuzione viene visualizzata in modo più "liscio".
+
+1. Chiamare `catplot()` passando i parametri `x=Color`, `kind="violin"` :
+
+   ```python
+   sns.catplot(x="Color", y="Item Size",
+               kind="violin", data=new_pumpkins)
+   ```
+
+   ![una tabella di un grafico di tipo violino](../images/violin.png)
+
+   ✅ Provare a creare questo grafico e altri grafici Seaborn, utilizzando altre variabili.
+
+Ora che si ha un'idea della relazione tra le categorie binarie di colore e il gruppo più ampio di dimensioni, si esplora la regressione logistica per determinare il probabile colore di una data zucca.
+
+> **🧮 Mostrami la matematica**
+>
+> Si ricorda come la regressione lineare usava spesso i minimi quadrati ordinari per arrivare a un valore? La regressione logistica si basa sul concetto di "massima verosimiglianza" utilizzando [le funzioni sigmoidi](https://wikipedia.org/wiki/Sigmoid_function). Una "Funzione Sigmoide" su un grafico ha l'aspetto di una forma a "S". Prende un valore e lo mappa da qualche parte tra 0 e 1. La sua curva è anche chiamata "curva logistica". La sua formula si presenta così:
+>
+> ![funzione logistica](../images/sigmoid.png)
+>
+> dove il punto medio del sigmoide si trova nel punto 0 di x, L è il valore massimo della curva e k è la pendenza della curva. Se l'esito della funzione è maggiore di 0,5, all'etichetta in questione verrà assegnata la classe '1' della scelta binaria. In caso contrario, sarà classificata come '0'.
+
+## Costruire il modello
+
+Costruire un modello per trovare queste classificazioni binarie è sorprendentemente semplice in Scikit-learn.
+
+1. Si selezionano le variabili da utilizzare nel modello di classificazione e si dividono gli insiemi di training e test chiamando `train_test_split()`:
+
+   ```python
+   from sklearn.model_selection import train_test_split
+
+   Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+
+   X = new_pumpkins[Selected_features]
+   y = new_pumpkins['Color']
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   ```
+
+1. Ora si può addestrare il modello, chiamando `fit()` con i dati di addestramento e stamparne il risultato:
+
+   ```python
+   from sklearn.model_selection import train_test_split
+   from sklearn.metrics import accuracy_score, classification_report
+   from sklearn.linear_model import LogisticRegression
+
+   model = LogisticRegression()
+   model.fit(X_train, y_train)
+   predictions = model.predict(X_test)
+
+   print(classification_report(y_test, predictions))
+   print('Predicted labels: ', predictions)
+   print('Accuracy: ', accuracy_score(y_test, predictions))
+   ```
+
+   Si dia un'occhiata al tabellone segnapunti del modello. Non è male, considerando che si hanno solo circa 1000 righe di dati:
+
+   ```output
+                      precision    recall  f1-score   support
+
+              0       0.85      0.95      0.90       166
+              1       0.38      0.15      0.22        33
+
+       accuracy                           0.82       199
+      macro avg       0.62      0.55      0.56       199
+   weighted avg       0.77      0.82      0.78       199
+
+   Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+    0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+    1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+    0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+    0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+    0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+   ```
+
+## Migliore comprensione tramite una matrice di confusione
+
+Sebbene si possano ottenere [i termini](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) del rapporto dei punteggi stampando gli elementi di cui sopra, si potrebbe essere in grado di comprendere più facilmente il modello utilizzando una [matrice di confusione](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) che aiuti a capire come lo stesso sta funzionando.
+
+> 🎓 Una '[matrice di confusione](https://it.wikipedia.org/wiki/Matrice_di_confusione)' (o 'matrice di errore') è una tabella che esprime i veri contro i falsi positivi e negativi del modello, misurando così l'accuratezza delle previsioni.
+
+1. Per utilizzare una metrica di confusione, si `chiama confusion_matrix()`:
+
+   ```python
+   from sklearn.metrics import confusion_matrix
+   confusion_matrix(y_test, predictions)
+   ```
+
+   Si dia un'occhiata alla matrice di confusione del modello:
+
+   ```output
+   array([[162,   4],
+          [ 33,   0]])
+   ```
+
+Cosa sta succedendo qui? Si supponga che al modello venga chiesto di classificare gli elementi tra due categorie binarie, la categoria "zucca" e la categoria "non una zucca".
+
+- Se il modello prevede qualcosa come una zucca e appartiene alla categoria 'zucca' in realtà lo si chiama un vero positivo, mostrato dal numero in alto a sinistra.
+- Se il modello prevede qualcosa come non una zucca e appartiene alla categoria 'zucca' in realtà si chiama falso positivo, mostrato dal numero in alto a destra.
+- Se il modello prevede qualcosa come una zucca e appartiene alla categoria 'non-una-zucca' in realtà si chiama falso negativo, mostrato dal numero in basso a sinistra.
+- Se il modello prevede qualcosa come non una zucca e appartiene alla categoria 'non-una-zucca' in realtà lo si chiama un vero negativo, mostrato dal numero in basso a destra.
+
+Come si sarà intuito, è preferibile avere un numero maggiore di veri positivi e veri negativi e un numero inferiore di falsi positivi e falsi negativi, il che implica che il modello funziona meglio.
+
+✅ Domanda: Secondo la matrice di confusione, come si è comportato il modello? Risposta: Non male; ci sono un buon numero di veri positivi ma anche diversi falsi negativi.
+
+I termini visti in precedenza vengono rivisitati con l'aiuto della mappatura della matrice di confusione di TP/TN e FP/FN:
+
+🎓 Precisione: TP/(TP + FP) La frazione di istanze rilevanti tra le istanze recuperate (ad es. quali etichette erano ben etichettate)
+
+🎓 Richiamo: TP/(TP + FN) La frazione di istanze rilevanti che sono state recuperate, ben etichettate o meno
+
+🎓 f1-score: (2 * precisione * richiamo)/(precisione + richiamo) Una media ponderata della precisione e del richiamo, dove il migliore è 1 e il peggiore è 0
+
+🎓 Supporto: il numero di occorrenze di ciascuna etichetta recuperata
+
+🎓 Accuratezza: (TP + TN)/(TP + TN + FP + FN) La percentuale di etichette prevista accuratamente per un campione.
+
+🎓 Macro Media: il calcolo delle metriche medie non ponderate per ciascuna etichetta, senza tener conto dello squilibrio dell'etichetta.
+
+🎓 Media ponderata: il calcolo delle metriche medie per ogni etichetta, tenendo conto dello squilibrio dell'etichetta pesandole in base al loro supporto (il numero di istanze vere per ciascuna etichetta).
+
+✅ Si riesce a pensare a quale metrica si dovrebbe guardare se si vuole che il modello riduca il numero di falsi negativi?
+
+## Visualizzare la curva ROC di questo modello
+
+Questo non è un cattivo modello; la sua precisione è nell'intervallo dell'80%, quindi idealmente si potrebbe usare per prevedere il colore di una zucca dato un insieme di variabili.
+
+Si rende un'altra visualizzazione per vedere il cosiddetto punteggio 'ROC':
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+Usando di nuovo Seaborn, si traccia la [Caratteristica Operativa di Ricezione](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) o il ROC del modello. Le curve ROC vengono spesso utilizzate per ottenere una visualizzazione dell'output di un classificatore in termini di veri e falsi positivi. "Le curve ROC in genere presentano un tasso di veri positivi sull'asse Y e un tasso di falsi positivi sull'asse X". Pertanto, la ripidità della curva e lo spazio tra la linea del punto medio e la curva contano: si vuole una curva che si sposti rapidamente verso l'alto e oltre la linea. In questo caso, ci sono falsi positivi con cui iniziare, quindi la linea si dirige correttamente:
+
+![ROC](../images/ROC.png)
+
+Infine, si usa l'[`API roc_auc_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) di Scikit-learn per calcolare l'effettiva "Area sotto la curva" (AUC):
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+Il risultato è `0.6976998904709748`. Dato che l'AUC varia da 0 a 1, si desidera un punteggio elevato, poiché un modello corretto al 100% nelle sue previsioni avrà un AUC di 1; in questo caso, il modello è _abbastanza buono_.
+
+Nelle lezioni future sulle classificazioni si imparerà come eseguire l'iterazione per migliorare i punteggi del  modello. Ma per ora, congratulazioni! Si sono completate queste lezioni di regressione!
+
+---
+## 🚀 Sfida
+
+C'è molto altro da svelare riguardo alla regressione logistica! Ma il modo migliore per imparare è sperimentare. Trovare un insieme di dati che si presti a questo tipo di analisi e costruire un modello con esso. Cosa si è appreso? suggerimento: provare [Kaggle](https://kaggle.com) per ottenere insiemi di dati interessanti.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Leggere le prime pagine di [questo articolo da Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) su alcuni usi pratici della regressione logistica. Si pensi alle attività più adatte per l'uno o l'altro tipo di attività di regressione studiate fino a questo punto. Cosa funzionerebbe meglio?
+
+## Compito
+
+[Ritentare questa regressione](assignment.it.md)
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+# カテゴリ予測のためのロジスティック回帰
+
+![ロジスティク回帰 vs 線形回帰のインフォグラフィック](../images/logistic-linear.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
+## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+
+## イントロダクション
+
+回帰の最後のレッスンでは、古典的な機械学習手法の一つである、「ロジスティック回帰」を見ていきます。この手法は、2値のカテゴリを予測するためのパターンを発見するために使います。例えば、「このお菓子は、チョコレートかどうか？」、「この病気は伝染するかどうか？」、「この顧客は、この商品を選ぶかどうか？」などです。
+
+このレッスンでは以下の内容を扱います。
+
+- データを可視化するための新しいライブラリ
+- ロジスティック回帰について
+
+✅ この[モジュール](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) では、今回のタイプのような回帰について理解を深めることができます。
+
+## 前提条件
+
+カボチャのデータを触ったことで、データの扱いにかなり慣れてきました。その際にバイナリカテゴリが一つあることに気づきました。「`Color`」です。
+
+いくつかの変数が与えられたときに、あるカボチャがどのような色になる可能性が高いか (オレンジ🎃または白👻)を予測するロジスティック回帰モデルを構築してみましょう。
+
+> なぜ、回帰についてのレッスンで二値分類の話をしているのでしょうか？ロジスティック回帰は、線形ベースのものではありますが、[実際には分類法](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) であるため、言語的な便宜上です。次のレッスングループでは、データを分類する他の方法について学びます。
+
+## 質問の定義
+
+ここでは、「Orange」か「Not Orange」かの二値で表現しています。データセットには「striped」というカテゴリーもありますが、ほとんど例がないので、ここでは使いません。データセットからnull値を削除すると、このカテゴリーは消えてしまいます。
+
+> 🎃 面白いことに、白いカボチャを「お化けカボチャ」と呼ぶことがあります。彫るのが簡単ではないので、オレンジ色のカボチャほど人気はありませんが、見た目がクールですよね！
+
+## ロジスティック回帰について
+
+ロジスティック回帰は、前回学んだ線形回帰とは、いくつかの重要な点で異なります。
+
+### 2値分類
+
+ロジスティック回帰は、線形回帰とは異なる特徴を持っています。ロジスティック回帰は、二値のカテゴリー（「オレンジ色かオレンジ色でないか」）についての予測を行うのに対し、線形回帰は連続的な値を予測します。例えば、カボチャの産地と収穫時期が与えられれば、その価格がどれだけ上昇するかを予測することができます。
+
+![カボチャ分類モデル](../images/pumpkin-classifier.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
+### その他の分類
+
+ロジスティック回帰には他にもMultinomialやOrdinalなどの種類があります。
+
+- **Multinomial**: これは2つ以上のカテゴリーを持つ場合です。 (オレンジ、白、ストライプ)
+- **Ordinal**: これは、順序付けられたカテゴリを含むもので、有限の数のサイズ（mini、sm、med、lg、xl、xxl）で並べられたカボチャのように、結果を論理的に並べたい場合に便利です。
+
+![Multinomial vs ordinal 回帰](../images/multinomial-ordinal.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
+
+### 線形について
+
+このタイプの回帰は、「カテゴリーの予測」が目的ですが、従属変数（色）と他の独立変数（都市名やサイズなどのデータセットの残りの部分）の間に明確な線形関係がある場合に最も効果的です。これらの変数を分ける線形性があるかどうかを把握するのは良いことです。
+
+### 変数が相関している必要はない
+
+線形回帰は、相関性の高い変数ほどよく働くことを覚えていますか？ロジスティック回帰は、そうとは限りません。相関関係がやや弱いこのデータには有効ですね。
+
+### 大量のきれいなデータが必要です
+
+一般的にロジスティック回帰は、より多くのデータを使用すれば、より正確な結果が得られます。私たちの小さなデータセットは、このタスクには最適ではありませんので、その点に注意してください。
+
+✅ ロジスティック回帰に適したデータの種類を考えてみてください。
+
+## エクササイズ - データの整形
+
+まず、NULL値を削除したり、一部の列だけを選択したりして、データを少し綺麗にします。
+
+1. 以下のコードを追加:
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+    
+    new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    
+    new_pumpkins.dropna(inplace=True)
+    
+    new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+    ```
+
+    新しいデータフレームはいつでも確認することができます。
+
+    ```python
+    new_pumpkins.info
+    ```
+
+### 可視化 - グリッド状に並べる
+
+ここまでで、[スターターノートブック](../notebook.ipynb) にパンプキンデータを再度読み込み、`Color`を含むいくつかの変数を含むデータセットを保持するように整形しました。別のライブラリを使って、ノートブック内のデータフレームを可視化してみましょう。[Seaborn](https://seaborn.pydata.org/index.html) というライブラリを使って、ノートブック内のデータフレームを可視化してみましょう。このライブラリは、今まで使っていた`Matplotlib`をベースにしています。
+
+Seabornには、データを可視化するためのいくつかの優れた方法があります。例えば、各データの分布を横並びのグリッドで比較することができます。
+
+1. かぼちゃのデータ`new_pumpkins`を使って、`PairGrid`をインスタンス化し、`map()`メソッドを呼び出して、以下のようなグリッドを作成します。
+
+    ```python
+    import seaborn as sns
+    
+    g = sns.PairGrid(new_pumpkins)
+    g.map(sns.scatterplot)
+    ```
+
+    ![グリッド状の可視化](../images/grid.png)
+
+    データを並べて観察することで、Colorのデータが他の列とどのように関連しているのかを知ることができます。
+
+    ✅ この散布図をもとに、どのような面白い試みが考えられるでしょうか？
+
+### swarm plot
+
+Colorは2つのカテゴリー（Orange or Not）であるため、「カテゴリカルデータ」と呼ばれ、「可視化にはより[専門的なアプローチ](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) 」が必要となります。このカテゴリと他の変数との関係を可視化する方法は他にもあります。
+
+Seabornプロットでは、変数を並べて表示することができます。
+
+1. 値の分布を示す、'swarm' plotを試してみます。 
+
+    ```python
+    sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+    ```
+
+    ![swarm plotによる可視化](../images/swarm.png)
+
+### Violin plot
+
+'violin' タイプのプロットは、2つのカテゴリーのデータがどのように分布しているかを簡単に視覚化できるので便利です。Violin plotは、分布がより「滑らか」に表示されるため、データセットが小さい場合はあまりうまくいきません。
+
+1. パラメータとして`x=Color`、`kind="violin"` をセットし、 `catplot()`メソッドを呼びます。
+
+    ```python
+    sns.catplot(x="Color", y="Item Size",
+                kind="violin", data=new_pumpkins)
+    ```
+
+    ![バイオリンタイプのチャート](../images/violin.png)
+
+    ✅ 他の変数を使って、このプロットや他のSeabornのプロットを作成してみてください。
+
+さて、`Color`の二値カテゴリと、より大きなサイズのグループとの関係がわかったところで、ロジスティック回帰を使って、あるカボチャの色について調べてみましょう。
+
+> **🧮 数学の確認**
+>
+> 線形回帰では、通常の最小二乗法を用いて値を求めることが多かったことを覚えていますか？ロジスティック回帰は、[シグモイド関数](https://wikipedia.org/wiki/Sigmoid_function) を使った「最尤」の概念に依存しています。シグモイド関数は、プロット上では「S」字のように見えます。その曲線は「ロジスティック曲線」とも呼ばれます。数式は次のようになります。
+>
+> ![ロジスティック関数](../images/sigmoid.png)
+>
+> ここで、シグモイドの中点はx=0の点、Lは曲線の最大値、kは曲線の急峻さを表します。この関数の結果が0.5以上であれば、そのラベルは二値選択のクラス「1」になります。そうでない場合は、「0」に分類されます。
+
+## モデルの構築
+
+これらの二値分類を行うためのモデルの構築は、Scikit-learnでは驚くほど簡単にできます。
+
+1. 分類モデルで使用したい変数を選択し、`train_test_split()`メソッドでトレーニングセットとテストセットを分割します。
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+    
+    X = new_pumpkins[Selected_features]
+    y = new_pumpkins['Color']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    
+    ```
+
+2. これで、学習データを使って`fit()`メソッドを呼び出し、モデルを訓練し、その結果を出力することができます。
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+    モデルのスコアボードを見てみましょう。1000行程度のデータしかないことを考えると、悪くないと思います。
+
+    ```output
+                       precision    recall  f1-score   support
+    
+               0       0.85      0.95      0.90       166
+               1       0.38      0.15      0.22        33
+    
+        accuracy                           0.82       199
+       macro avg       0.62      0.55      0.56       199
+    weighted avg       0.77      0.82      0.78       199
+    
+    Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+     0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+    ```
+
+## 混同行列による理解度の向上
+
+
+上記の項目を出力することで[スコアボードレポート](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) を得ることができますが、[混同行列](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) を使うことで、より簡単にモデルを理解することができるかもしれません。
+
+
+> 🎓 [混同行列](https://wikipedia.org/wiki/Confusion_matrix) とは、モデルの真の陽性と陰性を表す表で、予測の正確さを測ることができます。
+
+1. `confusion_matrix()`メソッドを呼んで、混同行列を作成します。
+
+    ```python
+    from sklearn.metrics import confusion_matrix
+    confusion_matrix(y_test, predictions)
+    ```
+
+    T作成したモデルの混同行列をみてみてください。
+
+    ```output
+    array([[162,   4],
+           [ 33,   0]])
+    ```
+
+Scikit-learnでは、混同行列の行 (axis=0)が実際のラベル、列 (axis=1)が予測ラベルとなります。
+
+|       |   0   |   1   |
+| :---: | :---: | :---: |
+|   0   |  TN   |  FP   |
+|   1   |  FN   |  TP   |
+
+ここで何が起こっているのか？例えば、カボチャを「オレンジ色」と「オレンジ色でない」という2つのカテゴリーに分類するように求められたとしましょう。
+
+- モデルではオレンジ色ではないと予測されたカボチャが、実際には「オレンジ色ではない」というカテゴリーに属していた場合、「true negative」と呼ばれ、左上の数字で示されます。
+- モデルではオレンジ色と予測されたカボチャが、実際には「オレンジ色ではない」カテゴリーに属していた場合、「false negative」と呼ばれ、左下の数字で示されます。
+- モデルがオレンジではないと予測したかぼちゃが、実際にはカテゴリー「オレンジ」に属していた場合、「false positive」と呼ばれ、右上の数字で示されます。
+- モデルがカボチャをオレンジ色と予測し、それが実際にカテゴリ「オレンジ」に属する場合、「true positive」と呼ばれ、右下の数字で示されます。
+
+お気づきの通り、true positiveとtrue negativeの数が多く、false positiveとfalse negativeの数が少ないことが好ましく、これはモデルの性能が高いことを意味します。
+
+混同行列は、precisionとrecallにどのように関係するのでしょうか？上記の分類レポートでは、precision（0.83）とrecall（0.98）が示されています。
+
+Precision = tp / (tp + fp) = 162 / (162 + 33) = 0.8307692307692308
+
+Recall = tp / (tp + fn) = 162 / (162 + 4) = 0.9759036144578314
+
+✅ Q: 混同行列によると、モデルの出来はどうでしたか？ A: 悪くありません。true negativeがかなりの数ありますが、false negativeもいくつかあります。
+
+先ほどの用語を、混同行列のTP/TNとFP/FNのマッピングを参考にして再確認してみましょう。
+
+🎓 Precision: TP/(TP + FP) 探索されたインスタンスのうち、関連性のあるインスタンスの割合（どのラベルがよくラベル付けされていたかなど）。
+
+🎓 Recall: TP/(TP + FN) ラベリングされているかどうかに関わらず、探索された関連インスタンスの割合です。
+
+🎓 f1-score: (2 * precision * recall)/(precision + recall) precisionとrecallの加重平均で、最高が1、最低が0となる。
+
+🎓 Support: 取得した各ラベルの出現回数です。
+
+🎓 Accuracy: (TP + TN)/(TP + TN + FP + FN) サンプルに対して正確に予測されたラベルの割合です。
+
+🎓 Macro Avg: 各ラベルの非加重平均指標の計算で、ラベルの不均衡を考慮せずに算出される。
+
+🎓 Weighted Avg: 各ラベルのサポート数（各ラベルの真のインスタンス数）で重み付けすることにより、ラベルの不均衡を考慮して、各ラベルの平均指標を算出する。
+
+✅ 自分のモデルでfalse negativeの数を減らしたい場合、どの指標に注目すべきか考えられますか？
+
+## モデルのROC曲線を可視化する
+
+これは悪いモデルではありません。精度は80%の範囲で、理想的には、一連の変数が与えられたときにカボチャの色を予測するのに使うことができます。
+
+いわゆる「ROC」スコアを見るために、もう一つの可視化を行ってみましょう。
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+Seaborn を再度使用して、モデルの [受信者操作特性 (Receiving Operating Characteristic)](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) またはROCをプロットします。ROC曲線は、分類器の出力を、true positiveとfalse positiveの観点から見るためによく使われます。ROC曲線は通常、true positive rateをY軸に、false positive rateをX軸にとっています。したがって、曲線の急峻さと、真ん中の線形な線と曲線の間のスペースが重要で、すぐに頭を上げて中線を超えるような曲線を求めます。今回のケースでは、最初にfalse positiveが出て、その後、ラインがきちんと上に向かって超えていきます。
+
+![ROC](../images/ROC.png)
+
+最後に、Scikit-learnの[`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) を使って、実際の「Area Under the Curve」(AUC)を計算します。
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+結果は`0.6976998904709748`となりました。AUCの範囲が0から1であることを考えると、大きなスコアが欲しいところです。なぜなら、予測が100%正しいモデルはAUCが1になるからです。
+
+今後の分類のレッスンでは、モデルのスコアを向上させるための反復処理の方法を学びます。一旦おめでとうございます。あなたはこの回帰のレッスンを完了しました。
+
+---
+## 🚀チャレンジ
+
+ロジスティック回帰については、まだまだ解き明かすべきことがたくさんあります。しかし、学ぶための最良の方法は、実験することです。この種の分析に適したデータセットを見つけて、それを使ってモデルを構築してみましょう。ヒント：面白いデータセットを探すために[Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) を試してみてください。
+
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+
+## レビュー & 自主学習
+
+ロジスティック回帰の実用的な使い方について、[Stanfordからのこの論文](https://web.stanford.edu/~jurafsky/slp3/5.pdf) の最初の数ページを読んでみてください。これまで学んできた回帰タスクのうち、どちらか一方のタイプに適したタスクについて考えてみてください。何が一番うまくいくでしょうか？
+
+## 課題 
+
+[回帰に再挑戦する](./assignment.ja.md)
diff --git a/2-Regression/4-Logistic/translations/README.ko.md b/2-Regression/4-Logistic/translations/README.ko.md
new file mode 100644
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@@ -0,0 +1,311 @@
+# 카테고리 예측하는 Logistic regression
+
+![Logistic vs. linear regression infographic](.././images/logistic-linear.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+
+## 소개
+
+기초 _classic_ ML 기술의 하나인, Regression에 대한 마지막 강의에서, Logistic Regression를 보겠습니다. 기술을 사용하면 binary categories를 예측하는 패턴을 찾습니다. 사탕 초콜릿인가요? 질병이 전염되나요? 고객이 제품을 선택하나요?
+
+이 강의에서, 다음을 배웁니다:
+
+- 데이터 시각화를 위한 새로운 라이브러리
+- logistic regression 기술
+
+✅ [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)애서 regression의 타입에 대하여 깊게 이해해봅니다.
+
+## 필요 조건
+
+호박 데이터로 작업했고, 지금부터 작업할 수 있는 하나의 binary category: `Color`가 있다는 것을 알기에 충분히 익숙해졌습니다.
+
+logistic regression 모델을 만들어서 몇가지 변수가 주어졌을 때, _호박의 (오렌지 🎃 또는 화이트 👻)색을 예측해봅니다_.
+
+> regression에 대하여 강의를 그룹으로 묶어서 binary classification에 관련된 대화를 하는 이유는 뭘까요? logistic regression은 linear-기반이지만, [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)이므로, 단지 언어적으로 편리해서 가능합니다. 다음 강의 그룹에서 데이터를 분류하는 다른 방식에 대하여 배워봅니다.
+
+## 질문 정의하기
+
+목적을 위하여, 바이너리로 표현합니다: 'Orange' 또는 'Not Orange'. 데이터셋에 'striped' 카테고리도 있지만 인스턴스가 약간 있으므로, 사용하지 않을 예정입니다. 데이터셋에서 null 값을 지우면 없어질겁니다.
+
+> 🎃 재미있는 사실은, 하얀 호박을 'ghost' 호박으로 부르고 있습니다. 조각내기 쉽지 않기 때문에, 오랜지만큼 인기가 없지만 멋집니다!
+
+## logistic regression 대하여
+
+Logistic regression는 일부 중요한 점에서, 이전에 배운, linear regression과 차이가 있습니다.
+
+### Binary classification
+
+Logistic regression은 linear regression와 동일한 features를 제공하지 않습니다. 이전은 binary category ("orange or not orange")에 대한 예측을 해주는 데에 비해 후자를 예시로 들어보면, 호박의 태생과 수확 시기에 따라 _가격이 얼마나 오르는 지_ 연속 값을 예측할 수 있습니다.
+
+![Pumpkin classification Model](.././images/pumpkin-classifier.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+### 다른 classifications
+
+다항 분포와 순서수를 포함한, 다른 타입의 logistic regression이 있습니다:
+
+- **다항분포**, 하나보다 더 많은 카테고리를 포함합니다. - "Orange, White, and Striped".
+- **순서수**, 정렬된 카테고리가 포함되며, 유한한 숫자 크기 (mini,sm,med,lg,xl,xxl)로 정렬된 호박과 같이, 결과를 논리적으로 정렬하고 싶을 때 유용합니다.
+
+![Multinomial vs ordinal regression](.././images/multinomial-ordinal.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+### 여전한 linear
+
+Regression의 타입은 모두 'category predictions'이지만, 종속 변수 (color)와 다른 독립 변수 (the rest of the dataset, like city name and size) 사이 깔끔한 linear relationship이 있을 때 잘 작동합니다. 변수를 나눌 선형성이 있는 지에 대한 아이디어를 찾는 게 좋습니다. 
+
+### 변수에 상관 관계가 없습니다
+
+linear regression이 많은 상관 변수에서 어떻게 작동하는지 기억나시나요? Logistic regression은 - 변수를 정렬할 필요가 없는 반대 입장입니다. 조금 약한 correlations로 데이터가 작용합니다.
+
+### 깔끔한 데이터가 많이 필요합니다
+
+Logistic regression은 많은 데이터로 정확한 결과를 줍니다; 작은 데이터셋은 최적화된 작업이 아니므로, 유념합시다. 
+
+✅ logistic regression에 적당한 데이터의 타입에 대해 생각합니다.
+
+## 연습 - tidy the data
+
+먼저, 데이터를 조금 정리하면서, null 값을 드랍하고 일부 열만 선택합니다:
+
+1. 다음 코드를 추가합니다:
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+    
+    new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    
+    new_pumpkins.dropna(inplace=True)
+    
+    new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+    ```
+
+    항상 새로운 데이터프레임을 살짝 볼 수 있습니다:
+
+    ```python
+    new_pumpkins.info
+    ```
+
+### 시각화 - side-by-side 그리드
+
+호박 데이터가 있는 [starter notebook](.././notebook.ipynb)을 다시 불러오고 `Color` 포함한, 몇 변수를 데이터셋에 컨테이너화하려고 정리했습니다. 다른 라이브러리를 사용해서 노트북에서 데이터프레임을 시각화해봅니다: [Seaborn](https://seaborn.pydata.org/index.html)은, 이전에 썻던 Matplotlib을 기반으로 만들어졌습니다. 
+
+Seaborn은 데이터를 시각화하는 깔끔한 방식들을 제공합니다. 예시로, side-by-side 그리드의 각 포인트에 대한 데이터의 분포를 비교할 수 있습니다.
+
+1. 호박 데이터 `new_pumpkins`를 사용해서, `PairGrid`를 인스턴스화하고, `map()`을 불러서 그리드를 만듭니다:
+
+    ```python
+    import seaborn as sns
+    
+    g = sns.PairGrid(new_pumpkins)
+    g.map(sns.scatterplot)
+    ```
+
+    ![A grid of visualized data](../images/grid.png)
+
+    데이터를 side-by-side로 지켜보면, 다른 열과 어떻게 관계가 이루어지는 지 볼 수 있습니다.
+
+    ✅ scatterplot 그리드가 주어지면, 흥미있는 탐구를 구상할 수 있나요?
+
+### swarm plot 사용하기
+
+색상은 binary category (Orange or Not)이므로, 'categorical data'라고 부르고 '시각화에는 더 [specialized approach](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar)'가 필요합니다. 카테고리와 다른 변수 관계를 시각화하는 다른 방식도 있습니다.
+
+Seaborn plots을 side-by-side로 변수를 시각화할 수 있습니다.
+
+1. 값의 분포를 보여주기 위해서 'swarm' plot을 시도합니다:
+
+    ```python
+    sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+    ```
+
+    ![A swarm of visualized data](../images/swarm.png)
+
+### Violin plot
+
+'violin' 타입 plot은 두개 카테고리의 데이터 배포하는 방식을 쉽게 시각화할 수 있어서 유용합니다. Violin plots은 분포가 더 'smoothly'하게 출력하기 때문에 더 작은 데이터셋에서 작동하지 않습니다.
+
+1. 파라미터 `x=Color`, `kind="violin"` 와 `catplot()`을 호출합니다:
+
+    ```python
+    sns.catplot(x="Color", y="Item Size",
+                kind="violin", data=new_pumpkins)
+    ```
+
+    ![a violin type chart](../images/violin.png)
+
+    ✅ 다른 변수를 사용해서, plot과 다른 Seaborn plots을 만들어봅니다.
+
+이제부터 색상의 binary categories와 큰 사이즈의 그룹 사이 관계에 대한 아이디어를 낼 수 있으므로, 주어진 호박의 가능한 색상을 결정하기 위해서 logistic regression를 찾아봅니다.
+
+> **🧮 Show Me The Math**
+>
+> linear regression이 값에 도달하기 위해서 자주 ordinary least squares을 사용하는 방식이 생각날까요? Logistic regression은 [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function)으로 'maximum likelihood' 컨셉에 의존합니다. plot의 'Sigmoid Function'은 'S' 모양으로 보이기도 합니다. 값을 가져와서 0과 1 사이 맵핑합니다. 이 곡선을 'logistic curve'라고 부릅니다. 공식은 이와 같습니다:
+>
+> ![logistic function](../images/sigmoid.png)
+>
+> 시그모이드의 중간 지점은 x의 0 포인트에서 자기자신이며, L은 곡선의 최대 값, 그리고 k는 곡선의 기울기입니다. 만약 함수의 결과가 0.5보다 크면, 질문 레이블에서 binary choice의 클래스 '1'이 할당됩니다. 만약 아니면, '0'으로 분류됩니다.
+
+## 모델 만들기
+
+binary classification을 찾는 모델을 만드는 건 Scikit-learn에서 놀랍도록 간단합니다.
+
+1. classification 모델을 사용하고 싶은 변수를 선택하고 `train_test_split()`을 불러서 훈련과 테스트할 셋으로 나눕니다:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+    
+    X = new_pumpkins[Selected_features]
+    y = new_pumpkins['Color']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    
+    ```
+
+1. 이제부터 훈련된 데이터로 `fit()`을 불러서, 모델을 훈련하고, 결과를 출력할 수 있습니다:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+   모델의 스코어보드를 봅니다. 데이터의 100개 행만 있다는 것을 고려하면, 나쁘지 않습니다
+
+    ```output
+                       precision    recall  f1-score   support
+    
+               0       0.85      0.95      0.90       166
+               1       0.38      0.15      0.22        33
+    
+        accuracy                           0.82       199
+       macro avg       0.62      0.55      0.56       199
+    weighted avg       0.77      0.82      0.78       199
+    
+    Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+     0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+    ```
+
+## confusion matrix으로 더 좋게 이해하기
+
+이 아이템을 출력해서 스코어보드 리포트 [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report)를 얻을 수 있지만, 모델의 성능을 이해하는 순간 도와주는 [confusion matrix](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix)를 사용하여 모델을 쉽게 이해할 수 있게 됩니다.
+
+> 🎓 '[confusion matrix](https://wikipedia.org/wiki/Confusion_matrix)' (또는 'error matrix')는 모델의 true 대 false 로 긍정 및 부정을 나타내서, 예측의 정확도를 측정하는 테이블입니다.
+
+1. `confusion_matrix()` 불러서, confusion metrics를 사용합니다:
+
+    ```python
+    from sklearn.metrics import confusion_matrix
+    confusion_matrix(y_test, predictions)
+    ```
+
+    모델의 confusion matrix를 봅니다:
+
+    ```output
+    array([[162,   4],
+           [ 33,   0]])
+    ```
+
+Scikit-learn에서, confusion matrices 행은 (axis 0) 실제 라벨이고 열은 (axis 1) 예측된 라벨입니다.
+
+|       |   0   |   1   |
+| :---: | :---: | :---: |
+|   0   |  TN   |  FP   |
+|   1   |  FN   |  TP   |
+
+어떤 일이 생기나요? 모델이 'orange'와 'not-orange' 카테고리의, 두 바이너리 카테고리로 호박을 분류하게 요청받았다고 가정합니다.
+
+- 만약 모델이 호박을 오랜지색이 아닌 것으로 예측하고 실제로 'not-orange' 카테고리에 있다면 좌측 상단에서 보여지고, true negative 라고 불립니다.
+- 만약 모델이 호박을 오랜지색으로 예측하고 실제로 'not-orange' 카테고리에 있다면 좌측 하단에 보여지고, false negative 라고 불립니다.
+- 만약 모델이 호박을 오랜지색이 아닌 것으로 예측하고 실제로 'orange' 카테고리에 있다면 우측 상단에 보여지고, false positive 라고 불립니다.
+- 만약 모델이 호박을 오랜지색으로 예측하고 실제로 'orange' 카테고리에 있다면 우측 하단에 보여지고, true positive 라고 불립니다.
+
+예상한 것처럼 true positives와 true negatives는 큰 숫자를 가지고 false positives와 false negatives은 낮은 숫자을 가지는 게 더 좋습니다, 모델의 성능이 더 좋다는 것을 의미합니다.
+
+confusion matrix는 정확도와 재현율에 얼마나 관련있나요? classification 리포트에 정확도와 (0.83) 재현율 (0.98)으로 보여져서 출력되었습니다.
+
+Precision = tp / (tp + fp) = 162 / (162 + 33) = 0.8307692307692308
+
+Recall = tp / (tp + fn) = 162 / (162 + 4) = 0.9759036144578314
+
+✅ Q: confusion matrix에 따르면, 모델은 어떻게 되나요? A: 나쁘지 않습니다; true positives의 많은 숫자뿐만 아니라 몇 false negatives도 있습니다.
+
+confusion matrix TP/TN 과 FP/FN의 맵핑으로 미리 본 용어에 대하여 다시 봅니다:
+
+🎓 정밀도: TP/(TP + FP) 검색된 인스턴스 중 관련된 인스턴스의 비율 (예시. 잘 라벨링된 라벨)
+
+🎓 재현율: TP/(TP + FN) 라벨링이 잘 되었는 지 상관없이, 검색한 관련된 인스턴스의 비율
+
+🎓 f1-score: (2 * precision * recall)/(precision + recall) 정밀도와 재현율의 가중치 평균은, 최고 1과 최저 0
+
+🎓 Support: 검색된 각 라벨의 발생 수
+
+🎓 정확도: (TP + TN)/(TP + TN + FP + FN) 샘플에서 정확한 예측이 이루어진 라벨의 백분율
+
+🎓 Macro Avg: 라벨 불균형을 고려하지 않고, 각 라벨의 가중치가 없는 평균 지표를 계산합니다.
+
+🎓 Weighted Avg: (각 라벨의 true 인스턴스 수) 지원에 따라 가중치를 할당해서 라벨 불균형을 고려해보고, 각 라벨의 평균 지표를 계산합니다.
+
+✅ 만약 모델이 false negatives의 숫자를 줄어들게 하려면 어떤 지표를 봐야할 지 생각할 수 있나요?
+
+## 모델의 ROC 곡선 시각화
+
+나쁜 모델은 아닙니다; 정확도가 80% 범위라서 이상적으로 주어진 변수의 셋에서 호박의 색을 예측할 때 사용할 수 있습니다.
+
+'ROC' 스코어라는 것을 보려면 더 시각화해봅니다:
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+
+Seaborn을 다시 사용해서, 모델의 [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) 또는 ROC를 plot합니다. ROC 곡선은 classifier의 아웃풋을 true 대 false positives 관점에서 보려고 수시로 사용됩니다. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." 따라서, 곡선의 경사도와 중간 포인트 라인과 곡선 사이 공간이 중요합니다; 라인 위로 빠르게 지나는 곡선을 원합니다. 이 케이스에서, 시작해야 할 false positives가 있고, 라인이 적당하게 계속 이어집니다:
+
+![ROC](.././images/ROC.png)
+
+마지막으로, Scikit-learn의 [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score)를 사용해서 실제 'Area Under the Curve' (AUC)를 계산합니다:
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+결과는 `0.6976998904709748`입니다. AUC 범위가 0부터 1까지 주어지면, 100% 예측하는 정확한 모델의 AUC가 1이므로, 큰 스코어를 원하게 됩니다. 이 케이스는, 모델이 _의외로 좋습니다_.
+
+classifications에 대한 이후 강의에서, 모델의 스코어를 개선하기 위하여 반복하는 방식을 배울 예정입니다. 하지만 지금은, 축하합니다! regression 강의를 완료했습니다!
+
+---
+## 🚀 도전
+
+logistic regression과 관련해서 풀어야할 내용이 더 있습니다! 하지만 배우기 좋은 방식은 실험입니다. 이런 분석에 적당한 데이터셋을 찾아서 모델을 만듭니다. 무엇을 배우나요? 팁: 흥미로운 데이터셋으로 [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets)에서 시도해보세요.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+
+## 검토 & 자기주도 학습
+
+logistic regression을 사용하는 약간의 실용적 사용에 대한 [this paper from Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf)의 처음 몇 페이지를 읽어봅니다. 연구했던 regression 작업 중에서 하나 또는 그 외 타입에 적당한 작업을 생각해봅니다. 어떤게 더 잘 작동하나요?
+
+## 과제 
+
+[Retrying this regression](../assignment.md)
diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 52453de5a..44994663f 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -1,8 +1,9 @@
 # 逻辑回归预测分类
 
 ![逻辑与线性回归信息图](../images/logistic-linear.png)
-> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## 介绍
 
@@ -38,7 +39,8 @@
 逻辑回归不提供与线性回归相同的功能。前者提供关于二元类别（“橙色或非橙色”）的预测，而后者能够预测连续值，例如，给定南瓜的起源和收获时间，_其价格将上涨多少_。
 
 ![南瓜分类模型](../images/pumpkin-classifier.png)
-> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
+> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
 ### 其他分类
 
 还有其他类型的逻辑回归，包括多项和有序：
@@ -47,7 +49,7 @@
 - **有序**，涉及有序类别，如果我们想对我们的结果进行逻辑排序非常有用，例如我们的南瓜按有限数量的大小（mini、sm、med、lg、xl、xxl）排序。
 
 ![多项式与有序回归](../images/multinomial-ordinal.png)
-> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
+> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ### 仍然是线性的
 
@@ -89,11 +91,11 @@
 
 ### 可视化 - 并列网格
 
-到现在为止，你已经再次使用南瓜数据加载了[starter notebook](./notebook.ipynb)并对其进行了清理，以保留包含一些变量（包括`Color`）的数据集。让我们使用不同的库来可视化notebook中的数据帧：[Seaborn](https://seaborn.pydata.org/index.html)，它是基于我们之前使用的Matplotlib构建的。
+到现在为止，你已经再次使用南瓜数据加载了 [starter notebook](./notebook.ipynb) 并对其进行了清理，以保留包含一些变量（包括 `Color`）的数据集。让我们使用不同的库来可视化 notebook 中的数据帧：[Seaborn](https://seaborn.pydata.org/index.html)，它是基于我们之前使用的 Matplotlib 构建的。
 
-Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可以比较并列网格中每个点的数据分布。
+Seaborn 提供了一些巧妙的方法来可视化你的数据。例如，你可以比较并列网格中每个点的数据分布。
 
-1. 通过实例化一个`PairGrid`，使用我们的南瓜数据`new_pumpkins`，然后调用`map()`来创建这样一个网格：
+1. 通过实例化一个 `PairGrid`，使用我们的南瓜数据 `new_pumpkins`，然后调用 `map()` 来创建这样一个网格：
 
     ```python
     import seaborn as sns
@@ -112,7 +114,7 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
 
 由于颜色是一个二元类别（橙色或非橙色），它被称为“分类数据”，需要一种更[专业的方法](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar)来可视化。还有其他方法可以可视化此类别与其他变量的关系。
 
-你可以使用Seaborn图并列可视化变量。
+你可以使用 Seaborn 图并列可视化变量。
 
 1. 尝试使用“分类散点”图来显示值的分布：
 
@@ -120,22 +122,22 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
     sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
     ```
 
-    ![分类散点图可视化数据](images/swarm.png)
+    ![分类散点图可视化数据](../images/swarm.png)
 
 ### 小提琴图
 
 “小提琴”类型的图很有用，因为你可以轻松地可视化两个类别中数据的分布方式。小提琴图不适用于较小的数据集，因为分布显示得更“平滑”。
 
-1. 作为参数`x=Color`、`kind="violin"`并调用`catplot()`：
+1. 作为参数 `x=Color`、`kind="violin"` 并调用 `catplot()`：
 
     ```python
     sns.catplot(x="Color", y="Item Size",
                 kind="violin", data=new_pumpkins)
     ```
 
-    ![小提琴图](images/violin.png)
+    ![小提琴图](../images/violin.png)
 
-    ✅ 尝试使用其他变量创建此图和其他Seaborn图。
+    ✅ 尝试使用其他变量创建此图和其他 Seaborn 图。
 
 现在我们已经了解了颜色的二元类别与更大的尺寸组之间的关系，让我们探索逻辑回归来确定给定南瓜的可能颜色。
 
@@ -145,13 +147,13 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
 >
 > ![逻辑函数](../images/sigmoid.png)
 >
-> 其中sigmoid的中点位于x的0点，L是曲线的最大值，k是曲线的陡度。如果函数的结果大于0.5，则所讨论的标签将被赋予二进制选择的类“1”。否则，它将被分类为“0”。
+> 其中 sigmoid 的中点位于 x 的 0 点，L 是曲线的最大值，k 是曲线的陡度。如果函数的结果大于 0.5，则所讨论的标签将被赋予二进制选择的类“1”。否则，它将被分类为“0”。
 
 ## 建立你的模型
 
-在Scikit-learn中构建模型来查找这些二元分类非常简单。
+在 Scikit-learn 中构建模型来查找这些二元分类非常简单。
 
-1. 选择要在分类模型中使用的变量，并调用`train_test_split()`拆分训练集和测试集：
+1. 选择要在分类模型中使用的变量，并调用 `train_test_split()` 拆分训练集和测试集：
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -165,7 +167,7 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
     
     ```
 
-2. 现在你可以训练你的模型，用你的训练数据调用`fit()`，并打印出它的结果：
+2. 现在你可以训练你的模型，用你的训练数据调用 `fit()`，并打印出它的结果：
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -181,7 +183,7 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
     print('Accuracy: ', accuracy_score(y_test, predictions))
     ```
 
-    看看你的模型的记分板。考虑到你只有大约1000行数据，这还不错：
+    看看你的模型的记分板。考虑到你只有大约 1000 行数据，这还不错：
 
     ```output
                        precision    recall  f1-score   support
@@ -228,19 +230,15 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
 - 如果你的模型将某物预测为南瓜并且它实际上属于“非南瓜”类别，我们将其称为假阴性，由左下角的数字显示。
 - 如果你的模型预测某物不是南瓜，并且它实际上属于“非南瓜”类别，我们将其称为真阴性，如右下角的数字所示。
 
-![混淆矩阵](../images/confusion-matrix.png)
-
-> 作者[Jen Looper](https://twitter.com/jenlooper)
-
 正如你可能已经猜到的那样，最好有更多的真阳性和真阴性以及较少的假阳性和假阴性，这意味着模型性能更好。
 
 ✅ Q：根据混淆矩阵，模型怎么样？ A：还不错；有很多真阳性，但也有一些假阴性。
 
 让我们借助混淆矩阵对TP/TN和FP/FN的映射，重新审视一下我们之前看到的术语：
 
-🎓 准确率：TP/（TP+FN）检索实例中相关实例的分数（例如，哪些标签标记得很好）
+🎓 准确率：TP/(TP + FP) 检索实例中相关实例的分数（例如，哪些标签标记得很好）
 
-🎓 召回率: TP/(TP + FP) 检索到的相关实例的比例，无论是否标记良好
+🎓 召回率: TP/(TP + FN) 检索到的相关实例的比例，无论是否标记良好
 
 🎓 F1分数: (2 * 准确率 * 召回率)/(准确率 + 召回率) 准确率和召回率的加权平均值，最好为1，最差为0
 
@@ -253,9 +251,10 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
 🎓 加权平均值：计算每个标签的平均指标，通过按支持度（每个标签的真实实例数）加权来考虑标签不平衡。
 
 ✅ 如果你想让你的模型减少假阴性的数量，你能想出应该关注哪个指标吗？
-## 可视化该模型的ROC曲线
 
-这不是一个糟糕的模型；它的准确率在80%范围内，因此理想情况下，你可以使用它来预测给定一组变量的南瓜颜色。
+## 可视化该模型的 ROC 曲线
+
+这不是一个糟糕的模型；它的准确率在 80% 范围内，因此理想情况下，你可以使用它来预测给定一组变量的南瓜颜色。
 
 让我们再做一个可视化来查看所谓的“ROC”分数
 
@@ -268,30 +267,34 @@ fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
 sns.lineplot([0, 1], [0, 1])
 sns.lineplot(fpr, tpr)
 ```
-再次使用Seaborn，绘制模型的[接收操作特性](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc)或ROC。 ROC曲线通常用于根据分类器的真假阳性来了解分类器的输出。“ROC曲线通常具有Y轴上的真阳性率和X轴上的假阳性率。” 因此，曲线的陡度以及中点线与曲线之间的空间很重要：你需要一条快速向上并越过直线的曲线。在我们的例子中，一开始就有误报，然后这条线正确地向上和重复：
+
+再次使用 Seaborn，绘制模型的[接收操作特性](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc)或 ROC。 ROC 曲线通常用于根据分类器的真假阳性来了解分类器的输出。“ROC 曲线通常具有 Y 轴上的真阳性率和 X 轴上的假阳性率。” 因此，曲线的陡度以及中点线与曲线之间的空间很重要：你需要一条快速向上并越过直线的曲线。在我们的例子中，一开始就有误报，然后这条线正确地向上和重复：
 
 ![ROC](../images/ROC.png)
 
-最后，使用Scikit-learn的[`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score)来计算实际“曲线下面积”（AUC）：
+最后，使用 Scikit-learn 的[`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) 来计算实际“曲线下面积”（AUC）：
 
 ```python
 auc = roc_auc_score(y_test,y_scores[:,1])
 print(auc)
 ```
-结果是`0.6976998904709748`。 鉴于AUC的范围从0到1，你需要一个高分，因为预测100%正确的模型的AUC为1；在这种情况下，模型_相当不错_。
+
+结果是 `0.6976998904709748`。 鉴于 AUC 的范围从 0 到 1，你需要一个高分，因为预测 100% 正确的模型的 AUC 为 1；在这种情况下，模型_相当不错_。
 
 在以后的分类课程中，你将学习如何迭代以提高模型的分数。但是现在，恭喜！你已经完成了这些回归课程！
+
 ---
+
 ## 🚀挑战
 
-关于逻辑回归，还有很多东西需要解开！但最好的学习方法是实验。找到适合此类分析的数据集并用它构建模型。你学到了什么？小贴士：尝试[Kaggle](https://kaggle.com)获取有趣的数据集。
+关于逻辑回归，还有很多东西需要解开！但最好的学习方法是实验。找到适合此类分析的数据集并用它构建模型。你学到了什么？小贴士：尝试 [Kaggle](https://kaggle.com) 获取有趣的数据集。
 
-## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## 复习与自学
 
 阅读[斯坦福大学的这篇论文](https://web.stanford.edu/~jurafsky/slp3/5.pdf)的前几页关于逻辑回归的一些实际应用。想想那些更适合于我们目前所研究的一种或另一种类型的回归任务的任务。什么最有效？
 
-## 任务 
+## 任务
 
-[重试此回归](../assignment.md)
+[重试此回归](./assignment.zh-cn.md)
diff --git a/2-Regression/4-Logistic/translations/assignment.it.md b/2-Regression/4-Logistic/translations/assignment.it.md
new file mode 100644
index 000000000..7b9b20161
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/assignment.it.md
@@ -0,0 +1,10 @@
+# Riprovare un po' di Regressione
+
+## Istruzioni
+
+Nella lezione è stato usato un sottoinsieme dei dati della zucca. Ora si torna ai dati originali e si prova a usarli tutti, puliti e standardizzati, per costruire un modello di regressione logistica.
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
+|          | Un notebook viene presentato con un modello ben spiegato con buone prestazioni | Un notebook viene presentato con un modello dalle prestazioni minime | Un notebook viene presentato con un modello con scarse  o nessuna prestazione |
diff --git a/2-Regression/4-Logistic/translations/assignment.ja.md b/2-Regression/4-Logistic/translations/assignment.ja.md
new file mode 100644
index 000000000..6c838173b
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 回帰に再挑戦する
+
+## 課題の指示
+
+レッスンでは、カボチャのデータのサブセットを使用しました。今度は、元のデータに戻って、ロジスティック回帰モデルを構築するために、整形して標準化したデータをすべて使ってみましょう。
+
+## ルーブリック
+
+| 指標 | 模範的                                                               | 適切                                                     | 要改善                                           |
+| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
+|          | 説明がわかりやすく、性能の良いモデルが含まれたノートブック| 最小限の性能しか発揮できないモデルが含まれたノートブック | 性能の劣るモデルや、何もないモデルが含まれたノートブック |
diff --git a/2-Regression/4-Logistic/translations/assignment.zh-cn.md b/2-Regression/4-Logistic/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..9d8849679
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 再探回归模型
+
+## 说明
+
+在这节课中，你使用了 pumpkin 数据集的子集。现在，让我们回到原始数据，并尝试使用所有数据。经过了数据清理和标准化，建立一个逻辑回归模型。
+
+## 评判标准
+
+| 标准 | 优秀                                                               | 中规中矩                                                     | 仍需努力                                           |
+| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
+|          | 用 notebook 呈现了一个解释性和性能良好的模型 | 用 notebook 呈现了一个性能一般的模型 | 用 notebook 呈现了一个性能差的模型或根本没有模型 |
diff --git a/2-Regression/README.md b/2-Regression/README.md
index 4446bc2c8..a8eeec748 100644
--- a/2-Regression/README.md
+++ b/2-Regression/README.md
@@ -8,11 +8,14 @@ In North America, pumpkins are often carved into scary faces for Halloween. Let'
   
 ## What you will learn
 
+[![Introduction to Regression](https://img.youtube.com/vi/5QnJtDad4iQ/0.jpg)](https://youtu.be/5QnJtDad4iQ "Regression Introduction video - Click to Watch!")
+> 🎥 Click the image above for a quick introduction video to this lesson
+
 The lessons in this section cover types of regression in the context of machine learning. Regression models can help determine the _relationship_ between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes data points.
 
 In this series of lessons, you'll discover the difference between linear vs. logistic regression, and when you should use one or the other.
 
-In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter.
+In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio Code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter.
 
 > There are useful low-code tools that can help you learn about working with regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 
diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md
new file mode 100644
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--- /dev/null
+++ b/2-Regression/translations/README.es.md
@@ -0,0 +1,33 @@
+# Modelos de regresión para el machine learning
+## Tema regional: Modelos de regresión para los precios de las calabazas en América del Norte 🎃
+
+En América del Norte, las calabazas se tallan a menudo con caras aterradoras para Halloween. ¡Descubramos más sobre estas fascinantes verduras!
+
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
+> Foto de <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> en <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## Lo que vas a aprender
+
+Las lecciones de esta sección cubren los tipos de regressión en el contexto de machine learning. Los modelos de regresión pueden ayudar a determinar la _relación_ entre variables. Este tipo de modelos puede predecir valores como la longitud, la temperatura o la edad, descubriendo así relaciones entre variables a medida que analiza puntos de datos.
+
+En esta serie de lecciones, descubrirá la diferencia entre la regresión lineal y la logística, y cuándo debe usar una u otra.
+
+En este grupo de lecciones, se preparará para comenzar las tares de machine learning, incluida la configuración de Visual Studio Code para manejar los cuadernos, el entorno común para los científicos de datos. Descubrirá Scikit-learn, una librería para machine learning, y creará sus primeros modelos, centrándose en los modelos de Regresión en este capítulo.
+
+> Existen herramientas útiles _low-code_ que pueden ayudarlo a aprender a trabjar con modelos de regresión. Pruebe [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+### Lecciones
+
+1. [Herramientas del oficio](1-Tools/README.md)
+2. [Gestión de datos](2-Data/README.md)
+3. [Regresión lineal y polinomial](3-Linear/README.md)
+4. [Regresión logística](4-Logistic/README.md)
+
+---
+### Créditos
+
+"ML con regresión" fue escrito con ♥️ por [Jen Looper](https://twitter.com/jenlooper)
+
+♥️ Los contribuyentes del cuestionario incluyen: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) y [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+El _dataset_ de calabaza es sugerido por [este proyecto en Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) y sus datos provienen de [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribuido por el Departamento de Agricultura de los Estados Unidos. Hemos agragado algunos puntos alrededor color basados en la variedad para normalizar la distribución. Estos datos son de dominio público.
diff --git a/2-Regression/translations/README.it.md b/2-Regression/translations/README.it.md
new file mode 100644
index 000000000..c6e957f9e
--- /dev/null
+++ b/2-Regression/translations/README.it.md
@@ -0,0 +1,34 @@
+# Modelli di regressione per machine learning
+
+## Argomento regionale: modelli di Regressione per i prezzi della zucca in Nord America 🎃
+
+In Nord America, le zucche sono spesso intagliate in facce spaventose per Halloween. Si scoprirà di più su queste affascinanti verdure!
+
+![jack-o-lantern](../images/jack-o-lanterns.jpg)
+> Foto di <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> su <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Cosa si imparerà
+
+Le lezioni in questa sezione riguardano i tipi di regressione nel contesto di machine learning. I modelli di regressione possono aiutare a determinare la _relazione_ tra le variabili. Questo tipo di modello può prevedere valori come lunghezza, temperatura o età, scoprendo così le relazioni tra le variabili mentre analizza i punti dati.
+
+In questa serie di lezioni si scoprirà la differenza tra regressione lineare e regressione logistica e quando si dovrebbe usare l'una o l'altra.
+
+In questo gruppo di lezioni si imposterà una configurazione per iniziare le attività di machine learning, inclusa la configurazione di Visual Studio Code per gestire i notebook, l'ambiente comune per i data scientist. Si scoprirà Scikit-learn, una libreria per machine learning, e si creeranno i primi modelli, concentrandosi in questo capitolo sui modelli di Regressione.
+
+> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure Machine Learning per questa attività](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+### Lezioni
+
+1. [Gli Attrezzi Necessari](../1-Tools/translations/README.it.md)
+2. [Gestione dati](../2-Data/translations/README.it.md)
+3. [Regressione lineare e polinomiale](../3-Linear/translations/README.it.md)
+4. [Regressione logistica](../4-Logistic/translations/README.it.md)
+
+---
+### Crediti
+
+"ML con regressione" scritto con ♥️ da [Jen Looper](https://twitter.com/jenlooper)
+
+♥️ I collaboratori del quiz includono: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) e [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+L'insieme di dati relativi alla zucca è suggerito da [questo progetto su](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) Kaggle e i suoi dati provengono dai [Rapporti Standard sui Mercati Terminali delle Colture Speciali](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)  distribuiti dal Dipartimento dell'Agricoltura degli Stati Uniti. Sono stati aggiunti alcuni punti intorno al colore in base alla varietà per normalizzare la distribuzione. Questi dati sono di pubblico dominio.
diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
new file mode 100644
index 000000000..50d8294b4
--- /dev/null
+++ b/2-Regression/translations/README.ja.md
@@ -0,0 +1,32 @@
+# 機械学習のための回帰モデル
+## トピック: 北米のカボチャ価格に関する回帰モデル 🎃
+
+北米では、ハロウィンのためにカボチャはよく怖い顔に彫られています。そんな魅力的な野菜についてもっと知りましょう！
+
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
+> <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a>によって<a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>に投稿された写真
+
+## 今回学ぶこと
+この章のレッスンでは、機械学習の文脈における回帰の種類について説明します。回帰モデルは変数間の"関係"を決定するのに役立ちます。このタイプのモデルは、長さ、温度、年齢などの値を予測し、データポイントの分析をすることで変数間の関係性を明らかにします。
+
+今回のレッスンでは、線形回帰とロジスティック回帰の違いやどのように使い分けるかを説明します。
+
+データサイエンティストの共通開発環境であるノートブックを管理するためのVisual Studio Codeの構成や機械学習のタスクを開始するための準備を行います。また、機械学習用のライブラリであるScikit-learnを利用し最初のモデルを構築します。この章では回帰モデルに焦点を当てます。
+
+> 回帰モデルを学習するのに役立つローコードツールがあります。ぜひ[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)を使ってみてください。
+
+### レッスン
+
+1. [商売道具](../1-Tools/translations/README.ja.md)
+2. [データ管理](../2-Data/translations/README.ja.md)
+3. [線形回帰と多項式回帰](../3-Linear/translations/README.ja.md)
+4. [ロジスティック回帰](../4-Logistic/translations/README.ja.md)
+
+---
+### クレジット
+
+"機械学習と回帰"は、[Jen Looper](https://twitter.com/jenlooper)によって制作されました。
+
+クイズの貢献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)と[Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+pumpkin datasetは、[こちらのKaggleプロジェクト](https://www.kaggle.com/usda/a-year-of-pumpkin-prices)で提案されています。このデータは、アメリカ合衆国農務省が配布している[Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)が元になっています。私たちは、分布を正規化するために多様性を元に色についていくつか追加を行っています。このデータはパブリックドメインです。
diff --git a/2-Regression/translations/README.ko.md b/2-Regression/translations/README.ko.md
new file mode 100644
index 000000000..81a16ec3a
--- /dev/null
+++ b/2-Regression/translations/README.ko.md
@@ -0,0 +1,35 @@
+# 머신러닝을 위한 Regression 모델
+
+## 지역 토픽: 북미의 호박 가격을 위한 Regression 모델 🎃
+
+북미에서, Halloween을 위해서 호박을 소름돋게 무서운 얼굴로 조각하는 경우가 자주 있습니다. 매혹적인 채소에 대하여 찾아봅시다!
+
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
+> Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## 무엇을 배우나요
+
+이 섹션의 강의는 머신러닝의 컨텍스트에서 regression 타입을 다루게 됩니다. Regression 모델은 변수 사이 _relationship_ 을 결정하도록 도울 수 있습니다. 모델의 타입은 길이, 온도, 또는 나이와 같은 값을 예측할 수 있으므로, 데이터 포인트를 분석하는 순간 변수 사이의 관계를 알 수 있습니다.
+
+이 강의의 시리즈에서, linear와 logistic regression 간의 다른 점을 찾을 수 있고, 둘 중 하나를 언제 사용해야 될 지 알 수 있습니다.
+
+이 강의의 그룹에서, 데이터 사이언티스트를 위한 일반적 환경의, 노트북을 관리할 Visual Studio code 구성을 포함해서, 머신러닝 작업을 시작하도록 맞춥니다. 머신러닝을 위한 라이브러리인, Scikit-learn을 찾고, 이 챕터의 Regression 모델에 초점을 맞추어, 첫 모델을 만들 예정입니다.
+
+> Regression 모델을 작업할 때 배울 수 있는 유용한 low-code 도구가 있습니다. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)를 시도해보세요.
+
+### Lessons
+
+1. [무역의 도구](../1-Tools/translations/README.ko.md)
+2. [데이터 관리](../2-Data/translations/README.ko.md)
+3. [Linear와 polynomial regression](../3-Linear/translations/README.ko.md)
+4. [Logistic regression](../4-Logistic/translations/README.ko.md)
+
+---
+### 크레딧
+
+"ML with regression" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper)
+
+♥️ Quiz contributors include: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) and [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+호박 데이터셋은 [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices)에서 제안되었고 데이터는 United States Department of Agriculture에서 배포한 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)에 기반합니다. 분포를 정규화하기 위하여 다양성을 기반으로 색상을 주변에 몇 포인트 더 했습니다. 데이터는 공개 도메인에 존재합니다.
+ 
diff --git a/2-Regression/translations/README.zh-cn.md b/2-Regression/translations/README.zh-cn.md
new file mode 100644
index 000000000..f7c511e62
--- /dev/null
+++ b/2-Regression/translations/README.zh-cn.md
@@ -0,0 +1,34 @@
+# 机器学习中的回归模型
+## 本节主题： 北美南瓜价格的回归模型 🎃
+
+在北美，南瓜经常在万圣节被刻上吓人的鬼脸。让我们来深入研究一下这种奇妙的蔬菜
+
+![jack-o-lantern](../images/jack-o-lanterns.jpg)
+> Foto oleh <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> di <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## 你会学到什么
+
+这节的课程包括机器学习领域中的多种回归模型。回归模型可以明确多种变量间的_关系_。这种模型可以用来预测类似长度、温度和年龄之类的值， 通过分析数据点来揭示变量之间的关系。
+
+在本节的一系列课程中，你会学到线性回归和逻辑回归之间的区别，并且你将知道对于特定问题如何在这两种模型中进行选择
+
+在这组课程中，你会准备好包括为管理笔记而设置VS Code、配置数据科学家常用的环境等机器学习的初始任务。你会开始上手Scikit-learn学习项目（一个机器学习的百科），并且你会以回归模型为主构建起你的第一种机器学习模型
+
+> 这里有一些代码难度较低但很有用的工具可以帮助你学习使用回归模型。 试一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+
+### Lessons
+
+1. [交易的工具](../1-Tools/translations/README.zh-cn.md)
+2. [管理数据](../2-Data/translations/README.zh-cn.md)
+3. [线性和多项式回归](../3-Linear/translations/README.zh-cn.md)
+4. [逻辑回归](../4-Logistic/translations/README.zh-cn.md)
+
+---
+### Credits
+
+"机器学习中的回归" 由[Jen Looper](https://twitter.com/jenlooper)♥️ 撰写
+
+♥️ 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+南瓜数据集受此启发 [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数据源自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根据种类添加了围绕颜色的一些数据点。这些数据处在公共的域名上。
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 6150aece7..eb124eb53 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -1,6 +1,6 @@
 # Build a Web App to use a ML Model
 
-In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from [NUFORC's database](https://www.nuforc.org).
+In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from NUFORC's database.
 
 You will learn:
 
@@ -11,7 +11,7 @@ We will continue our use of notebooks to clean data and train our model, but you
 
 To do this, you need to build a web app using Flask.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
 
 ## Building an app
 
@@ -19,15 +19,15 @@ There are several ways to build web apps to consume machine learning models. You
 
 ### Considerations
 
-There are many questions you need to ask: 
+There are many questions you need to ask:
 
 - **Is it a web app or a mobile app?** If you are building a mobile app or need to use the model in an IoT context, you could use [TensorFlow Lite](https://www.tensorflow.org/lite/) and use the model in an Android or iOS app.
-- **Where will the model reside**? In the cloud or locally?
-- **Offline support**. Does the app have to work offline?
+- **Where will the model reside?** In the cloud or locally?
+- **Offline support.** Does the app have to work offline?
 - **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
-   - **Using Tensor flow**. If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).  
-   - **Using PyTorch**.  If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
-   - **Using Lobe.ai or Azure Custom vision**. If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
+    - **Using Tensor flow.** If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).
+    - **Using PyTorch.** If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
+    - **Using Lobe.ai or Azure Custom Vision.** If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
 
 You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.
 
@@ -37,7 +37,7 @@ For our purposes, since we have been working with Python-based notebooks, let's
 
 For this task, you need two tools: Flask and Pickle, both of which run on Python.
 
-✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages.  Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
+✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
 
 ✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
 
@@ -45,12 +45,12 @@ For this task, you need two tools: Flask and Pickle, both of which run on Python
 
 In this lesson you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:
 
-- **Long example description**. "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
-- **Short example description**. "the lights chased us".
+- **Long example description.** "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
+- **Short example description.** "the lights chased us".
 
-The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`. 
+The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`.
 
-In the blank [notebook](notebook.ipynb) included in this lesson: 
+In the blank [notebook](notebook.ipynb) included in this lesson:
 
 1. import `pandas`, `matplotlib`, and `numpy` as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
 
@@ -58,7 +58,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     import pandas as pd
     import numpy as np
     
-    ufos = pd.read_csv('../data/ufos.csv')
+    ufos = pd.read_csv('./data/ufos.csv')
     ufos.head()
     ```
 
@@ -82,7 +82,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
 
 1. Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:
 
-   ✅ LabelEncoder encodes data alphabetically
+    ✅ LabelEncoder encodes data alphabetically
 
     ```python
     from sklearn.preprocessing import LabelEncoder
@@ -96,16 +96,16 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
 
     ```output
     	Seconds	Country	Latitude	Longitude
-    2	20.0	3	    53.200000	-2.916667
-    3	20.0	4	    28.978333	-96.645833
-    14	30.0	4	    35.823889	-80.253611
-    23	60.0	4	    45.582778	-122.352222
-    24	3.0	    3	    51.783333	-0.783333
+    2	20.0	3		53.200000	-2.916667
+    3	20.0	4		28.978333	-96.645833
+    14	30.0	4		35.823889	-80.253611
+    23	60.0	4		45.582778	-122.352222
+    24	3.0		3		51.783333	-0.783333
     ```
 
 ## Exercise - build your model
 
-Now you can get ready to train a model by diving the data into the training and testing group. 
+Now you can get ready to train a model by dividing the data into the training and testing group.
 
 1. Select the three features you want to train on as your X vector, and the y vector will be the `Country`. You want to be able to input `Seconds`, `Latitude` and `Longitude` and get a country id to return.
 
@@ -123,7 +123,7 @@ Now you can get ready to train a model by diving the data into the training and
 1. Train your model using logistic regression:
 
     ```python
-    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.metrics import accuracy_score, classification_report
     from sklearn.linear_model import LogisticRegression
     model = LogisticRegression()
     model.fit(X_train, y_train)
@@ -159,20 +159,20 @@ Now you can build a Flask app to call your model and return similar results, but
 
 1. Start by creating a folder called **web-app** next to the _notebook.ipynb_ file where your _ufo-model.pkl_ file resides.
 
-1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates`**. You should now have the following files and directories:
+1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates**. You should now have the following files and directories:
 
     ```output
     web-app/
       static/
         css/
-        templates/
+      templates/
     notebook.ipynb
     ufo-model.pkl
-    ``` 
+    ```
 
-   ✅ Refer to the solution folder for a view of the finished app
+    ✅ Refer to the solution folder for a view of the finished app
 
-1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines: 
+1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines:
 
     ```text
     scikit-learn
@@ -183,23 +183,23 @@ Now you can build a Flask app to call your model and return similar results, but
 
 1. Now, run this file by navigating to _web-app_:
 
-   ```bash
-   cd web-app
-   ```
+    ```bash
+    cd web-app
+    ```
 
-1. In your terminal type `pip install`, to install the libraries listed in _reuirements.txt_:
+1. In your terminal type `pip install`, to install the libraries listed in _requirements.txt_:
 
-   ```bash
-   pip install -r requirements.txt
-   ```
+    ```bash
+    pip install -r requirements.txt
+    ```
 
 1. Now, you're ready to create three more files to finish the app:
 
-    1. Create **app.py** in the root
+    1. Create **app.py** in the root.
     2. Create **index.html** in _templates_ directory.
     3. Create **styles.css** in _static/css_ directory.
 
-1. Build out the _styles.css__ file with a few styles:
+1. Build out the _styles.css_ file with a few styles:
 
     ```css
     body {
@@ -238,33 +238,33 @@ Now you can build a Flask app to call your model and return similar results, but
     ```html
     <!DOCTYPE html>
     <html>
-    <head>
-      <meta charset="UTF-8">
-      <title>🛸 UFO Appearance Prediction! 👽</title>
-      <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}"> 
-    </head>
+      <head>
+        <meta charset="UTF-8">
+        <title>🛸 UFO Appearance Prediction! 👽</title>
+        <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
+      </head>
     
-    <body>
-     <div class="grid">
+      <body>
+        <div class="grid">
     
-      <div class="box">
+          <div class="box">
     
-      <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+            <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
     
-        <form action="{{ url_for('predict')}}" method="post">
-        	<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
-          <input type="text" name="latitude" placeholder="Latitude" required="required" />
-    		  <input type="text" name="longitude" placeholder="Longitude" required="required" />
-          <button type="submit" class="btn">Predict country where the UFO is seen</button>
-        </form>
+            <form action="{{ url_for('predict')}}" method="post">
+              <input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+              <input type="text" name="latitude" placeholder="Latitude" required="required" />
+              <input type="text" name="longitude" placeholder="Longitude" required="required" />
+              <button type="submit" class="btn">Predict country where the UFO is seen</button>
+            </form>
     
-      
-       <p>{{ prediction_text }}</p>
+            <p>{{ prediction_text }}</p>
     
-     </div>
-    </div>
+          </div>
     
-    </body>
+        </div>
+    
+      </body>
     </html>
     ```
 
@@ -309,7 +309,7 @@ Now you can build a Flask app to call your model and return similar results, but
         app.run(debug=True)
     ```
 
-   > 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
+    > 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
 
 If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
 
@@ -324,24 +324,22 @@ On the `/predict` route, several things happen when the form is posted:
 1. The form variables are gathered and converted to a numpy array. They are then sent to the model and a prediction is returned.
 2. The Countries that we want displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
 
-Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction. 
+Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.
 
 In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
 
 ---
 
-## 🚀 Challenge: 
+## 🚀 Challenge
 
 Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
 
 ## Review & Self Study
 
 There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.
 
-## Assignment 
+## Assignment
 
 [Try a different model](assignment.md)
-
-
diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md
new file mode 100644
index 000000000..2b167c880
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/README.it.md
@@ -0,0 +1,347 @@
+# Creare un'app web per utilizzare un modello ML
+
+In questa lezione, si addestrerà un modello ML su un insieme di dati fuori dal mondo: _avvistamenti di UFO nel secolo scorso_, provenienti dal [database di NUFORC](https://www.nuforc.org).
+
+Si imparerà:
+
+- Come serializzare/deserializzare un modello addestrato
+- Come usare quel modello in un'app Flask
+
+Si continuerà a utilizzare il notebook per pulire i dati e addestrare il modello, ma si può fare un ulteriore passo avanti nel processo esplorando l'utilizzo del modello direttamente in un'app web.
+
+Per fare ciò, è necessario creare un'app Web utilizzando Flask.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/?loc=it)
+
+## Costruire un'app
+
+Esistono diversi modi per creare app Web per utilizzare modelli di machine learning. L'architettura web può influenzare il modo in cui il modello viene addestrato. Si immagini di lavorare in un'azienda nella quale il gruppo di data science ha addestrato un modello che va utilizzato in un'app.
+
+### Considerazioni
+
+Ci sono molte domande da porsi:
+
+- **È un'app web o un'app su dispositivo mobile?** Se si sta creando un'app su dispositivo mobile o si deve usare il modello in un contesto IoT, ci si può avvalere [di TensorFlow Lite](https://www.tensorflow.org/lite/) e usare il modello in un'app Android o iOS.
+- **Dove risiederà il modello**? E' utilizzato in cloud o in locale?
+- **Supporto offline**. L'app deve funzionare offline?
+- **Quale tecnologia è stata utilizzata per addestrare il modello?** La tecnologia scelta può influenzare gli strumenti che è necessario utilizzare.
+   - **Utilizzare** TensorFlow. Se si sta addestrando un modello utilizzando TensorFlow, ad esempio, tale ecosistema offre la possibilità di convertire un modello TensorFlow per l'utilizzo in un'app Web utilizzando [TensorFlow.js](https://www.tensorflow.org/js/).
+   - **Utilizzare PyTorch**. Se si sta costruendo un modello utilizzando una libreria come PyTorch[,](https://pytorch.org/) si ha la possibilità di esportarlo in formato [ONNX](https://onnx.ai/) ( Open Neural Network Exchange) per l'utilizzo in app Web JavaScript che possono utilizzare il [motore di esecuzione Onnx](https://www.onnxruntime.ai/). Questa opzione verrà esplorata in una lezione futura per un modello addestrato da Scikit-learn
+   - **Utilizzo di Lobe.ai o Azure Custom vision**. Se si sta usando un sistema ML SaaS (Software as a Service) come [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) per addestrare un modello, questo tipo di software fornisce modi per esportare il modello per molte piattaforme, inclusa la creazione di un'API su misura da interrogare nel cloud dalla propria applicazione online.
+
+Si ha anche l'opportunità di creare un'intera app Web Flask in grado di addestrare il modello stesso in un browser Web. Questo può essere fatto anche usando TensorFlow.js in un contesto JavaScript.
+
+Per questo scopo, poiché si è lavorato con i notebook basati su Python, verranno esplorati i passaggi necessari per esportare un modello addestrato da tale notebook in un formato leggibile da un'app Web creata in Python.
+
+## Strumenti
+
+Per questa attività sono necessari due strumenti: Flask e Pickle, entrambi eseguiti su Python.
+
+✅ Cos'è [Flask](https://palletsprojects.com/p/flask/)? Definito come un "micro-framework" dai suoi creatori, Flask fornisce le funzionalità di base dei framework web utilizzando Python e un motore di template per creare pagine web. Si dia un'occhiata a [questo modulo di apprendimento](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) per esercitarsi a sviluppare con Flask.
+
+✅ Cos'è [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 è un modulo Python che serializza e de-serializza la struttura di un oggetto Python. Quando si utilizza pickle in un modello, si serializza o si appiattisce la sua struttura per l'uso sul web. Cautela: pickle non è intrinsecamente sicuro, quindi si faccia  attenzione se viene chiesto di de-serializzare un file. Un file creato con pickle ha il suffisso `.pkl`.
+
+## Esercizio: pulire i dati
+
+In questa lezione verranno utilizzati i dati di 80.000 avvistamenti UFO, raccolti dal Centro Nazionale per gli Avvistamenti di UFO [NUFORC](https://nuforc.org) (The National UFO Reporting Center). Questi dati hanno alcune descrizioni interessanti di avvistamenti UFO, ad esempio:
+
+- **Descrizione di esempio lunga**. "Un uomo emerge da un raggio di luce che di notte brilla su un campo erboso e corre verso il parcheggio della Texas Instruments".
+- **Descrizione di esempio breve**. "le luci ci hanno inseguito".
+
+Il foglio di calcolo [ufo.csv](../data/ufos.csv) include colonne su città (`city`), stato (`state`) e nazione (`country`) in cui è avvenuto l'avvistamento, la forma (`shape`) dell'oggetto e la sua latitudine (`latitude`) e longitudine (`longitude`).
+
+Nel [notebook](../notebook.ipynb) vuoto incluso in questa lezione:
+
+1. importare `pandas`, `matplotlib` e `numpy` come fatto nelle lezioni precedenti e importare il foglio di calcolo ufo.csv. Si può dare un'occhiata a un insieme di dati campione:
+
+   ```python
+   import pandas as pd
+   import numpy as np
+
+   ufos = pd.read_csv('../data/ufos.csv')
+   ufos.head()
+   ```
+
+1. Convertire i dati ufos in un piccolo dataframe con nuove intestazioni Controllare i valori univoci nel campo `Country` .
+
+   ```python
+   ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
+
+   ufos.Country.unique()
+   ```
+
+1. Ora si può ridurre la quantità di dati da gestire eliminando qualsiasi valore nullo e importando solo avvistamenti tra 1-60 secondi:
+
+   ```python
+   ufos.dropna(inplace=True)
+
+   ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
+
+   ufos.info()
+   ```
+
+1. Importare la libreria `LabelEncoder` di Scikit-learn per convertire i valori di testo per le nazioni in un numero:
+
+   ✅ LabelEncoder codifica i dati in ordine alfabetico
+
+   ```python
+   from sklearn.preprocessing import LabelEncoder
+
+   ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
+
+   ufos.head()
+   ```
+
+   I dati dovrebbero assomigliare a questo:
+
+   ```output
+   	Seconds	Country	Latitude	Longitude
+   2	20.0	3	    53.200000	-2.916667
+   3	20.0	4	    28.978333	-96.645833
+   14	30.0	4	    35.823889	-80.253611
+   23	60.0	4	    45.582778	-122.352222
+   24	3.0	    3	    51.783333	-0.783333
+   ```
+
+## Esercizio: costruire il proprio modello
+
+Ora ci si può preparare per addestrare un modello portando i dati nei gruppi di addestramento e test.
+
+1. Selezionare le tre caratteristiche su cui lo si vuole allenare come vettore X mentre il vettore y sarà `Country` Si deve essere in grado di inserire secondi (`Seconds`), latitudine (`Latitude`) e longitudine (`Longitude`) e ottenere un ID nazione da restituire.
+
+   ```python
+   from sklearn.model_selection import train_test_split
+
+   Selected_features = ['Seconds','Latitude','Longitude']
+
+   X = ufos[Selected_features]
+   y = ufos['Country']
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   ```
+
+1. Addestrare il modello usando la regressione logistica:
+
+   ```python
+   from sklearn.metrics import accuracy_score, classification_report
+   from sklearn.linear_model import LogisticRegression
+   model = LogisticRegression()
+   model.fit(X_train, y_train)
+   predictions = model.predict(X_test)
+
+   print(classification_report(y_test, predictions))
+   print('Predicted labels: ', predictions)
+   print('Accuracy: ', accuracy_score(y_test, predictions))
+   ```
+
+La precisione non è male **(circa il 95%)**, non sorprende che `Country` e `Latitude/Longitude` siano correlati.
+
+Il modello creato non è molto rivoluzionario in quanto si dovrebbe essere in grado di dedurre una nazione (`Country`) dalla sua latitudine e longitudine (`Latitude` e `Longitude`), ma è un buon esercizio provare ad allenare dai dati grezzi che sono stati puliti ed esportati, e quindi utilizzare questo modello in una app web.
+
+## Esercizio: usare pickle con il modello
+
+Ora è il momento di utilizzare _pickle_ con il modello! Lo si può fare in poche righe di codice. Una volta che è stato _serializzato con pickle_, caricare il modello e testarlo rispetto a un array di dati di esempio contenente valori per secondi, latitudine e longitudine,
+
+```python
+import pickle
+model_filename = 'ufo-model.pkl'
+pickle.dump(model, open(model_filename,'wb'))
+
+model = pickle.load(open('ufo-model.pkl','rb'))
+print(model.predict([[50,44,-12]]))
+```
+
+Il modello restituisce **"3"**, che è il codice nazione per il Regno Unito. Fantastico! 👽
+
+## Esercizio: creare un'app Flask
+
+Ora si può creare un'app Flask per chiamare il modello e restituire risultati simili, ma in un modo visivamente più gradevole.
+
+1. Iniziare creando una cartella chiamata **web-app** a livello del file _notebook.ipynb_ dove risiede il file _ufo-model.pkl_.
+
+1. In quella cartella creare altre tre cartelle: **static**, con una cartella **css** al suo interno e **templates**. Ora si dovrebbero avere i seguenti file e directory:
+
+   ```output
+   web-app/
+     static/
+       css/
+     templates/
+   notebook.ipynb
+   ufo-model.pkl
+   ```
+
+   ✅ Fare riferimento alla cartella della soluzione per una visualizzazione dell'app finita.
+
+1. Il primo file da creare nella cartella _web-app_ è il file **requirements.txt**. Come _package.json_ in un'app JavaScript, questo file elenca le dipendenze richieste dall'app. In **requirements.txt** aggiungere le righe:
+
+   ```text
+   scikit-learn
+   pandas
+   numpy
+   flask
+   ```
+
+1. Ora, eseguire questo file portandosi su _web-app_:
+
+   ```bash
+   cd web-app
+   ```
+
+1. Aprire una finestra di terminale dove risiede requirements.txt e digitare `pip install`, per installare le librerie elencate in _reuirements.txt_:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+1. Ora si è pronti per creare altri tre file per completare l'app:
+
+   1. Creare **app.py** nella directory radice.
+   2. Creare **index.html** nella directory _templates_.
+   3. Creare **sytles.css** nella directory _static/css_.
+
+1. Inserire nel file _styles.css_ alcuni stili:
+
+   ```css
+   body {
+      width: 100%;
+      height: 100%;
+      font-family: 'Helvetica';
+      background: black;
+      color: #fff;
+      text-align: center;
+      letter-spacing: 1.4px;
+      font-size: 30px;
+   }
+
+   input {
+      min-width: 150px;
+   }
+
+   .grid {
+      width: 300px;
+      border: 1px solid #2d2d2d;
+      display: grid;
+      justify-content: center;
+      margin: 20px auto;
+   }
+
+   .box {
+      color: #fff;
+      background: #2d2d2d;
+      padding: 12px;
+      display: inline-block;
+   }
+   ```
+
+1. Quindi, creare il file _index.html_ :
+
+   ```html
+   <!DOCTYPE html>
+   <html>
+   <head>
+     <meta charset="UTF-8">
+     <title>🛸 UFO Appearance Prediction! 👽</title>
+     <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
+   </head>
+
+   <body>
+    <div class="grid">
+
+     <div class="box">
+
+     <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+
+       <form action="{{ url_for('predict')}}" method="post">
+       	<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+         <input type="text" name="latitude" placeholder="Latitude" required="required" />
+   		  <input type="text" name="longitude" placeholder="Longitude" required="required" />
+         <button type="submit" class="btn">Predict country where the UFO is seen</button>
+       </form>
+
+
+      <p>{{ prediction_text }}</p>
+
+    </div>
+   </div>
+
+   </body>
+   </html>
+   ```
+
+   Dare un'occhiata al template di questo file. Notare la sintassi con le parentesi graffe attorno alle variabili che verranno fornite dall'app, come il testo di previsione: `{{}}`. C'è anche un modulo che invia una previsione alla rotta `/predict`.
+
+   Infine, si è pronti per creare il file python che guida il consumo del modello e la visualizzazione delle previsioni:
+
+1. In `app.py` aggiungere:
+
+   ```python
+   import numpy as np
+   from flask import Flask, request, render_template
+   import pickle
+
+   app = Flask(__name__)
+
+   model = pickle.load(open("../ufo-model.pkl", "rb"))
+
+
+   @app.route("/")
+   def home():
+       return render_template("index.html")
+
+
+   @app.route("/predict", methods=["POST"])
+   def predict():
+
+       int_features = [int(x) for x in request.form.values()]
+       final_features = [np.array(int_features)]
+       prediction = model.predict(final_features)
+
+       output = prediction[0]
+
+       countries = ["Australia", "Canada", "Germany", "UK", "US"]
+
+       return render_template(
+           "index.html", prediction_text="Likely country: {}".format(countries[output])
+       )
+
+
+   if __name__ == "__main__":
+       app.run(debug=True)
+   ```
+
+   > 💡 Suggerimento: quando si aggiunge [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) durante l'esecuzione dell'app web utilizzando Flask, qualsiasi modifica apportata all'applicazione verrà recepita immediatamente senza la necessità di riavviare il server. Attenzione! Non abilitare questa modalità in un'app di produzione.
+
+Se si esegue `python app.py` o `python3 app.py` , il server web si avvia, localmente, e si può compilare un breve modulo per ottenere una risposta alla domanda scottante su dove sono stati avvistati gli UFO!
+
+Prima di farlo, dare un'occhiata alle parti di `app.py`:
+
+1. Innanzitutto, le dipendenze vengono caricate e l'app si avvia.
+1. Poi il modello viene importato.
+1. Infine index.html viene visualizzato sulla rotta home.
+
+Sulla rotta `/predict` , accadono diverse cose quando il modulo viene inviato:
+
+1. Le variabili del modulo vengono raccolte e convertite in un array numpy. Vengono quindi inviate al modello e viene restituita una previsione.
+2. Le nazioni che si vogliono visualizzare vengono nuovamente esposte come testo leggibile ricavato dal loro codice paese previsto e tale valore viene inviato a index.html per essere visualizzato nel template della pagina web.
+
+Usare un modello in questo modo, con Flask e un modello serializzato è relativamente semplice. La cosa più difficile è capire che forma hanno i dati che devono essere inviati al modello per ottenere una previsione. Tutto dipende da come è stato addestrato il modello. Questo ha tre punti dati da inserire per ottenere una previsione.
+
+In un ambiente professionale, si può vedere quanto sia necessaria una buona comunicazione tra le persone che addestrano il modello e coloro che lo consumano in un'app web o su dispositivo mobile. In questo caso, si ricoprono entrambi i ruoli!
+
+---
+
+## 🚀 Sfida
+
+Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato `/train`. Quali sono i pro e i contro nel seguire questo metodo?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Esistono molti modi per creare un'app web per utilizzare i modelli ML. Elencare dei modi in cui si potrebbe utilizzare JavaScript o Python per creare un'app web per sfruttare machine learning. Considerare l'architettura: il modello dovrebbe rimanere nell'app o risiedere nel cloud? In quest'ultimo casi, come accedervi? Disegnare un modello architettonico per una soluzione web ML applicata.
+
+## Compito
+
+[Provare un modello diverso](assignment.it.md)
+
+
diff --git a/3-Web-App/1-Web-App/translations/README.ja.md b/3-Web-App/1-Web-App/translations/README.ja.md
new file mode 100644
index 000000000..b23050dd6
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/README.ja.md
@@ -0,0 +1,345 @@
+# 機械学習モデルを使うためのWebアプリを構築する
+
+この講義では、この世界のものではないデータセットを使って機械学習モデルを学習させます。NUFORCのデータベースに登録されている「過去100年のUFO目撃情報」です。
+
+あなたが学ぶ内容は以下の通りです。
+
+- 学習したモデルを「塩漬け」にする方法
+- モデルをFlaskアプリで使う方法
+
+引き続きノートブックを使ってデータのクリーニングやモデルの学習を行いますが、さらに一歩進んでモデルを「野生で」、つまりWebアプリで使うのを検討することも可能です。
+
+そのためには、Flaskを使ってWebアプリを構築する必要があります。
+
+## [講義前の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ja)
+
+## アプリの構築
+
+機械学習モデルを使うためのWebアプリを構築する方法はいくつかあります。Webアーキテクチャはモデルの学習方法に影響を与える可能性があります。データサイエンスグループが学習したモデルをアプリで使用する、という業務があなたに任されている状況をイメージしてください。
+
+### 検討事項
+
+あなたがすべき質問はたくさんあります。
+
+- **Webアプリですか？それともモバイルアプリですか？** モバイルアプリを構築している場合や、IoTの環境でモデルを使う必要がある場合は、[TensorFlow Lite](https://www.tensorflow.org/lite/) を使用して、AndroidまたはiOSアプリでモデルを使うことができます。
+- **モデルはどこに保存しますか？** クラウドでしょうか？それともローカルでしょうか？
+- **オフラインでのサポート。** アプリはオフラインで動作する必要がありますか？
+- **モデルの学習にはどのような技術が使われていますか？** 選択された技術は使用しなければいけないツールに影響を与える可能性があります。
+    - **Tensor flow を使っている。** 例えば TensorFlow を使ってモデルを学習している場合、 [TensorFlow.js](https://www.tensorflow.org/js/) を使って、Webアプリで使用できるように TensorFlow モデルを変換する機能をそのエコシステムは提供しています。
+    - **PyTorchを使っている。** [PyTorch](https://pytorch.org/) などのライブラリを使用してモデルを構築している場合、[ONNX](https://onnx.ai/) (Open Neural Network Exchange) 形式で出力して、JavaScript のWebアプリで [Onnx Runtime](https://www.onnxruntime.ai/) を使用するという選択肢があります。この選択肢は、Scikit-learn で学習したモデルを使う今後の講義で調べます。
+    - **Lobe.ai または Azure Custom Vision を使っている。** [Lobe.ai](https://lobe.ai/) や [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) のような機械学習SaaS (Software as a Service) システムを使用してモデルを学習している場合、この種のソフトウェアは多くのプラットフォーム向けにモデルを出力する方法を用意していて、これにはクラウド上のオンラインアプリケーションからリクエストされるような専用APIを構築することも含まれます。
+
+また、ウェブブラウザ上でモデルを学習することができるFlaskのWebアプリを構築することもできます。JavaScript の場合でも TensorFlow.js を使うことで実現できます。
+
+私たちの場合はPythonベースのノートブックを今まで使用してきたので、学習したモデルをそのようなノートブックからPythonで構築されたWebアプリで読める形式に出力するために必要な手順を探ってみましょう。
+
+## ツール
+
+ここでの作業には2つのツールが必要です。FlaskとPickleで、どちらもPython上で動作します。
+
+✅ [Flask](https://palletsprojects.com/p/flask/) とは？制作者によって「マイクロフレームワーク」と定義されているFlaskは、Pythonを使ったWebフレームワークの基本機能と、Webページを構築するためのテンプレートエンジンを提供しています。Flaskでの構築を練習するために [この学習モジュール](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) を見てみてください。
+
+✅ [Pickle](https://docs.python.org/3/library/pickle.html) とは？Pickle 🥒 は、Pythonのオブジェクト構造をシリアライズ・デシリアライズするPythonモジュールです。モデルを「塩漬け」にすると、Webで使用するためにその構造をシリアライズしたり平坦化したりします。pickleは本質的に安全ではないので、ファイルの 'un-pickle' を促された際は注意してください。塩漬けされたファイルの末尾は `.pkl` となります。
+
+## 演習 - データをクリーニングする
+
+この講義では、[NUFORC](https://nuforc.org) (The National UFO Reporting Center) が集めた8万件のUFO目撃情報のデータを使います。このデータには、UFOの目撃情報に関する興味深い記述があります。例えば以下のようなものです。
+
+- **長い記述の例。** 「夜の草原を照らす光線から男が現れ、Texas Instruments の駐車場に向かって走った」
+- **短い記述の例。** 「私たちを光が追いかけてきた」
+
+[ufos.csv](../data/ufos.csv) のスプレッドシートには、目撃された場所の都市 (`city`)、州 (`state`)、国 (`country`)、物体の形状 (`shape`)、緯度 (`latitude`)、経度 (`longitude`) などの列が含まれています。
+
+この講義に含んでいる空の [ノートブック](../notebook.ipynb) で、以下の手順に従ってください。
+
+1. 前回の講義で行ったように `pandas`、`matplotlib`、`numpy` をインポートし、UFOのスプレッドシートをインポートしてください。サンプルのデータセットを見ることができます。
+
+    ```python
+    import pandas as pd
+    import numpy as np
+    
+    ufos = pd.read_csv('./data/ufos.csv')
+    ufos.head()
+    ```
+
+1. UFOのデータを新しいタイトルで小さいデータフレームに変換してください。また、`Country` 属性の一意な値を確認してください。
+
+    ```python
+    ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
+    
+    ufos.Country.unique()
+    ```
+
+1. ここで、null値をすべて削除し、1~60秒の目撃情報のみを読み込むことで処理すべきデータ量を減らすことができます。
+
+    ```python
+    ufos.dropna(inplace=True)
+    
+    ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
+    
+    ufos.info()
+    ```
+
+1. Scikit-learn の `LabelEncoder` ライブラリをインポートして、国の文字列値を数値に変換してください。
+
+   ✅ LabelEncoder はデータをアルファベット順にエンコードします。
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
+    
+    ufos.head()
+    ```
+
+    データは以下のようになります。
+
+    ```output
+    	Seconds	Country	Latitude	Longitude
+    2	20.0	3		53.200000	-2.916667
+    3	20.0	4		28.978333	-96.645833
+    14	30.0	4		35.823889	-80.253611
+    23	60.0	4		45.582778	-122.352222
+    24	3.0		3		51.783333	-0.783333
+    ```
+
+## 演習 - モデルを構築する
+
+これでデータを訓練グループとテストグループに分けてモデルを学習する準備ができました。
+
+1. Xベクトルとして学習したい3つの特徴を選択し、Yベクトルには `Country` を指定します。`Seconds`、`Latitude`、`Longitude` を入力して国のIDを取得することにします。
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Seconds','Latitude','Longitude']
+    
+    X = ufos[Selected_features]
+    y = ufos['Country']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    ```
+
+1. ロジスティック回帰を使ってモデルを学習してください。
+
+    ```python
+    from sklearn.metrics import accuracy_score, classification_report
+    from sklearn.linear_model import LogisticRegression
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+国 (`Country`) と緯度・経度 (`Latitude/Longitude`) は相関しているので当然ですが、精度は悪くないです。**（約95%）**
+
+緯度 (`Latitude`) と経度 (`Longitude`) から国 (`Country`) を推測することができるので、作成したモデルは画期的なものではありませんが、クリーニングして出力した生のデータから学習を行い、このモデルをWebアプリで使用してみる良い練習にはなります。
+
+## 演習 - モデルを「塩漬け」にする
+
+さて、いよいよモデルを「塩漬け」にしてみましょう！これは数行のコードで実行できます。「塩漬け」にした後は、そのモデルを読み込んで、秒・緯度・経度を含むサンプルデータの配列でテストしてください。
+
+```python
+import pickle
+model_filename = 'ufo-model.pkl'
+pickle.dump(model, open(model_filename,'wb'))
+
+model = pickle.load(open('ufo-model.pkl','rb'))
+print(model.predict([[50,44,-12]]))
+```
+
+モデルはイギリスの国番号である **「3」** を返します。すばらしい！👽
+
+## 演習 - Flaskアプリを構築する
+
+これでFlaskアプリを構築してモデルを呼び出すことができるようになり、これは同じような結果を返しますが、視覚的によりわかりやすい方法です。
+
+1. まず、_ufo-model.pkl_ ファイルと _notebook.ipynb_ ファイルが存在する場所に **web-app** というフォルダを作成してください。
+
+1. そのフォルダの中に、さらに3つのフォルダを作成してください。**css** というフォルダを含む **static** と、**templates** です。以下のようなファイルとディレクトリになっているはずです。
+
+    ```output
+    web-app/
+      static/
+        css/
+      templates/
+    notebook.ipynb
+    ufo-model.pkl
+    ```
+
+    ✅ 完成したアプリを見るには、solution フォルダを参照してください。
+
+1. _web-app_ フォルダの中に作成する最初のファイルは **requirements.txt** です。JavaScript アプリにおける _package.json_ と同様に、このファイルはアプリに必要な依存関係をリストにしたものです。**requirements.txt** に以下の行を追加してください。
+
+    ```text
+    scikit-learn
+    pandas
+    numpy
+    flask
+    ```
+
+1. 次に、_web-app_ に移動して、このファイルを実行します。
+
+    ```bash
+    cd web-app
+    ```
+
+1. _requirements.txt_ に記載されているライブラリをインストールするために、ターミナルで `pip install` と入力してください。
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+1. アプリを完成させるために、さらに3つのファイルを作成する準備が整いました。
+
+    1. ルートに **app.py** を作成してください。
+    2. _templates_ ディレクトリに **index.html** を作成してください。
+    3. _static/css_ ディレクトリに **styles.css** を作成してください。
+
+1. 以下のスタイルで _styles.css_ ファイルを構築してください。
+
+    ```css
+    body {
+    	width: 100%;
+    	height: 100%;
+    	font-family: 'Helvetica';
+    	background: black;
+    	color: #fff;
+    	text-align: center;
+    	letter-spacing: 1.4px;
+    	font-size: 30px;
+    }
+    
+    input {
+    	min-width: 150px;
+    }
+    
+    .grid {
+    	width: 300px;
+    	border: 1px solid #2d2d2d;
+    	display: grid;
+    	justify-content: center;
+    	margin: 20px auto;
+    }
+    
+    .box {
+    	color: #fff;
+    	background: #2d2d2d;
+    	padding: 12px;
+    	display: inline-block;
+    }
+    ```
+
+1. 次に _index.html_ を構築してください。
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+      <head>
+        <meta charset="UTF-8">
+        <title>🛸 UFO Appearance Prediction! 👽</title>
+        <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
+      </head>
+    
+      <body>
+        <div class="grid">
+    
+          <div class="box">
+    
+            <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+    
+            <form action="{{ url_for('predict')}}" method="post">
+              <input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+              <input type="text" name="latitude" placeholder="Latitude" required="required" />
+              <input type="text" name="longitude" placeholder="Longitude" required="required" />
+              <button type="submit" class="btn">Predict country where the UFO is seen</button>
+            </form>
+    
+            <p>{{ prediction_text }}</p>
+    
+          </div>
+    
+        </div>
+    
+      </body>
+    </html>
+    ```
+
+    このファイルのテンプレートを見てみましょう。予測テキストのようにアプリから渡された変数を、 `{{}}` という「マスタッシュ」構文で囲んでいることに注目してください。また、`/predict` のパスに対して予測を送信するフォームもあります。
+
+    ついに、モデルを使用して予測値を表示するpythonファイルを構築する準備ができました。
+
+1. `app.py` に以下を追加してください。
+
+    ```python
+    import numpy as np
+    from flask import Flask, request, render_template
+    import pickle
+    
+    app = Flask(__name__)
+    
+    model = pickle.load(open("../ufo-model.pkl", "rb"))
+    
+    
+    @app.route("/")
+    def home():
+        return render_template("index.html")
+    
+    
+    @app.route("/predict", methods=["POST"])
+    def predict():
+    
+        int_features = [int(x) for x in request.form.values()]
+        final_features = [np.array(int_features)]
+        prediction = model.predict(final_features)
+    
+        output = prediction[0]
+    
+        countries = ["Australia", "Canada", "Germany", "UK", "US"]
+    
+        return render_template(
+            "index.html", prediction_text="Likely country: {}".format(countries[output])
+        )
+    
+    
+    if __name__ == "__main__":
+        app.run(debug=True)
+    ```
+
+    > 💡 ヒント: Flaskを使ったWebアプリを実行する際に [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) を加えると、サーバを再起動しなくてもアプリに加えた変更がすぐに反映されます。注意！本番アプリではこのモードを有効にしないでください。
+
+`python app.py` もしくは `python3 app.py` を実行すると、Webサーバがローカルに起動し、UFOがどこで目撃されたのかという重要な質問に対する答えを、短いフォームに記入することで得られます。
+
+その前に `app.py` を見てみましょう。
+
+1. 最初に、依存関係が読み込まれてアプリが起動します。
+1. 次に、モデルが読み込まれます。
+1. 次に、ホームのパスで index.html がレンダリングされます。
+
+`/predict` のパスにフォームを送信するといくつかのことが起こります。
+
+1. フォームの変数が集められてnumpyの配列に変換されます。それらはモデルに送られ、予測が返されます。
+2. 表示させたい国は、予測されたコードから読みやすい文字列に再レンダリングされて、index.html に送り返された後にテンプレートの中でレンダリングされます。
+
+このように、Flaskとpickleされたモデルを使うのは比較的簡単です。一番難しいのは、予測を得るためにモデルに送らなければならないデータがどのような形をしているかを理解することです。それはモデルがどのように学習されたかによります。今回の場合は、予測を得るために入力すべきデータが3つあります。
+
+プロの現場では、モデルを学習する人と、それをWebやモバイルアプリで使用する人との間に、良好なコミュニケーションが必要であることがわかります。今回はたった一人の人間であり、それはあなたです！
+
+---
+
+## 🚀 チャレンジ
+
+ノートブックで作業してモデルをFlaskアプリにインポートする代わりに、Flaskアプリの中でモデルをトレーニングすることができます。おそらくデータをクリーニングした後になりますが、ノートブック内のPythonコードを変換して、アプリ内の `train` というパスでモデルを学習してみてください。この方法を採用することの長所と短所は何でしょうか？
+
+## [講義後の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ja)
+
+## 振り返りと自主学習
+
+機械学習モデルを使用するWebアプリを構築する方法はたくさんあります。JavaScript やPythonを使って機械学習を活用するWebアプリを構築する方法を挙げてください。アーキテクチャに関する検討: モデルはアプリ内に置くべきでしょうか？それともクラウドに置くべきでしょうか？後者の場合、どのようにアクセスするでしょうか？機械学習を使ったWebソリューションのアーキテクチャモデルを描いてください。
+
+## 課題
+
+[違うモデルを試す](assignment.ja.md)
diff --git a/3-Web-App/1-Web-App/translations/README.ko.md b/3-Web-App/1-Web-App/translations/README.ko.md
new file mode 100644
index 000000000..9b3be2ed6
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/README.ko.md
@@ -0,0 +1,348 @@
+# ML 모델 사용하여 Web App 만들기
+
+이 강의에서, 이 세상에 없었던 데이터셋에 대하여 ML 모델을 훈련할 예정입니다: _UFO sightings over the past century_, sourced from NUFORC's database.
+
+다음을 배우게 됩니다:
+
+- 훈련된 모델을 'pickle'하는 방식
+- Flask 앱에서 모델을 사용하는 방식
+
+계속 노트북으로 데이터를 정리하고 모델을 훈련하지만, 웹 앱에서 'in the wild' 모델을 사용하면 단계를 넘어서 발전할 수 있습니다. 
+
+이러면, Flask로 웹 앱을 만들어야 합니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
+
+## 앱 만들기
+
+머신러닝 모델로 웹 앱을 만드는 여러 방식이 존재합니다. 웹 구조는 모델을 훈련하는 방식에 영향을 줄 수 있습니다. 데이터 사이언스 그룹이 앱에서 사용하고 싶은 훈련된 모델을 가지고 비지니스에서 일한다고 상상해봅니다.
+
+### 고려할 사항
+
+많은 질문들을 물어볼 필요가 있습니다:
+
+- **웹 앱 혹은 모바일 앱인가요?** 만약 모바일 앱을 만들거나 IoT 컨텍스트에서 모델을 사용해야 되는 경우, [TensorFlow Lite](https://www.tensorflow.org/lite/)로 Android 또는 iOS 앱에서 모델을 사용할 수 있습니다.
+- **모델은 어디에 있나요?** 클라우드 또는 로컬 중 어디인가요?
+- **오프라인 지원합니다.** 앱이 오프라인으로 동작하나요?
+- **모델을 훈련시킬 때 사용하는 기술은 무엇인가요?** 선택된 기술은 사용할 도구에 영향을 줄 수 있습니다.
+   - **Tensor flow 사용합니다.** 만약 TensorFlow로 모델을 훈련한다면, 예시로, 에코 시스템은 [TensorFlow.js](https://www.tensorflow.org/js/)로 웹 앱에서 사용할 TensorFlow 모델을 변환해주는 기능을 제공합니다.
+   - **PyTorch 사용합니다.** 만약 [PyTorch](https://pytorch.org/) 같은 라이브러리로 모델을 만들면, [Onnx Runtime](https://www.onnxruntime.ai/)으로 할 수 있는 JavaScript 웹 앱에서 사용하기 위한 [ONNX](https://onnx.ai/) (Open Neural Network Exchange) 포맷으로 내보낼 옵션이 존재합니다. 이 옵션은 Scikit-learn-trained 모델로 이후 강의에서 알아볼 예정입니다.
+   - **Lobe.ai 또는 Azure Custom vision 사용합니다.** 만약 [Lobe.ai](https://lobe.ai/) 또는 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) 같은 ML SaaS (Software as a Service) 시스템으로 모델을 훈련하게 된다면, 이 소프트웨어 타입은 온라인 애플리케이션이 클라우드에서 쿼리된 bespoke API를 만드는 것도 포함해서 많은 플랫폼의 모델들을 내보낼 방식을 제공합니다.
+
+또 웹 브라우저에서 모델로만 훈련할 수 있는 모든 Flask 웹 앱을 만들 수 있습니다. JavaScript 컨텍스트에서 TensorFlow.js로 마무리 지을 수 있습니다.
+
+목적을 위해서, Python-기반의 노트북으로 작성했기 때문에, 노트북에서 훈련된 모델을 Python-제작한 웹 앱에서 읽을 수 있는 포맷으로 내보낼 때 필요한 단계를 알아봅니다.
+
+## 도구
+
+작업에서, 2가지 도구가 필요합니다: Flask 와 Pickle은, 둘 다 Python에서 작동합니다.
+
+✅ [Flask](https://palletsprojects.com/p/flask/)는 무엇일까요? 작성자가 'micro-framework'로 정의한, Flask는 Python으로 웹 프레임워크의 기본적인 기능과 웹 페이지를 만드는 템플릿 엔진을 제공합니다. [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)을 보고 Flask로 만드는 것을 연습합니다.
+
+✅ [Pickle](https://docs.python.org/3/library/pickle.html)은 무엇일까요? Pickle 🥒은 Python 객체 구조를 serializes와 de-serializes하는 Python 모듈입니다. 모델을 'pickle'하게 되면, 웹에서 쓰기 위해서 serialize 또는 flatten합니다. 주의합시다: pickle은 원래 안전하지 않아서, 파일을 'un-pickle'한다고 나오면 조심합니다. pickled 파일은 접미사 `.pkl`로 있습니다. 
+
+## 연습 - 데이터 정리하기
+
+[NUFORC](https://nuforc.org) (The National UFO Reporting Center)에서 모아둔, 80,000 UFO 목격 데이터를 이 강의에서 사용합니다. 데이터에 UFO 목격 관련한 몇 흥미로운 설명이 있습니다, 예시로 들어봅니다:
+
+- **긴 예시를 설명합니다.** "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
+- **짧은 예시를 설명합니다.** "the lights chased us".
+
+[ufos.csv](.././data/ufos.csv) 스프레드시트에는 목격된 `city`, `state` 와 `country`, 오브젝트의 `shape` 와 `latitude` 및 `longitude` 열이 포함되어 있습니다.
+
+강의에 있는 빈 [notebook](../notebook.ipynb)에서 진행합니다:
+
+1. 이전 강의에서 했던 것처럼 `pandas`, `matplotlib`, 와 `numpy`를 import하고 ufos 스프레드시트도 import합니다. 샘플 데이터셋을 볼 수 있습니다:
+
+    ```python
+    import pandas as pd
+    import numpy as np
+    
+    ufos = pd.read_csv('./data/ufos.csv')
+    ufos.head()
+    ```
+
+1. ufos 데이터를 새로운 제목의 작은 데이터프레임으로 변환합니다. `Country` 필드가 유니크 값인지 확인합니다.
+
+    ```python
+    ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
+    
+    ufos.Country.unique()
+    ```
+
+1. 지금부터, 모든 null 값을 드랍하고 1-60초 사이 목격만 가져와서 처리할 데이터의 수량을 줄일 수 있습니다:
+
+    ```python
+    ufos.dropna(inplace=True)
+    
+    ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
+    
+    ufos.info()
+    ```
+
+1. Scikit-learn의 `LabelEncoder` 라이브러리를 Import해서 국가의 텍스트 값을 숫자로 변환합니다:
+
+   ✅ LabelEncoder는 데이터를 알파벳 순서로 인코드합니다.
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
+    
+    ufos.head()
+    ```
+
+    데이터는 이렇게 보일 것입니다:
+
+    ```output
+    	Seconds	Country	Latitude	Longitude
+    2	20.0	3	    53.200000	-2.916667
+    3	20.0	4	    28.978333	-96.645833
+    14	30.0	4	    35.823889	-80.253611
+    23	60.0	4	    45.582778	-122.352222
+    24	3.0	    3	    51.783333	-0.783333
+    ```
+
+## 연습 - 모델 만들기
+
+지금부터 데이터를 훈련하고 테스트할 그룹으로 나누어서 모델을 훈련할 준비가 되었습니다.
+
+1. X 백터로 훈련할 3가지 features를 선택하면, y 백터는 `Country`로 됩니다. `Seconds`, `Latitude` 와 `Longitude`를 입력하면 국가 id로 반환되기를 원합니다.
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Seconds','Latitude','Longitude']
+    
+    X = ufos[Selected_features]
+    y = ufos['Country']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    ```
+
+1. logistic regression을 사용해서 모델을 훈련합니다:
+
+    ```python
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+당연하게, `Country` 와 `Latitude/Longitude`가 상관 관계있어서, 정확도 **(around 95%)** 가 나쁘지 않습니다.
+
+만든 모델은 `Latitude` 와 `Longitude`에서 `Country`를 알 수 있어야 하므로 매우 혁신적이지 않지만, 정리하면서, 뽑은 원본 데이터에서 훈련을 해보고 웹 앱에서 모델을 쓰기에 좋은 연습입니다.
+
+## 연습 - 모델 'pickle'하기
+
+모델을 _pickle_ 할 시간이 되었습니다! 코드 몇 줄로 할 수 있습니다. _pickled_ 되면, pickled 모델을 불러와서 초, 위도와 경도 값이 포함된 샘플 데이터 배열을 대상으로 테스트합니다.
+
+```python
+import pickle
+model_filename = 'ufo-model.pkl'
+pickle.dump(model, open(model_filename,'wb'))
+
+model = pickle.load(open('ufo-model.pkl','rb'))
+print(model.predict([[50,44,-12]]))
+```
+
+모델은 영국 국가 코드인, **'3'** 이 반환됩니다. Wild! 👽
+
+## 연습 - Flask 앱 만들기
+
+지금부터 Flask 앱을 만들어서 모델을 부르고 비슷한 결과를 반환하지만, 시각적으로 만족할 방식으로도 가능합니다.
+
+1. _ufo-model.pkl_ 파일과 _notebook.ipynb_ 파일 옆에 **web-app** 이라고 불리는 폴더를 만들면서 시작합니다.
+
+1. 폴더에서 3가지 폴더를 만듭니다: **static**, 내부에 **css** 폴더가 있으며, **templates`** 도 있습니다. 지금부터 다음 파일과 디렉토리들이 있어야 합니다:
+
+    ```output
+    web-app/
+      static/
+        css/
+        templates/
+    notebook.ipynb
+    ufo-model.pkl
+    ``` 
+
+   ✅ 완성된 앱을 보려면 solution 폴더를 참조합니다
+
+1. _web-app_ 폴더에서 만들 첫 파일은 **requirements.txt** 파일입니다. JavaScript 앱의 _package.json_ 처럼, 앱에 필요한 의존성을 리스트한 파일입니다. **requirements.txt** 에 해당 라인을 추가합니다:
+
+    ```text
+    scikit-learn
+    pandas
+    numpy
+    flask
+    ```
+
+1. 지금부터, _web-app_ 으로 이동해서 파일을 실행합니다:
+
+   ```bash
+   cd web-app
+   ```
+
+1. 터미널에서 `pip install`을 타이핑해서, _requirements.txt_ 에 나열된 라이브러리를 설치합니다:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+1. 지금부터, 앱을 완성하기 위해서 3가지 파일을 더 만들 준비를 했습니다:
+
+    1. 최상단에  **app.py**를 만듭니다.
+    2. _templates_ 디렉토리에 **index.html**을 만듭니다.
+    3. _static/css_ 디렉토리에 **styles.css**를 만듭니다.
+
+1. 몇 스타일로 _styles.css_ 파일을 만듭니다:
+
+    ```css
+    body {
+    	width: 100%;
+    	height: 100%;
+    	font-family: 'Helvetica';
+    	background: black;
+    	color: #fff;
+    	text-align: center;
+    	letter-spacing: 1.4px;
+    	font-size: 30px;
+    }
+    
+    input {
+    	min-width: 150px;
+    }
+    
+    .grid {
+    	width: 300px;
+    	border: 1px solid #2d2d2d;
+    	display: grid;
+    	justify-content: center;
+    	margin: 20px auto;
+    }
+    
+    .box {
+    	color: #fff;
+    	background: #2d2d2d;
+    	padding: 12px;
+    	display: inline-block;
+    }
+    ```
+
+1. 다음으로 _index.html_ 파일을 만듭니다:
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+    <head>
+      <meta charset="UTF-8">
+      <title>🛸 UFO Appearance Prediction! 👽</title>
+      <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}"> 
+    </head>
+    
+    <body>
+     <div class="grid">
+    
+      <div class="box">
+    
+      <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+    
+        <form action="{{ url_for('predict')}}" method="post">
+        	<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+          <input type="text" name="latitude" placeholder="Latitude" required="required" />
+    		  <input type="text" name="longitude" placeholder="Longitude" required="required" />
+          <button type="submit" class="btn">Predict country where the UFO is seen</button>
+        </form>
+    
+      
+       <p>{{ prediction_text }}</p>
+    
+     </div>
+    </div>
+    
+    </body>
+    </html>
+    ```
+
+   파일의 템플릿을 봅니다. 예측 텍스트: `{{}}`처럼, 앱에서 제공할 수 있는 변수 주위, 'mustache' 구문을 확인해봅니다. `/predict` 라우터에 예측을 보낼 폼도 있습니다.
+
+    마지막으로, 모델을 써서 예측으로 보여줄 python 파일을 만들 준비가 되었습니다:
+
+1. `app.py` 에 추가합니다:
+
+    ```python
+    import numpy as np
+    from flask import Flask, request, render_template
+    import pickle
+    
+    app = Flask(__name__)
+    
+    model = pickle.load(open("../ufo-model.pkl", "rb"))
+    
+    
+    @app.route("/")
+    def home():
+        return render_template("index.html")
+    
+    
+    @app.route("/predict", methods=["POST"])
+    def predict():
+    
+        int_features = [int(x) for x in request.form.values()]
+        final_features = [np.array(int_features)]
+        prediction = model.predict(final_features)
+    
+        output = prediction[0]
+    
+        countries = ["Australia", "Canada", "Germany", "UK", "US"]
+    
+        return render_template(
+            "index.html", prediction_text="Likely country: {}".format(countries[output])
+        )
+    
+    
+    if __name__ == "__main__":
+        app.run(debug=True)
+    ```
+
+   > 💡 팁: [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode)를 추가하면 Flask 사용해서 웹 앱을 실행하는 도중에, 서버를 다시 시작할 필요없이 애플리케이션에 변경점이 바로 반영됩니다. 조심하세요! 프로덕션 앱에서 이 모드를 활성화하지 맙시다. 
+
+만약 `python app.py` 또는 `python3 app.py`를 실행하면 - 웹 서버가 로컬에서 시작하고, 짧은 폼을 작성하면 UFOs가 목격된 장소에 대해 주목받을 질문의 답을 얻을 수 있습니다!
+
+하기 전, `app.py`의 일부분을 봅니다:
+
+1. 먼저, 의존성을 불러오고 앱이 시작합니다.
+1. 그 다음, 모델을 가져옵니다.
+1. 그 다음, index.html을 홈 라우터에 랜더링합니다.
+
+`/predict` 라우터에서, 폼이 보내질 때 몇가지 해프닝이 생깁니다:
+
+1. 폼 변수를 모아서 numpy 배열로 변환합니다. 그러면 모델로 보내지고 예측이 반환됩니다.
+2. 국가를 보여줄 때는 예상된 국가 코드에서 읽을 수 있는 텍스트로 다시 랜더링하고, 이 값을 템플릿에서 랜더링할 수 있게 index.html로 보냅니다.
+
+Flask와 pickled 모델과 같이, 모델을 사용하는 이 방식은, 비교적으로 간단합니다. 어려운 것은 예측을 받기 위해서 모델에 줄 데이터의 모양을 이해해야 한다는 것입니다. 모든 모델이 어떻게 훈련받았는 지에 따릅니다. 예측을 받기 위해서 3개 데이터 포인트를 넣어야 합니다.
+
+전문 세팅에서, 모델을 훈련하는 사람과 웹 또는 모바일 앱에서 사용하는 사람 사이 얼마나 좋은 소통이 필요한 지 알 수 있습니다. 이 케이스는, 오직 한 사람, 당신입니다!
+
+---
+
+## 🚀 도전
+
+노트북에서 작성하고 Flask 앱에서 모델을 가져오는 대신, Flask 앱에서 바로 모델을 훈련할 수 있습니다!  어쩌면 데이터를 정리하고, 노트북에서 Python 코드로 변환해서, `train`이라고 불리는 라우터로 앱에서 모델을 훈련합니다. 이러한 방식을 추구했을 때 장점과 단점은 무엇인가요?
+
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
+
+## 검토 & 자기주도 학습
+
+ML 모델로 웹 앱을 만드는 방식은 많습니다. JavaScript 또는 Python으로 머신러닝을 활용하는 웹 앱의 제작 방식에 대한 목록을 만듭니다. 구조를 고려합니다: 모델이 앱이나 클라우드에 있나요? 만약 후자의 경우, 어떻게 접근하나요? 적용한 ML 웹 솔루션에 대해 아키텍쳐 모델을 그립니다.
+
+## 과제 
+
+[Try a different model](../assignment.md)
+
+
diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index cb8a051c5..af45d1ce9 100644
--- a/3-Web-App/1-Web-App/translations/README.zh-cn.md
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -1,58 +1,58 @@
-# 构建使用ML模型的Web应用程序
+# 构建使用 ML 模型的 Web 应用程序
 
-在本课中，你将在一个数据集上训练一个ML模型，这个数据集来自世界各地：过去一个世纪的UFO目击事件，来源于[NUFORC的数据库](https://www.nuforc.org)。
+在本课中，你将在一个数据集上训练一个 ML 模型，这个数据集来自世界各地：过去一个世纪的 UFO 目击事件，来源于 [NUFORC 的数据库](https://www.nuforc.org)。
 
 你将学会：
 
 - 如何“pickle”一个训练有素的模型
-- 如何在Flask应用程序中使用该模型
+- 如何在 Flask 应用程序中使用该模型
 
-我们将继续使用notebook来清理数据和训练我们的模型，但你可以进一步探索在web应用程序中使用模型。
+我们将继续使用 notebook 来清理数据和训练我们的模型，但你可以进一步探索在 web 应用程序中使用模型。
 
-为此，你需要使用Flask构建一个web应用程序。
+为此，你需要使用 Flask 构建一个 web 应用程序。
 
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
 
 ## 构建应用程序
 
-有多种方法可以构建Web应用程序以使用机器学习模型。你的web架构可能会影响你的模型训练方式。想象一下，你在一家企业工作，其中数据科学小组已经训练了他们希望你在应用程序中使用的模型。 
+有多种方法可以构建 Web 应用程序以使用机器学习模型。你的 web 架构可能会影响你的模型训练方式。想象一下，你在一家企业工作，其中数据科学小组已经训练了他们希望你在应用程序中使用的模型。
 
-### 注意事项 
+### 注意事项
 
-你需要问很多问题： 
+你需要问很多问题：
 
-- **它是web应用程序还是移动应用程序？**如果你正在构建移动应用程序或需要在物联网环境中使用模型，你可以使用[TensorFlow Lite](https://www.tensorflow.org/lite/)并在Android或iOS应用程序中使用该模型。
-- **模型放在哪里？**在云端还是本地？
-- **离线支持**。该应用程序是否必须离线工作？ 
-- **使用什么技术来训练模型？**所选的技术可能会影响你需要使用的工具。 
-   - **使用Tensor flow**。例如，如果你正在使用TensorFlow训练模型，则该生态系统提供了使用[TensorFlow.js](https://www.tensorflow.org/js/)转换TensorFlow模型以便在Web应用程序中使用的能力。
-   - **使用 PyTorch**。如果你使用[PyTorch](https://pytorch.org/)等库构建模型，则可以选择将其导出到[ONNX](https://onnx.ai/)（开放神经网络交换)格式，用于可以使用 [Onnx Runtime](https://www.onnxruntime.ai/)的JavaScript Web 应用程序。此选项将在Scikit-learn-trained模型的未来课程中进行探讨。
-   - **使用Lobe.ai或Azure自定义视觉**。如果你使用ML SaaS（软件即服务）系统，例如[Lobe.ai](https://lobe.ai/)或[Azure Custom Vision](https://azure.microsoft.com/services/ cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa)来训练模型，这种类型的软件提供了为许多平台导出模型的方法，包括构建一个定制API，供在线应用程序在云中查询。
+- **它是 web 应用程序还是移动应用程序？** 如果你正在构建移动应用程序或需要在物联网环境中使用模型，你可以使用 [TensorFlow Lite](https://www.tensorflow.org/lite/) 并在 Android 或 iOS 应用程序中使用该模型。
+- **模型放在哪里？** 在云端还是本地？
+- **离线支持**。该应用程序是否必须离线工作？
+- **使用什么技术来训练模型？** 所选的技术可能会影响你需要使用的工具。
+   - **使用 TensorFlow**。例如，如果你正在使用 TensorFlow 训练模型，则该生态系统提供了使用 [TensorFlow.js](https://www.tensorflow.org/js/) 转换 TensorFlow 模型以便在Web应用程序中使用的能力。
+   - **使用 PyTorch**。如果你使用 [PyTorch](https://pytorch.org/) 等库构建模型，则可以选择将其导出到 [ONNX](https://onnx.ai/)（开放神经网络交换）格式，用于可以使用 [Onnx Runtime](https://www.onnxruntime.ai/)的JavaScript Web 应用程序。此选项将在 Scikit-learn-trained 模型的未来课程中进行探讨。
+   - **使用 Lobe.ai 或 Azure 自定义视觉**。如果你使用 ML SaaS（软件即服务）系统，例如 [Lobe.ai](https://lobe.ai/) 或 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) 来训练模型，这种类型的软件提供了为许多平台导出模型的方法，包括构建一个定制A PI，供在线应用程序在云中查询。
 
-你还有机会构建一个完整的Flask Web应用程序，该应用程序能够在 Web浏览器中训练模型本身。这也可以在JavaScript上下文中使用 TensorFlow.js来完成。 
+你还有机会构建一个完整的 Flask Web 应用程序，该应用程序能够在 Web浏览器中训练模型本身。这也可以在 JavaScript 上下文中使用 TensorFlow.js 来完成。
 
-出于我们的目的，既然我们一直在使用基于Python的notebook，那么就让我们探讨一下将经过训练的模型从notebook导出为Python构建的web应用程序可读的格式所需要采取的步骤。
+出于我们的目的，既然我们一直在使用基于 Python 的 notebook，那么就让我们探讨一下将经过训练的模型从 notebook 导出为 Python 构建的 web 应用程序可读的格式所需要采取的步骤。
 
 ## 工具
 
-对于此任务，你需要两个工具：Flask和Pickle，它们都在Python上运行。 
+对于此任务，你需要两个工具：Flask 和 Pickle，它们都在 Python 上运行。
 
-✅ 什么是 [Flask](https://palletsprojects.com/p/flask/)？ Flask被其创建者定义为“微框架”，它提供了使用Python和模板引擎构建网页的Web框架的基本功能。看看[本学习单元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)练习使用Flask构建应用程序。 
+✅ 什么是 [Flask](https://palletsprojects.com/p/flask/)？ Flask 被其创建者定义为“微框架”，它提供了使用 Python 和模板引擎构建网页的 Web 框架的基本功能。看看[本学习单元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)练习使用 Flask 构建应用程序。
 
-✅ 什么是[Pickle](https://docs.python.org/3/library/pickle.html)？ Pickle🥒是一 Python模块，用于序列化和反序列化 Python对象结构。当你“pickle”一个模型时，你将其结构序列化或展平以在 Web上使用。小心：pickle本质上不是安全的，所以如果提示“un-pickle”文件，请小心。生产的文件具有后缀`.pkl`。
+✅ 什么是 [Pickle](https://docs.python.org/3/library/pickle.html)？ Pickle🥒是一个 Python 模块，用于序列化和反序列化 Python 对象结构。当你“pickle”一个模型时，你将其结构序列化或展平以在 Web 上使用。小心：pickle 本质上不是安全的，所以如果提示“un-pickle”文件，请小心。生产的文件具有后缀 `.pkl`。
 
 ## 练习 - 清理你的数据 
 
-在本课中，你将使用由 [NUFORC](https://nuforc.org)（国家 UFO 报告中心）收集的80,000次UFO目击数据。这些数据对UFO目击事件有一些有趣的描述，例如：
+在本课中，你将使用由 [NUFORC](https://nuforc.org)（国家 UFO 报告中心）收集的 80,000 次 UFO 目击数据。这些数据对 UFO 目击事件有一些有趣的描述，例如：
 
 - **详细描述**。"一名男子从夜间照射在草地上的光束中出现，他朝德克萨斯仪器公司的停车场跑去"。
-- **简短描述**。 “灯光追着我们”。 
+- **简短描述**。 “灯光追着我们”。
 
-[ufos.csv](./data/ufos.csv)电子表格包括有关目击事件发生的`city`、`state`和`country`、对象的`shape`及其`latitude`和`longitude`的列。
+[ufos.csv](./data/ufos.csv) 电子表格包括有关目击事件发生的 `city`、`state` 和 `country`、对象的 `shape` 及其 `latitude` 和 `longitude` 的列。
 
-在包含在本课中的空白[notebook](notebook.ipynb)中： 
+在包含在本课中的空白 [notebook](notebook.ipynb) 中：
 
-1. 像在之前的课程中一样导入`pandas`、`matplotlib`和`numpy`，然后导入ufos电子表格。你可以查看一个示例数据集： 
+1. 像在之前的课程中一样导入 `pandas`、`matplotlib` 和 `numpy`，然后导入 ufos 电子表格。你可以查看一个示例数据集：
 
     ```python
     import pandas as pd
@@ -62,7 +62,7 @@
     ufos.head()
     ```
 
-2. 将ufos数据转换为带有新标题的小dataframe。检查`country`字段中的唯一值。 
+2. 将 ufos 数据转换为带有新标题的小 dataframe。检查 `country` 字段中的唯一值。
 
     ```python
     ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
@@ -70,7 +70,7 @@
     ufos.Country.unique()
     ```
 
-3. 现在，你可以通过删除任何空值并仅导入1-60秒之间的目击数据来减少我们需要处理的数据量：
+3. 现在，你可以通过删除任何空值并仅导入 1-60 秒之间的目击数据来减少我们需要处理的数据量：
 
     ```python
     ufos.dropna(inplace=True)
@@ -80,9 +80,9 @@
     ufos.info()
     ```
 
-4. 导入Scikit-learn的`LabelEncoder`库，将国家的文本值转换为数字： 
+4. 导入 Scikit-learn 的 `LabelEncoder` 库，将国家的文本值转换为数字：
 
-   ✅ LabelEncoder按字母顺序编码数据 
+   ✅ LabelEncoder 按字母顺序编码数据
 
     ```python
     from sklearn.preprocessing import LabelEncoder
@@ -103,11 +103,11 @@
     24	3.0	    3	    51.783333	-0.783333
     ```
 
-## 练习 - 建立你的模型 
+## 练习 - 建立你的模型
 
-现在，你可以通过将数据划分为训练和测试组来准备训练模型。 
+现在，你可以通过将数据划分为训练和测试组来准备训练模型。
 
-1. 选择要训练的三个特征作为X向量，y向量将是`Country` 你希望能够输入`Seconds`、`Latitude`和`Longitude`并获得要返回的国家/地区ID。
+1. 选择要训练的三个特征作为 X 向量，y 向量将是 `Country` 你希望能够输入 `Seconds`、`Latitude` 和 `Longitude` 并获得要返回的国家/地区 ID。
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -120,7 +120,7 @@
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
     ```
 
-2. 使用逻辑回归训练模型： 
+2. 使用逻辑回归训练模型：
 
     ```python
     from sklearn.metrics import accuracy_score, classification_report 
@@ -134,13 +134,13 @@
     print('Accuracy: ', accuracy_score(y_test, predictions))
     ```
 
-准确率还不错**（大约 95%）**，不出所料，因为`Country`和`Latitude/Longitude`相关。
+准确率还不错 **（大约 95%）**，不出所料，因为 `Country` 和 `Latitude/Longitude` 相关。
 
-你创建的模型并不是非常具有革命性，因为你应该能够从其`Latitude`和`Longitude`推断出`Country`，但是，尝试从清理、导出的原始数据进行训练，然后在web应用程序中使用此模型是一个很好的练习。
+你创建的模型并不是非常具有革命性，因为你应该能够从其 `Latitude` 和 `Longitude` 推断出 `Country`，但是，尝试从清理、导出的原始数据进行训练，然后在 web 应用程序中使用此模型是一个很好的练习。
 
 ## 练习 - “pickle”你的模型
 
-现在，是时候_pickle_你的模型了！你可以在几行代码中做到这一点。一旦它是 _pickled_，加载你的pickled模型并针对包含秒、纬度和经度值的示例数据数组对其进行测试， 
+现在，是时候 _pickle_ 你的模型了！你可以在几行代码中做到这一点。一旦它是 _pickled_，加载你的 pickled 模型并针对包含秒、纬度和经度值的示例数据数组对其进行测试，
 
 ```python
 import pickle
@@ -151,28 +151,28 @@ model = pickle.load(open('ufo-model.pkl','rb'))
 print(model.predict([[50,44,-12]]))
 ```
 
-该模型返回**'3'**，这是英国的国家代码。👽
+该模型返回 **'3'**，这是英国的国家代码。👽
 
 ## 练习 - 构建Flask应用程序 
 
 现在你可以构建一个Flask应用程序来调用你的模型并返回类似的结果，但以一种更美观的方式。
 
-1. 首先在你的 _ufo-model.pkl_ 文件所在的_notebook.ipynb_文件旁边创建一个名为**web-app**的文件夹。 
+1. 首先在你的 _ufo-model.pkl_ 文件所在的 _notebook.ipynb_ 文件旁边创建一个名为 **web-app** 的文件夹。
 
-2. 在该文件夹中创建另外三个文件夹：**static**，其中有文件夹**css**和**templates`**。 你现在应该拥有以下文件和目录
+2. 在该文件夹中创建另外三个文件夹：**static**，其中有文件夹 **css** 和 **templates**。 你现在应该拥有以下文件和目录
 
     ```output
     web-app/
       static/
         css/
-        templates/
+      templates/
     notebook.ipynb
     ufo-model.pkl
     ``` 
 
-   ✅ 请参阅解决方案文件夹以查看已完成的应用程序 
+   ✅ 请参阅解决方案文件夹以查看已完成的应用程序
 
-3. 在_web-app_文件夹中创建的第一个文件是**requirements.txt**文件。与JavaScript应用程序中的_package.json_一样，此文件列出了应用程序所需的依赖项。在**requirements.txt**中添加以下几行：  
+3. 在 _web-app_ 文件夹中创建的第一个文件是 **requirements.txt** 文件。与 JavaScript 应用程序中的 _package.json_ 一样，此文件列出了应用程序所需的依赖项。在 **requirements.txt** 中添加以下几行：  
 
     ```text
     scikit-learn
@@ -181,25 +181,25 @@ print(model.predict([[50,44,-12]]))
     flask
     ```
 
-4. 现在，进入web-app文件夹： 
+4. 现在，进入 web-app 文件夹：
 
    ```bash
    cd web-app
    ```
 
-5. 在你的终端中输入`pip install`，以安装_reuirements.txt_中列出的库：
+5. 在你的终端中输入 `pip install`，以安装 _reuirements.txt_ 中列出的库：
 
    ```bash
    pip install -r requirements.txt
    ```
 
-6. 现在，你已准备好创建另外三个文件来完成应用程序： 
+6. 现在，你已准备好创建另外三个文件来完成应用程序：
 
-    1. 在根目录中创建**app.py** 
-    2. 在_templates_目录中创建**index.html**。 
-    3. 在_static/css_目录中创建**styles.css**。 
+    1. 在根目录中创建 **app.py**。
+    2. 在 _templates_ 目录中创建**index.html**。
+    3. 在 _static/css_ 目录中创建**styles.css**。
 
-7. 使用一些样式构建_styles.css_文件： 
+7. 使用一些样式构建 _styles.css_ 文件：
 
     ```css
     body {
@@ -233,7 +233,7 @@ print(model.predict([[50,44,-12]]))
     }
     ```
 
-8. 接下来，构建_index.html_文件： 
+8. 接下来，构建 _index.html_ 文件：
 
     ```html
     <!DOCTYPE html>
@@ -268,9 +268,9 @@ print(model.predict([[50,44,-12]]))
     </html>
     ```
 
-    看看这个文件中的模板。请注意应用程序将提供的变量周围的“mustache”语法，例如预测文本：`{{}}`。还有一个表单可以将预测发布到`/predict`路由。
+    看看这个文件中的模板。请注意应用程序将提供的变量周围的“mustache”语法，例如预测文本：`{{}}`。还有一个表单可以将预测发布到 `/predict` 路由。
 
-    最后，你已准备好构建使用模型和显示预测的python 文件：
+    最后，你已准备好构建使用模型和显示预测的 python 文件：
 
 9. 在`app.py`中添加:
 
@@ -309,38 +309,38 @@ print(model.predict([[50,44,-12]]))
         app.run(debug=True)
     ```
 
-   > 💡 提示：当你在使用Flask运行Web应用程序时添加 [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode)时你对应用程序所做的任何更改将立即反映，无需重新启动服务器。注意！不要在生产应用程序中启用此模式
+   > 💡 提示：当你在使用 Flask 运行 Web 应用程序时添加 [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode)时你对应用程序所做的任何更改将立即反映，无需重新启动服务器。注意！不要在生产应用程序中启用此模式
 
-如果你运行`python app.py`或`python3 app.py` - 你的网络服务器在本地启动，你可以填写一个简短的表格来回答你关于在哪里看到UFO的问题！
+如果你运行 `python app.py` 或 `python3 app.py` - 你的网络服务器在本地启动，你可以填写一个简短的表格来回答你关于在哪里看到 UFO 的问题！
 
-在此之前，先看一下`app.py`的实现： 
+在此之前，先看一下 `app.py` 的实现：
 
 1. 首先，加载依赖项并启动应用程序。
 2. 然后，导入模型。
-3. 然后，在home路由上渲染index.html。 
+3. 然后，在 home 路由上渲染 index.html。
 
-在`/predict`路由上，当表单被发布时会发生几件事情： 
+在 `/predict` 路由上，当表单被发布时会发生几件事情： 
 
-1. 收集表单变量并转换为numpy数组。然后将它们发送到模型并返回预测。
-2. 我们希望显示的国家/地区根据其预测的国家/地区代码重新呈现为可读文本，并将该值发送回index.html以在模板中呈现。 
+1. 收集表单变量并转换为 numpy 数组。然后将它们发送到模型并返回预测。
+2. 我们希望显示的国家/地区根据其预测的国家/地区代码重新呈现为可读文本，并将该值发送回 index.html 以在模板中呈现。
 
-以这种方式使用模型，包括Flask和pickled模型，是相对简单的。最困难的是要理解数据是什么形状的，这些数据必须发送到模型中才能得到预测。这完全取决于模型是如何训练的。有三个数据要输入，以便得到一个预测。
+以这种方式使用模型，包括 Flask 和 pickled 模型，是相对简单的。最困难的是要理解数据是什么形状的，这些数据必须发送到模型中才能得到预测。这完全取决于模型是如何训练的。有三个数据要输入，以便得到一个预测。
 
-在一个专业的环境中，你可以看到训练模型的人和在Web或移动应用程序中使用模型的人之间的良好沟通是多么的必要。在我们的情况下，只有一个人，你！
+在一个专业的环境中，你可以看到训练模型的人和在 Web 或移动应用程序中使用模型的人之间的良好沟通是多么的必要。在我们的情况下，只有一个人，你！
 
 ---
 
-## 🚀 挑战: 
+## 🚀 挑战
 
-你可以在Flask应用程序中训练模型，而不是在notebook上工作并将模型导入Flask应用程序！尝试在notebook中转换Python代码，可能是在清除数据之后，从应用程序中的一个名为`train`的路径训练模型。采用这种方法的利弊是什么？
+你可以在 Flask 应用程序中训练模型，而不是在 notebook 上工作并将模型导入 Flask 应用程序！尝试在 notebook 中转换 Python 代码，可能是在清除数据之后，从应用程序中的一个名为 `train` 的路径训练模型。采用这种方法的利弊是什么？
 
-## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
 
-## 复习与自学 
+## 复习与自学
 
-有很多方法可以构建一个Web应用程序来使用ML模型。列出可以使用JavaScript或Python构建Web应用程序以利用机器学习的方法。考虑架构：模型应该留在应用程序中还是存在于云中？如果是后者，你将如何访问它？为应用的ML Web解决方案绘制架构模型。 
+有很多方法可以构建一个Web应用程序来使用ML模型。列出可以使用JavaScript或Python构建Web应用程序以利用机器学习的方法。考虑架构：模型应该留在应用程序中还是存在于云中？如果是后者，你将如何访问它？为应用的ML Web解决方案绘制架构模型。
 
-## 任务 
+## 任务
 
 [尝试不同的模型](../assignment.md)
 
diff --git a/3-Web-App/1-Web-App/translations/assignment.it.md b/3-Web-App/1-Web-App/translations/assignment.it.md
new file mode 100644
index 000000000..7bc7ffd94
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Provare un modello diverso
+
+## Istruzioni
+
+Ora che si è creato un'app web utilizzando un modello di Regressione addestrato, usare uno dei modelli da una lezione precedente sulla Regressione per rifare questa app web. Si può mantenere lo stile o progettarla in modo diverso per riflettere i dati della zucca. Fare attenzione a modificare gli input in modo che riflettano il metodo di addestramento del proprio modello.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------------------------- | --------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------- |
+| | L'app web funziona come previsto e viene distribuita nel cloud | L'app web contiene difetti o mostra risultati imprevisti | L'app web non funziona correttamente |
diff --git a/3-Web-App/1-Web-App/translations/assignment.ja.md b/3-Web-App/1-Web-App/translations/assignment.ja.md
new file mode 100644
index 000000000..2151a7c24
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 違うモデルを試す
+
+## 指示
+
+訓練された回帰モデルを使用して1つのWebアプリを構築したので、前回の回帰の講義で使用したモデルの1つを使用して、このWebアプリを再実行してください。スタイルをそのままにしても、かぼちゃのデータを反映するために別のデザインにしても構いません。モデルの学習方法に合わせて入力を変更するように注意してください。
+
+## 評価基準
+
+| 指標 | 模範的                                                      | 適切                                                              | 要改善                          |
+| ---- | ----------------------------------------------------------- | ----------------------------------------------------------------- | ------------------------------- |
+|      | Webアプリが期待通りに動作し、クラウド上にデプロイされている | Webアプリに欠陥が含まれているか、期待していない結果を表示している | Webアプリが正しく機能していない |
diff --git a/3-Web-App/1-Web-App/translations/assignment.zh-cn.md b/3-Web-App/1-Web-App/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..016dfa522
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/assignment.zh-cn.md
@@ -0,0 +1,12 @@
+# ���Բ�ͬ��ģ��
+
+## ˵��
+
+���ڣ����Ѿ��ܹ�ʹ��һ������ѵ���Ļع�ģ�����webӦ�ó�����ô�����ǰ��Ļع�γ�������ѡ��һ��ģ��������һ��webӦ�ó��������ʹ��ԭ���ķ�����������ͬ�ķ�������ƣ���չʾpumpkin���ݡ�ע����������Է�ӳģ�͵�ѵ��������
+
+
+## ���б�׼
+
+| ��׼                   | ����                                                 | �й��о�                                                  |  ����Ŭ��                      |
+| -------------------------- | --------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------- |
+| | webӦ�ó���Ԥ�����У��������ƶ� | webӦ�ó������ȱ�ݻ�����ʾ���벻���Ľ�� | webӦ�ó����޷��������� |
diff --git a/3-Web-App/README.md b/3-Web-App/README.md
index 771bbc617..c78be166e 100644
--- a/3-Web-App/README.md
+++ b/3-Web-App/README.md
@@ -1,11 +1,10 @@
 # Build a web app to use your ML model
 
-In this section of the curriculum, you will be introduced to an applied ML topic: how to save your Scikit-learn model as a file that can be used to make predictions within a web application. Once the model is saved, you'll learn how to use it in a web app built in Flask. You'll first create a model using some data that's all about UFO sightings! Then, you'll build a web app that will allow you to input a number of seconds with a latitude and a longitude value to predict which country reported seeing a UFO. 
+In this section of the curriculum, you will be introduced to an applied ML topic: how to save your Scikit-learn model as a file that can be used to make predictions within a web application. Once the model is saved, you'll learn how to use it in a web app built in Flask. You'll first create a model using some data that's all about UFO sightings! Then, you'll build a web app that will allow you to input a number of seconds with a latitude and a longitude value to predict which country reported seeing a UFO.
 
 ![UFO Parking](images/ufo.jpg)
 
 Photo by <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> on <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
-  
 
 ## Lessons
 
@@ -13,10 +12,10 @@ Photo by <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=
 
 ## Credits
 
-"Build a Web App" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper). 
+"Build a Web App" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper).
 
 ♥️ The quizzes were written by Rohan Raj.
 
-The dataset is sourced from [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings). 
+The dataset is sourced from [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings).
 
-The web app architecture was suggested in part by [this article](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) and [this repo](https://github.com/abhinavsagar/machine-learning-deployment) by Abhinav Sagar.
\ No newline at end of file
+The web app architecture was suggested in part by [this article](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) and [this repo](https://github.com/abhinavsagar/machine-learning-deployment) by Abhinav Sagar.
diff --git a/3-Web-App/translations/README.it.md b/3-Web-App/translations/README.it.md
new file mode 100644
index 000000000..d376b8ec7
--- /dev/null
+++ b/3-Web-App/translations/README.it.md
@@ -0,0 +1,22 @@
+# Creare un'app web per utilizzare il modello ML
+
+In questa sezione del programma di studi, verrà presentato un argomento ML applicato: come salvare il modello di Scikit-learn come file che può essere utilizzato per fare previsioni all'interno di un'applicazione web. Una volta salvato il modello, si imparerà come utilizzarlo in un'app web sviluppata con Flask. Per prima cosa si creerà un modello utilizzando alcuni dati che riguardano gli avvistamenti di UFO! Quindi, si creerà un'app web che consentirà di inserire un numero di secondi con un valore di latitudine e longitudine per prevedere quale paese ha riferito di aver visto un UFO.
+
+![Parcheggio UFO](../images/ufo.jpg)
+
+Foto di <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> su <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+
+## Lezioni
+
+1. [Costruire un'app web](../1-Web-App/translations/README.it.md)
+
+## Crediti
+
+"Costruire un'app web" è stato scritto con ♥️ da [Jen Looper](https://twitter.com/jenlooper).
+
+♥️ I quiz sono stati scritti da Rohan Raj.
+
+L'insieme di dati proviene da [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings).
+
+L'architettura dell'app web è stata suggerita in parte da [questo articolo](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) e da [questo](https://github.com/abhinavsagar/machine-learning-deployment) repository di Abhinav Sagar.
\ No newline at end of file
diff --git a/3-Web-App/translations/README.ja.md b/3-Web-App/translations/README.ja.md
new file mode 100644
index 000000000..3ada9808f
--- /dev/null
+++ b/3-Web-App/translations/README.ja.md
@@ -0,0 +1,21 @@
+# 機械学習モデルを使うためにWebアプリを構築する
+
+カリキュラムのこの部分では、機械学習の応用的な話題を学びます。 Webアプリで予測を行うために使用するファイルとして Scikit-learn のモデルを保存する方法です。モデルを保存した後に、Flaskを使って構築したWebアプリでそのモデルを使う方法を学びます。まずはUFOの目撃情報に関するデータでモデルを作ります。次に、UFOの目撃報告があった国を予測するために、緯度・経度・秒数を入力できるWebアプリを構築します。
+
+![UFOパーキング](../images/ufo.jpg)
+
+<a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> によって <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> に投稿された写真
+
+## 講義
+
+1. [Webアプリを構築する](../1-Web-App/translations/README.ja.md)
+
+## クレジット
+
+「Webアプリを構築する」️は [Jen Looper](https://twitter.com/jenlooper) によって執筆されました。
+
+小テストは Rohan Raj によって執筆されました。
+
+データセットは [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings) のものを元にしています。
+
+Webアプリのアーキテクチャは、Abhinav Sagar による [この記事](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) と [このリポジトリ](https://github.com/abhinavsagar/machine-learning-deployment) を一部参考にしています。
diff --git a/3-Web-App/translations/README.ko.md b/3-Web-App/translations/README.ko.md
new file mode 100644
index 000000000..45ba2b6a4
--- /dev/null
+++ b/3-Web-App/translations/README.ko.md
@@ -0,0 +1,22 @@
+# ML 모델을 사용하여 web app 만들기
+
+커리큘럼의 이 섹션에서, ML이 적용된 주제를 소개할 예정입니다: Scikit-learn 모델을 웹 애플리케이션에서 예측할 때 사용할 수 있는 파일로 저장해봅니다. 모델을 저장하고, Flask에 있는 웹 앱에서 어덯게 사용하는 지도 배웁니다. 먼저 UFO 목격 제보에 관련된 일부 데이터로 모델을 만듭니다! 그러면, 위도와 경도 값으로 몇 초 입력해서 UFO가 보고된 나라를 예측할 수 있는 웹 앱을 만들게 됩니다.
+
+![UFO Parking](../images/ufo.jpg)
+
+Photo by <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> on <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+
+## 강의
+
+1. [Web App 만들기](../1-Web-App/translations/README.ko.md)
+
+## 크레딧
+
+"Build a Web App" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper). 
+
+♥️ The quizzes were written by Rohan Raj.
+
+데이터셋은 [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings)에서 가져왔습니다.
+
+웹 앱 구조는 [this article](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4)과 Abhinav Sagar의 [this repo](https://github.com/abhinavsagar/machine-learning-deployment)에서 부분적으로 제안되었습니다.
\ No newline at end of file
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 4490131c7..03b0ba974 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -19,7 +19,9 @@ Remember:
 
 Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+
+> ### [This lesson is available in R!](./solution/R/lesson_10-R.ipynb)
 
 ### Introduction
 
@@ -163,7 +165,7 @@ Now you can dig deeper into the data and learn what are the typical ingredients
     def create_ingredient_df(df):
         ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
         ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
-        ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False,
         inplace=False)
         return ingredient_df
     ```
@@ -275,7 +277,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
     ```
 
     This fresh CSV can now be found in the root data folder.
@@ -286,7 +288,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
 
 This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/1-Introduction/images/dplyr_filter.jpg b/4-Classification/1-Introduction/images/dplyr_filter.jpg
new file mode 100644
index 000000000..8fa4eb2da
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diff --git a/4-Classification/1-Introduction/images/r_learners_sm.jpeg b/4-Classification/1-Introduction/images/r_learners_sm.jpeg
new file mode 100644
index 000000000..ff8d2945d
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diff --git a/4-Classification/1-Introduction/images/recipes.png b/4-Classification/1-Introduction/images/recipes.png
new file mode 100644
index 000000000..7fd24b06b
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diff --git a/4-Classification/1-Introduction/solution/Julia/README.md b/4-Classification/1-Introduction/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/4-Classification/1-Introduction/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..4592429f9
--- /dev/null
+++ b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,721 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "lesson_10-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a classification model: Delicious Asian and Indian Cuisines"
+      ],
+      "metadata": {
+        "id": "ItETB4tSFprR"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Introduction to classification: Clean, prep, and visualize your data\n",
+        "\n",
+        "In these four lessons, you will explore a fundamental focus of classic machine learning - *classification*. We will walk through using various classification algorithms with a dataset about all the brilliant cuisines of Asia and India. Hope you're hungry!\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/pinch.png\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Celebrate pan-Asian cuisines in these lessons! Image by Jen Looper</figcaption>\n",
+        "\n",
+        "\n",
+        "<!--![Celebrate pan-Asian cuisines in these lessons! Image by Jen Looper](images/pinch.png)-->\n",
+        "\n",
+        "Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with regression techniques. In classification, you train a model to predict which `category` an item belongs to. If machine learning is all about predicting values or names to things by using datasets, then classification generally falls into two groups: *binary classification* and *multiclass classification*.\n",
+        "\n",
+        "Remember:\n",
+        "\n",
+        "-   **Linear regression** helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict a numeric values such as *what price a pumpkin would be in September vs. December*, for example.\n",
+        "\n",
+        "-   **Logistic regression** helped you discover \"binary categories\": at this price point, *is this pumpkin orange or not-orange*?\n",
+        "\n",
+        "Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.\n",
+        "\n",
+        "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)\n",
+        "\n",
+        "### **Introduction**\n",
+        "\n",
+        "Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value (\"is this email spam or not?\"), to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it.\n",
+        "\n",
+        "To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables.\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/binary-multiclass.png\"\n",
+        "   width=\"600\"/>\n",
+        "   <figcaption>Binary vs. multiclass problems for classification algorithms to handle. Infographic by Jen Looper</figcaption>\n",
+        "\n",
+        "\n",
+        "\n",
+        "Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data.\n",
+        "\n",
+        "Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as `smoker`, `weight`, and `age` to determine *likelihood of developing X disease*. As a supervised learning technique similar to the regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome.\n",
+        "\n",
+        "✅ Take a moment to imagine a dataset about cuisines. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to use fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?\n",
+        "\n",
+        "### **Hello 'classifier'**\n",
+        "\n",
+        "The question we want to ask of this cuisine dataset is actually a **multiclass question**, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?\n",
+        "\n",
+        "Tidymodels offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two lessons, you'll learn about several of these algorithms.\n",
+        "\n",
+        "#### **Prerequisite**\n",
+        "\n",
+        "For this lesson, we'll require the following packages to clean, prep and visualize our data:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "-   `DataExplorer`: The [DataExplorer package](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) is meant to simplify and automate EDA process and report generation.\n",
+        "\n",
+        "-   `themis`: The [themis package](https://themis.tidymodels.org/) provides Extra Recipes Steps for Dealing with Unbalanced Data.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+        "\n",
+        "Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ],
+      "metadata": {
+        "id": "ri5bQxZ-Fz_0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+        "\r\n",
+        "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "KIPxa4elGAPI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll later load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)"
+      ],
+      "metadata": {
+        "id": "YkKAxOJvGD4C"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise - clean and balance your data\n",
+        "\n",
+        "The first task at hand, before starting this project, is to clean and **balance** your data to get better results\n",
+        "\n",
+        "Let's meet the data!🕵️"
+      ],
+      "metadata": {
+        "id": "PFkQDlk0GN5O"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Import data\r\n",
+        "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+        "\r\n",
+        "# View the first 5 rows\r\n",
+        "df %>% \r\n",
+        "  slice_head(n = 5)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Qccw7okxGT0S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Interesting! From the looks of it, the first column is a kind of `id` column. Let's get a little more information about the data."
+      ],
+      "metadata": {
+        "id": "XrWnlgSrGVmR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Basic information about the data\r\n",
+        "df %>%\r\n",
+        "  introduce()\r\n",
+        "\r\n",
+        "# Visualize basic information above\r\n",
+        "df %>% \r\n",
+        "  plot_intro(ggtheme = theme_light())"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "4UcGmxRxGieA"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the output, we can immediately see that we have `2448` rows and `385` columns and `0` missing values. We also have 1 discrete column, *cuisine*.\n",
+        "\n",
+        "## Exercise - learning about cuisines\n",
+        "\n",
+        "Now the work starts to become more interesting. Let's discover the distribution of data, per cuisine."
+      ],
+      "metadata": {
+        "id": "AaPubl__GmH5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Count observations per cuisine\r\n",
+        "df %>% \r\n",
+        "  count(cuisine) %>% \r\n",
+        "  arrange(n)\r\n",
+        "\r\n",
+        "# Plot the distribution\r\n",
+        "theme_set(theme_light())\r\n",
+        "df %>% \r\n",
+        "  count(cuisine) %>% \r\n",
+        "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+        "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+        "  ylab(\"cuisine\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "FRsBVy5eGrrv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "There are a finite number of cuisines, but the distribution of data is uneven. You can fix that! Before doing so, explore a little more.\r\n",
+        "\r\n",
+        "Next, let's assign each cuisine into its individual tibble and find out how much data is available (rows, columns) per cuisine.\r\n",
+        "\r\n",
+        "> A [tibble](https://tibble.tidyverse.org/) is a modern data frame.\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../../images/dplyr_filter.jpg\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n"
+      ],
+      "metadata": {
+        "id": "vVvyDb1kG2in"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create individual tibble for the cuisines\r\n",
+        "thai_df <- df %>% \r\n",
+        "  filter(cuisine == \"thai\")\r\n",
+        "japanese_df <- df %>% \r\n",
+        "  filter(cuisine == \"japanese\")\r\n",
+        "chinese_df <- df %>% \r\n",
+        "  filter(cuisine == \"chinese\")\r\n",
+        "indian_df <- df %>% \r\n",
+        "  filter(cuisine == \"indian\")\r\n",
+        "korean_df <- df %>% \r\n",
+        "  filter(cuisine == \"korean\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Find out how much data is avilable per cuisine\r\n",
+        "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+        "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+        "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+        "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+        "    \"korean_df:\", dim(korean_df))"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "0TvXUxD3G8Bk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Perfect!😋\n",
+        "\n",
+        "## **Exercise - Discovering top ingredients by cuisine using dplyr**\n",
+        "\n",
+        "Now you can dig deeper into the data and learn what are the typical ingredients per cuisine. You should clean out recurrent data that creates confusion between cuisines, so let's learn about this problem.\n",
+        "\n",
+        "\n",
+        "Create a function `create_ingredient()` in R that returns an ingredient dataframe. This function will start by dropping an unhelpful column and sort through ingredients by their count.\n",
+        "\n",
+        "The basic structure of a function in R is:\n",
+        "\n",
+        "`myFunction <- function(arglist){`\n",
+        "\n",
+        "**`...`**\n",
+        "\n",
+        "**`return`**`(value)`\n",
+        "\n",
+        "`}`\n",
+        "\n",
+        "A tidy introduction to R functions can be found [here](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+        "\n",
+        "Let's get right into it! We'll make use of [dplyr verbs](https://dplyr.tidyverse.org/) which we have been learning in our previous lessons. As a recap:\n",
+        "\n",
+        "-   `dplyr::select()`: help you pick which **columns** to keep or exclude.\n",
+        "\n",
+        "-   `dplyr::pivot_longer()`: helps you to \"lengthen\" data, increasing the number of rows and decreasing the number of columns.\n",
+        "\n",
+        "-   `dplyr::group_by()` and `dplyr::summarise()`: helps you to find find summary statistics for different groups, and put them in a nice table.\n",
+        "\n",
+        "-   `dplyr::filter()`: creates a subset of the data only containing rows that satisfy your conditions.\n",
+        "\n",
+        "-   `dplyr::mutate()`: helps you to create or modify columns.\n",
+        "\n",
+        "Check out this [*art*-filled learnr tutorial](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) by Allison Horst, that introduces some useful data wrangling functions in dplyr *(part of the Tidyverse)*"
+      ],
+      "metadata": {
+        "id": "K3RF5bSCHC76"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Creates a functions that returns the top ingredients by class\r\n",
+        "\r\n",
+        "create_ingredient <- function(df){\r\n",
+        "  \r\n",
+        "  # Drop the id column which is the first colum\r\n",
+        "  ingredient_df = df %>% select(-1) %>% \r\n",
+        "  # Transpose data to a long format\r\n",
+        "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+        "  # Find the top most ingredients for a particular cuisine\r\n",
+        "    group_by(ingredients) %>% \r\n",
+        "    summarise(n_instances = sum(count)) %>% \r\n",
+        "    filter(n_instances != 0) %>% \r\n",
+        "  # Arrange by descending order\r\n",
+        "    arrange(desc(n_instances)) %>% \r\n",
+        "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+        "  \r\n",
+        "  \r\n",
+        "  return(ingredient_df)\r\n",
+        "} # End of function"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "uB_0JR82HTPa"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we can use the function to get an idea of top ten most popular ingredient by cuisine. Let's take it out for a spin with `thai_df`\n"
+      ],
+      "metadata": {
+        "id": "h9794WF8HWmc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Call create_ingredient and display popular ingredients\r\n",
+        "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+        "\r\n",
+        "thai_ingredient_df %>% \r\n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "agQ-1HrcHaEA"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In the previous section, we used `geom_col()`, let's see how you can use `geom_bar` too, to create bar charts. Use `?geom_bar` for further reading."
+      ],
+      "metadata": {
+        "id": "kHu9ffGjHdcX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a bar chart for popular thai cuisines\r\n",
+        "thai_ingredient_df %>% \r\n",
+        "  slice_head(n = 10) %>% \r\n",
+        "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+        "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+        "  xlab(\"\") + ylab(\"\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "fb3Bx_3DHj6e"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's do the same for the Japanese data"
+      ],
+      "metadata": {
+        "id": "RHP_xgdkHnvM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+        "create_ingredient(df = japanese_df) %>% \r\n",
+        "  slice_head(n = 10) %>%\r\n",
+        "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+        "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+        "  xlab(\"\") + ylab(\"\")\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "019v8F0XHrRU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "What about the Chinese cuisines?\n"
+      ],
+      "metadata": {
+        "id": "iIGM7vO8Hu3v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+        "create_ingredient(df = chinese_df) %>% \r\n",
+        "  slice_head(n = 10) %>%\r\n",
+        "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+        "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+        "  xlab(\"\") + ylab(\"\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "lHd9_gd2HyzU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a look at the Indian cuisines 🌶️."
+      ],
+      "metadata": {
+        "id": "ir8qyQbNH1c7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+        "create_ingredient(df = indian_df) %>% \r\n",
+        "  slice_head(n = 10) %>%\r\n",
+        "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+        "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+        "  xlab(\"\") + ylab(\"\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ApukQtKjH5FO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, plot the Korean ingredients."
+      ],
+      "metadata": {
+        "id": "qv30cwY1H-FM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+        "create_ingredient(df = korean_df) %>% \r\n",
+        "  slice_head(n = 10) %>%\r\n",
+        "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+        "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+        "  xlab(\"\") + ylab(\"\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "lumgk9cHIBie"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the data visualizations, we can now drop the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.\n",
+        "\n",
+        "Everyone loves rice, garlic and ginger!"
+      ],
+      "metadata": {
+        "id": "iO4veMXuIEta"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+        "df_select <- df %>% \r\n",
+        "  select(-c(1, rice, garlic, ginger))\r\n",
+        "\r\n",
+        "# Display new data set\r\n",
+        "df_select %>% \r\n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "iHJPiG6rIUcK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Preprocessing data using recipes 👩‍🍳👨‍🍳 - Dealing with imbalanced data ⚖️\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../../images/recipes.png\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "Given that this lesson is about cuisines, we have to put `recipes` into context .\r\n",
+        "\r\n",
+        "Tidymodels provides yet another neat package: `recipes`- a package for preprocessing data.\r\n"
+      ],
+      "metadata": {
+        "id": "kkFd-JxdIaL6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a look at the distribution of our cuisines again.\n"
+      ],
+      "metadata": {
+        "id": "6l2ubtTPJAhY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Distribution of cuisines\r\n",
+        "old_label_count <- df_select %>% \r\n",
+        "  count(cuisine) %>% \r\n",
+        "  arrange(desc(n))\r\n",
+        "\r\n",
+        "old_label_count"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "1e-E9cb7JDVi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "As you can see, there is quite an unequal distribution in the number of cuisines. Korean cuisines are almost 3 times Thai cuisines. Imbalanced data often has negative effects on the model performance. Think about a binary classification. If most of your data is one class, a ML model is going to predict that class more frequently, just because there is more data for it. Balancing the data takes any skewed data and helps remove this imbalance. Many models perform best when the number of observations is equal and, thus, tend to struggle with unbalanced data.\n",
+        "\n",
+        "There are majorly two ways of dealing with imbalanced data sets:\n",
+        "\n",
+        "-   adding observations to the minority class: `Over-sampling` e.g using a SMOTE algorithm\n",
+        "\n",
+        "-   removing observations from majority class: `Under-sampling`\n",
+        "\n",
+        "Let's now demonstrate how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis."
+      ],
+      "metadata": {
+        "id": "soAw6826JKx9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load themis package for dealing with imbalanced data\r\n",
+        "library(themis)\r\n",
+        "\r\n",
+        "# Create a recipe for preprocessing data\r\n",
+        "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+        "  step_smote(cuisine)\r\n",
+        "\r\n",
+        "cuisines_recipe"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "HS41brUIJVJy"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's break down our preprocessing steps.\n",
+        "\n",
+        "-   The call to `recipe()` with a formula tells the recipe the *roles* of the variables using `df_select` data as the reference. For instance the `cuisine` column has been assigned an `outcome` role while the rest of the columns have been assigned a `predictor` role.\n",
+        "\n",
+        "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) creates a *specification* of a recipe step that synthetically generates new examples of the minority class using nearest neighbors of these cases.\n",
+        "\n",
+        "Now, if we wanted to see the preprocessed data, we'd have to [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) and [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) our recipe.\n",
+        "\n",
+        "`prep()`: estimates the required parameters from a training set that can be later applied to other data sets.\n",
+        "\n",
+        "`bake()`: takes a prepped recipe and applies the operations to any data set.\n"
+      ],
+      "metadata": {
+        "id": "Yb-7t7XcJaC8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Prep and bake the recipe\r\n",
+        "preprocessed_df <- cuisines_recipe %>% \r\n",
+        "  prep() %>% \r\n",
+        "  bake(new_data = NULL) %>% \r\n",
+        "  relocate(cuisine)\r\n",
+        "\r\n",
+        "# Display data\r\n",
+        "preprocessed_df %>% \r\n",
+        "  slice_head(n = 5)\r\n",
+        "\r\n",
+        "# Quick summary stats\r\n",
+        "preprocessed_df %>% \r\n",
+        "  introduce()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "9QhSgdpxJl44"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's now check the distribution of our cuisines and compare them with the imbalanced data."
+      ],
+      "metadata": {
+        "id": "dmidELh_LdV7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Distribution of cuisines\r\n",
+        "new_label_count <- preprocessed_df %>% \r\n",
+        "  count(cuisine) %>% \r\n",
+        "  arrange(desc(n))\r\n",
+        "\r\n",
+        "list(new_label_count = new_label_count,\r\n",
+        "     old_label_count = old_label_count)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "aSh23klBLwDz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Yum! The data is nice and clean, balanced, and very delicious 😋!\n",
+        "\n",
+        "> Normally, a recipe is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case, a `workflow()` is typically used (as we have already seen in our previous lessons) instead of manually estimating a recipe\n",
+        ">\n",
+        "> As such, you don't typically need to **`prep()`** and **`bake()`** recipes when you use tidymodels, but they are helpful functions to have in your toolkit for confirming that recipes are doing what you expect like in our case.\n",
+        ">\n",
+        "> When you **`bake()`** a prepped recipe with **`new_data = NULL`**, you get the data that you provided when defining the recipe back, but having undergone the preprocessing steps.\n",
+        "\n",
+        "Let's now save a copy of this data for use in future lessons:\n"
+      ],
+      "metadata": {
+        "id": "HEu80HZ8L7ae"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Save preprocessed data\r\n",
+        "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "cBmCbIgrMOI6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This fresh CSV can now be found in the root data folder.\r\n",
+        "\r\n",
+        "**🚀Challenge**\r\n",
+        "\r\n",
+        "This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?\r\n",
+        "\r\n",
+        "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)\r\n",
+        "\r\n",
+        "## **Review & Self Study**\r\n",
+        "\r\n",
+        "-   Check out [package themis](https://github.com/tidymodels/themis). What other techniques could we use to deal with imbalanced data?\r\n",
+        "\r\n",
+        "-   Tidy models [reference website](https://www.tidymodels.org/start/).\r\n",
+        "\r\n",
+        "-   H. Wickham and G. Grolemund, [*R for Data Science: Visualize, Model, Transform, Tidy, and Import Data*](https://r4ds.had.co.nz/).\r\n",
+        "\r\n",
+        "#### THANK YOU TO:\r\n",
+        "\r\n",
+        "[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\r\n",
+        "\r\n",
+        "[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../../images/r_learners_sm.jpeg\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n"
+      ],
+      "metadata": {
+        "id": "WQs5621pMGwf"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
new file mode 100644
index 000000000..b979d56fe
--- /dev/null
+++ b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
@@ -0,0 +1,422 @@
+---
+title: 'Build a classification model: Delicious Asian and Indian Cuisines'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Introduction to classification: Clean, prep, and visualize your data
+
+In these four lessons, you will explore a fundamental focus of classic machine learning - *classification*. We will walk through using various classification algorithms with a dataset about all the brilliant cuisines of Asia and India. Hope you're hungry!
+
+![Celebrate pan-Asian cuisines in these lessons! Image by Jen Looper](../../images/pinch.png)
+
+Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with regression techniques. In classification, you train a model to predict which `category` an item belongs to. If machine learning is all about predicting values or names to things by using datasets, then classification generally falls into two groups: *binary classification* and *multiclass classification*.
+
+Remember:
+
+-   **Linear regression** helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict a numeric values such as *what price a pumpkin would be in September vs. December*, for example.
+
+-   **Logistic regression** helped you discover "binary categories": at this price point, *is this pumpkin orange or not-orange*?
+
+Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
+
+### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+
+### **Introduction**
+
+Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value ("is this email spam or not?"), to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it.
+
+To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables.
+
+![Binary vs. multiclass problems for classification algorithms to handle. Infographic by Jen Looper](../../images/binary-multiclass.png){width="500"}
+
+Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data.
+
+Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as `smoker`, `weight`, and `age` to determine *likelihood of developing X disease*. As a supervised learning technique similar to the regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome.
+
+✅ Take a moment to imagine a dataset about cuisines. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to use fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?
+
+### **Hello 'classifier'**
+
+The question we want to ask of this cuisine dataset is actually a **multiclass question**, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?
+
+Tidymodels offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two lessons, you'll learn about several of these algorithms.
+
+#### **Prerequisite**
+
+For this lesson, we'll require the following packages to clean, prep and visualize our data:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `DataExplorer`: The [DataExplorer package](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) is meant to simplify and automate EDA process and report generation.
+
+-   `themis`: The [themis package](https://themis.tidymodels.org/) provides Extra Recipes Steps for Dealing with Unbalanced Data.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "DataExplorer", "here"))`
+
+Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman"))install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)
+```
+
+We'll later load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)
+
+## Exercise - clean and balance your data
+
+The first task at hand, before starting this project, is to clean and **balance** your data to get better results
+
+Let's meet the data!🕵️
+
+```{r import_data}
+# Import data
+df <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv")
+
+# View the first 5 rows
+df %>% 
+  slice_head(n = 5)
+
+```
+
+Interesting! From the looks of it, the first column is a kind of `id` column. Let's get a little more information about the data.
+
+```{r info}
+# Basic information about the data
+df %>%
+  introduce()
+
+# Visualize basic information above
+df %>% 
+  plot_intro(ggtheme = theme_light())
+```
+
+From the output, we can immediately see that we have `2448` rows and `385` columns and `0` missing values. We also have 1 discrete column, *cuisine*.
+
+## Exercise - learning about cuisines
+
+1.  Now the work starts to become more interesting. Let's discover the distribution of data, per cuisine.
+
+```{r filter_cuisine}
+# Count observations per cuisine
+df %>% 
+  count(cuisine) %>% 
+  arrange(n)
+
+# Plot the distribution
+theme_set(theme_light())
+df %>% 
+  count(cuisine) %>% 
+  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +
+  geom_col(fill = "midnightblue", alpha = 0.7) +
+  ylab("cuisine")
+```
+
+There are a finite number of cuisines, but the distribution of data is uneven. You can fix that! Before doing so, explore a little more.
+
+2.  Next, let's assign each cuisine into it's individual tibble and find out how much data is available (rows, columns) per cuisine.
+
+> A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not.
+
+![Artwork by \@allison_horst](../../images/dplyr_filter.jpg)
+
+```{r cuisine_df}
+# Create individual tibbles for the cuisines
+thai_df <- df %>% 
+  filter(cuisine == "thai")
+japanese_df <- df %>% 
+  filter(cuisine == "japanese")
+chinese_df <- df %>% 
+  filter(cuisine == "chinese")
+indian_df <- df %>% 
+  filter(cuisine == "indian")
+korean_df <- df %>% 
+  filter(cuisine == "korean")
+
+
+# Find out how much data is avilable per cuisine
+cat(" thai df:", dim(thai_df), "\n",
+    "japanese df:", dim(japanese_df), "\n",
+    "chinese_df:", dim(chinese_df), "\n",
+    "indian_df:", dim(indian_df), "\n",
+    "korean_df:", dim(korean_df))
+```
+
+Perfect!😋
+
+## **Exercise - Discovering top ingredients by cuisine using dplyr**
+
+Now you can dig deeper into the data and learn what are the typical ingredients per cuisine. You should clean out recurrent data that creates confusion between cuisines, so let's learn about this problem.
+
+1.  Create a function `create_ingredient()` in R that returns an ingredient dataframe. This function will start by dropping an unhelpful column and sort through ingredients by their count.
+
+The basic structure of a function in R is:
+
+`myFunction <- function(arglist){`
+
+**`...`**
+
+**`return`**`(value)`
+
+`}`
+
+A tidy introduction to R functions can be found [here](https://skirmer.github.io/presentations/functions_with_r.html#1).
+
+Let's get right into it! We'll make use of [dplyr verbs](https://dplyr.tidyverse.org/) which we have been learning in our previous lessons. As a recap:
+
+-   `dplyr::select()`: help you pick which **columns** to keep or exclude.
+
+-   `dplyr::pivot_longer()`: helps you to "lengthen" data, increasing the number of rows and decreasing the number of columns.
+
+-   `dplyr::group_by()` and `dplyr::summarise()`: helps you to find find summary statistics for different groups, and put them in a nice table.
+
+-   `dplyr::filter()`: creates a subset of the data only containing rows that satisfy your conditions.
+
+-   `dplyr::mutate()`: helps you to create or modify columns.
+
+Check out this [*art*-filled learnr tutorial](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) by Allison Horst, that introduces some useful data wrangling functions in dplyr *(part of the Tidyverse)*
+
+```{r create_ingredient}
+# Creates a functions that returns the top ingredients by class
+
+create_ingredient <- function(df){
+  
+  # Drop the id column which is the first colum
+  ingredient_df = df %>% select(-1) %>% 
+  # Transpose data to a long format
+    pivot_longer(!cuisine, names_to = "ingredients", values_to = "count") %>% 
+  # Find the top most ingredients for a particular cuisine
+    group_by(ingredients) %>% 
+    summarise(n_instances = sum(count)) %>% 
+    filter(n_instances != 0) %>% 
+  # Arrange by descending order
+    arrange(desc(n_instances)) %>% 
+    mutate(ingredients = factor(ingredients) %>% fct_inorder())
+  
+  
+  return(ingredient_df)
+} # End of function
+
+```
+
+2.  Now we can use the function to get an idea of top ten most popular ingredient by cuisine. Let's take it out for a spin with `thai_df`
+
+```{r thai_ingredient_df}
+# Call create_ingredient and display popular ingredients
+thai_ingredient_df <- create_ingredient(df = thai_df)
+
+thai_ingredient_df %>% 
+  slice_head(n = 10)
+
+```
+
+In the previous section, we used `geom_col()`, let's see how you can use `geom_bar` too, to create bar charts. Use `?geom_bar` for further reading.
+
+```{r thai_chart}
+# Make a bar chart for popular thai cuisines
+thai_ingredient_df %>% 
+  slice_head(n = 10) %>% 
+  ggplot(aes(x = n_instances, y = ingredients)) +
+  geom_bar(stat = "identity", width = 0.5, fill = "steelblue") +
+  xlab("") + ylab("")
+
+```
+
+3.  Let's do the same for the Japanese data
+
+```{r japanese_ingredient_df}
+# Get popular ingredients for Japanese cuisines and make bar chart
+create_ingredient(df = japanese_df) %>% 
+  slice_head(n = 10) %>%
+  ggplot(aes(x = n_instances, y = ingredients)) +
+  geom_bar(stat = "identity", width = 0.5, fill = "darkorange", alpha = 0.8) +
+  xlab("") + ylab("")
+
+
+
+```
+
+4.  What about the Chinese cuisines?
+
+```{r chinese_ingredient_df}
+# Get popular ingredients for Chinese cuisines and make bar chart
+create_ingredient(df = chinese_df) %>% 
+  slice_head(n = 10) %>%
+  ggplot(aes(x = n_instances, y = ingredients)) +
+  geom_bar(stat = "identity", width = 0.5, fill = "cyan4", alpha = 0.8) +
+  xlab("") + ylab("")
+
+
+```
+
+5.  Let's take a look at the Indian cuisines 🌶️.
+
+```{r indian_ingredient_df }
+# Get popular ingredients for Indian cuisines and make bar chart
+create_ingredient(df = indian_df) %>% 
+  slice_head(n = 10) %>%
+  ggplot(aes(x = n_instances, y = ingredients)) +
+  geom_bar(stat = "identity", width = 0.5, fill = "#041E42FF", alpha = 0.8) +
+  xlab("") + ylab("")
+```
+
+6.  Finally, plot the Korean ingredients.
+
+```{r korean_ingredient_df }
+# Get popular ingredients for Korean cuisines and make bar chart
+create_ingredient(df = korean_df) %>% 
+  slice_head(n = 10) %>%
+  ggplot(aes(x = n_instances, y = ingredients)) +
+  geom_bar(stat = "identity", width = 0.5, fill = "#852419FF", alpha = 0.8) +
+  xlab("") + ylab("")
+```
+
+7.  From the data visualizations, we can now drop the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.
+
+Everyone loves rice, garlic and ginger!
+
+```{r df_select}
+# Drop rice, garlic and ginger from our original data set
+df_select <- df %>% 
+  select(-c(1, rice, garlic, ginger))
+
+# Display new data set
+df_select %>% 
+  slice_head(n = 5)
+
+```
+
+## Preprocessing data using recipes 👩‍🍳👨‍🍳 - Dealing with imbalanced data ⚖️
+
+![Artwork by \@allison_horst](../../images/recipes.png)
+
+Given that this lesson is about cuisines, we have to put `recipes` into context .
+
+Tidymodels provides yet another neat package: `recipes`- a package for preprocessing data.
+
+Now we are on the same page 😅.
+
+Let's take a look at the distribution of our cuisines again.
+
+```{r df_select_n}
+# Distribution of cuisines
+old_label_count <- df_select %>% 
+  count(cuisine) %>% 
+  arrange(desc(n))
+
+old_label_count
+```
+
+As you can see, there is quite an unequal distribution in the number of cuisines. Korean cuisines are almost 3 times Thai cuisines. Imbalanced data often has negative effects on the model performance. Think about a binary classification. If most of your data is one class, a ML model is going to predict that class more frequently, just because there is more data for it. Balancing the data takes any skewed data and helps remove this imbalance. Many models perform best when the number of observations is equal and, thus, tend to struggle with unbalanced data.
+
+There are majorly two ways of dealing with imbalanced data sets:
+
+-   adding observations to the minority class: `Over-sampling` e.g using a SMOTE algorithm
+
+-   removing observations from majority class: `Under-sampling`
+
+Let's now demonstrate how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis.
+
+```{r recipe}
+# Load themis package for dealing with imbalanced data
+library(themis)
+
+# Create a recipe for preprocessing data
+cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% 
+  step_smote(cuisine)
+
+cuisines_recipe
+```
+
+Let's break down our preprocessing steps.
+
+-   The call to `recipe()` with a formula tells the recipe the *roles* of the variables using `df_select` data as the reference. For instance the `cuisine` column has been assigned an `outcome` role while the rest of the columns have been assigned a `predictor` role.
+
+-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) creates a *specification* of a recipe step that synthetically generates new examples of the minority class using nearest neighbors of these cases.
+
+Now, if we wanted to see the preprocessed data, we'd have to [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) and [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) our recipe.
+
+`prep()`: estimates the required parameters from a training set that can be later applied to other data sets.
+
+`bake()`: takes a prepped recipe and applies the operations to any data set.
+
+```{r prep_bake}
+# Prep and bake the recipe
+preprocessed_df <- cuisines_recipe %>% 
+  prep() %>% 
+  bake(new_data = NULL) %>% 
+  relocate(cuisine)
+
+# Display data
+preprocessed_df %>% 
+  slice_head(n = 5)
+
+# Quick summary stats
+preprocessed_df %>% 
+  introduce()
+
+```
+
+Let's now check the distribution of our cuisines and compare them with the imbalanced data.
+
+```{r prep_cuisines}
+# Distribution of cuisines
+new_label_count <- preprocessed_df %>% 
+  count(cuisine) %>% 
+  arrange(desc(n))
+
+list(new_label_count = new_label_count,
+     old_label_count = old_label_count)
+
+```
+
+Yum! The data is nice and clean, balanced, and very delicious 😋!
+
+> Normally, a recipe is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case, a `workflow()` is typically used (as we have already seen in our previous lessons) instead of manually estimating a recipe
+>
+> As such, you don't typically need to **`prep()`** and **`bake()`** recipes when you use tidymodels, but they are helpful functions to have in your toolkit for confirming that recipes are doing what you expect like in our case.
+>
+> When you **`bake()`** a prepped recipe with **`new_data = NULL`**, you get the data that you provided when defining the recipe back, but having undergone the preprocessing steps.
+
+Let's now save a copy of this data for use in future lessons:
+
+```{r save_preproc_data}
+# Save preprocessed data
+write_csv(preprocessed_df, "../../data/cleaned_cuisines_R.csv")
+
+```
+
+This fresh CSV can now be found in the root data folder.
+
+**🚀Challenge**
+
+This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
+
+## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+
+## **Review & Self Study**
+
+-   Check out [package themis](https://github.com/tidymodels/themis). What other techniques could we use to deal with imbalanced data?
+
+-   Tidy models [reference website](https://www.tidymodels.org/start/).
+
+-   H. Wickham and G. Grolemund, [*R for Data Science: Visualize, Model, Transform, Tidy, and Import Data*](https://r4ds.had.co.nz/).
+
+#### THANK YOU TO:
+
+[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).
+
+[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️
+
+![Artwork by \@allison_horst](../../images/r_learners_sm.jpeg)
diff --git a/4-Classification/1-Introduction/solution/notebook.ipynb b/4-Classification/1-Introduction/solution/notebook.ipynb
index c5b8c6299..5abb9693d 100644
--- a/4-Classification/1-Introduction/solution/notebook.ipynb
+++ b/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -622,7 +622,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transformed_df.to_csv(\"../../data/cleaned_cuisine.csv\")"
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
    ]
   },
   {
diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md
new file mode 100644
index 000000000..8115bb8d1
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/README.it.md
@@ -0,0 +1,297 @@
+# Introduzione alla classificazione
+
+In queste quattro lezioni si esplorerà un focus fondamentale del machine learning classico: _la classificazione_. Verrà analizzato l'utilizzo di vari algoritmi di classificazione con un insieme di dati su tutte le brillanti cucine dell'Asia e dell'India. Si spera siate affamati!
+
+![solo un pizzico!](../images/pinch.png)
+
+> In queste lezioni di celebrano le cucine panasiatiche! Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+La classificazione è una forma di [apprendimento supervisionato](https://it.wikipedia.org/wiki/Apprendimento_supervisionato) che ha molto in comune con le tecniche di regressione. Se machine learning riguarda la previsione di valori o nomi di cose utilizzando insiemi di dati, la classificazione generalmente rientra in due gruppi: _classificazione binaria_ e _classificazione multiclasse_.
+
+[![Introduzione allaclassificazione](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduzione alla classificazione")
+
+> 🎥 Fare clic sull'immagine sopra per un video: John Guttag del MIT introduce la classificazione
+
+Ricordare:
+
+- La **regressione lineare** ha aiutato a prevedere le relazioni tra le variabili e a fare previsioni accurate su dove un nuovo punto dati si sarebbe posizionato in relazione a quella linea. Quindi, si potrebbe prevedere _quale prezzo avrebbe una zucca a settembre rispetto a dicembre_, ad esempio.
+- La **regressione logistica** ha aiutato a scoprire le "categorie binarie": a questo prezzo, _questa zucca è arancione o non arancione_?
+
+La classificazione utilizza vari algoritmi per determinare altri modi per definire l'etichetta o la classe di un punto dati. Si lavorerà con questi dati di cucina per vedere se, osservando un gruppo di ingredienti, è possibile determinarne la cucina di origine.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=it)
+
+### Introduzione
+
+La classificazione è una delle attività fondamentali del ricercatore di machine learning e data scientist. Dalla classificazione basica di un valore binario ("questa email è spam o no?"), alla complessa classificazione e segmentazione di immagini utilizzando la visione artificiale, è sempre utile essere in grado di ordinare i dati in classi e porre domande su di essi.
+
+Per definire il processo in modo più scientifico, il metodo di classificazione crea un modello predittivo che consente di mappare la relazione tra le variabili di input e le variabili di output.
+
+![classificazione binaria vs. multiclasse](../images/binary-multiclass.png)
+
+> Problemi binari e multiclasse per la gestione di algoritmi di classificazione. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+Prima di iniziare il processo di pulizia dei dati, visualizzazione e preparazione per le attività di machine learning, si apprenderà qualcosa circa i vari modi in cui machine learning può essere sfruttato per classificare i dati.
+
+Derivata dalla [statistica](https://it.wikipedia.org/wiki/Classificazione_statistica), la classificazione che utilizza machine learning classico utilizza caratteristiche come l'`essere fumatore`, il `peso` e l'`età` per determinare _la probabilità di sviluppare la malattia X._ Essendo una tecnica di apprendimento supervisionata simile agli esercizi di regressione eseguiti in precedenza, i dati vengono etichettati e gli algoritmi ML utilizzano tali etichette per classificare e prevedere le classi (o "caratteristiche") di un insieme di dati e assegnarle a un gruppo o risultato.
+
+✅ Si prenda un momento per immaginare un insieme di dati sulle cucine. A cosa potrebbe rispondere un modello multiclasse? A cosa potrebbe rispondere un modello binario? Se si volesse determinare se una determinata cucina potrebbe utilizzare il fieno greco? Se si volesse vedere se, regalando una busta della spesa piena di anice stellato, carciofi, cavolfiori e rafano, si possa creare un piatto tipico indiano?
+
+[![Cesti misteriosi pazzeschi](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU " Cestini misteriosi pazzeschi")
+
+> 🎥 Fare clic sull'immagine sopra per un video. L'intera premessa dello spettacolo 'Chopped' è il 'cesto misterioso' dove gli chef devono preparare un piatto con una scelta casuale di ingredienti. Sicuramente un modello ML avrebbe aiutato!
+
+## Ciao 'classificatore'
+
+La domanda che si vuole porre a questo insieme di dati sulla cucina è in realtà una **domanda multiclasse**, poiché ci sono diverse potenziali cucine nazionali con cui lavorare. Dato un lotto di ingredienti, in quale di queste molte classi si identificheranno i dati?
+
+Scikit-learn offre diversi algoritmi da utilizzare per classificare i dati, a seconda del tipo di problema che si desidera risolvere. Nelle prossime due lezioni si impareranno a conoscere molti di questi algoritmi.
+
+## Esercizio: pulire e bilanciare i dati
+
+Il primo compito, prima di iniziare questo progetto, sarà pulire e **bilanciare** i dati per ottenere risultati migliori. Si inizia con il file vuoto _notebook.ipynb_ nella radice di questa cartella.
+
+La prima cosa da installare è [imblearn](https://imbalanced-learn.org/stable/). Questo è un pacchetto di apprendimento di Scikit che consentirà di bilanciare meglio i dati (si imparerà di più su questa attività tra un minuto).
+
+1. Per installare `imblearn`, eseguire `pip install`, in questo modo:
+
+   ```python
+   pip install imblearn
+   ```
+
+1. Importare i pacchetti necessari per caricare i dati e visualizzarli, importare anche `SMOTE` da `imblearn`.
+
+   ```python
+   import pandas as pd
+   import matplotlib.pyplot as plt
+   import matplotlib as mpl
+   import numpy as np
+   from imblearn.over_sampling import SMOTE
+   ```
+
+   Ora si è pronti per la successiva importazione dei dati.
+
+1. Il prossimo compito sarà quello di importare i dati:
+
+   ```python
+   df  = pd.read_csv('../data/cuisines.csv')
+   ```
+
+   Usando `read_csv()` si leggerà il contenuto del file csv _cusines.csv_ e lo posizionerà nella variabile `df`.
+
+1. Controllare la forma dei dati:
+
+   ```python
+   df.head()
+   ```
+
+   Le prime cinque righe hanno questo aspetto:
+
+   ```output
+   |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+   | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+   | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+   ```
+
+1. Si possono ottienere informazioni su questi dati chiamando `info()`:
+
+   ```python
+   df.info()
+   ```
+
+   Il risultato assomiglia a:
+
+   ```output
+   <class 'pandas.core.frame.DataFrame'>
+   RangeIndex: 2448 entries, 0 to 2447
+   Columns: 385 entries, Unnamed: 0 to zucchini
+   dtypes: int64(384), object(1)
+   memory usage: 7.2+ MB
+   ```
+
+## Esercizio - conoscere le cucine
+
+Ora il lavoro inizia a diventare più interessante. Si scoprirà la distribuzione dei dati, per cucina
+
+1. Tracciare i dati come barre chiamando `barh()`:
+
+   ```python
+   df.cuisine.value_counts().plot.barh()
+   ```
+
+   ![distribuzione dati cuisine](../images/cuisine-dist.png)
+
+   Esiste un numero finito di cucine, ma la distribuzione dei dati non è uniforme. Si può sistemare! Prima di farlo, occorre esplorare un po' di più.
+
+1. Si deve scoprire quanti dati sono disponibili per cucina e stamparli:
+
+   ```python
+   thai_df = df[(df.cuisine == "thai")]
+   japanese_df = df[(df.cuisine == "japanese")]
+   chinese_df = df[(df.cuisine == "chinese")]
+   indian_df = df[(df.cuisine == "indian")]
+   korean_df = df[(df.cuisine == "korean")]
+
+   print(f'thai df: {thai_df.shape}')
+   print(f'japanese df: {japanese_df.shape}')
+   print(f'chinese df: {chinese_df.shape}')
+   print(f'indian df: {indian_df.shape}')
+   print(f'korean df: {korean_df.shape}')
+   ```
+
+   il risultato si presenta così:
+
+   ```output
+   thai df: (289, 385)
+   japanese df: (320, 385)
+   chinese df: (442, 385)
+   indian df: (598, 385)
+   korean df: (799, 385)
+   ```
+
+## Alla scoperta degli ingredienti
+
+Ora si possono approfondire i dati e scoprire quali sono gli ingredienti tipici per cucina. Si dovrebbero ripulire i dati ricorrenti che creano confusione tra le cucine, quindi si affronterà questo problema.
+
+1. Creare una funzione `create_ingredient()` in Python per creare un dataframe ingredient Questa funzione inizierà eliminando una colonna non utile e ordinando gli ingredienti in base al loro conteggio:
+
+   ```python
+   def create_ingredient_df(df):
+       ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+       ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+       ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+       inplace=False)
+       return ingredient_df
+   ```
+
+   Ora si può usare questa funzione per farsi un'idea dei primi dieci ingredienti più popolari per cucina.
+
+1. Chiamare `create_ingredient_df()` e tracciare il grafico chiamando `barh()`:
+
+   ```python
+   thai_ingredient_df = create_ingredient_df(thai_df)
+   thai_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![thai](../images/thai.png)
+
+1. Fare lo stesso per i dati giapponesi:
+
+   ```python
+   japanese_ingredient_df = create_ingredient_df(japanese_df)
+   japanese_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![Giapponese](../images/japanese.png)
+
+1. Ora per gli ingredienti cinesi:
+
+   ```python
+   chinese_ingredient_df = create_ingredient_df(chinese_df)
+   chinese_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![cinese](../images/chinese.png)
+
+1. Tracciare gli ingredienti indiani:
+
+   ```python
+   indian_ingredient_df = create_ingredient_df(indian_df)
+   indian_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![indiano](../images/indian.png)
+
+1. Infine, tracciare gli ingredienti coreani:
+
+   ```python
+   korean_ingredient_df = create_ingredient_df(korean_df)
+   korean_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![Coreano](../images/korean.png)
+
+1. Ora, eliminare gli ingredienti più comuni che creano confusione tra le diverse cucine, chiamando `drop()`:
+
+   Tutti amano il riso, l'aglio e lo zenzero!
+
+   ```python
+   feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+   labels_df = df.cuisine #.unique()
+   feature_df.head()
+   ```
+
+## Bilanciare l'insieme di dati
+
+Ora che i dati sono puliti, si usa [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Tecnica di sovracampionamento della minoranza sintetica" - per bilanciarlo.
+
+1. Chiamare `fit_resample()`, questa strategia genera nuovi campioni per interpolazione.
+
+   ```python
+   oversample = SMOTE()
+   transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+   ```
+
+   Bilanciando i dati, si otterranno risultati migliori quando si classificano. Si pensi a una classificazione binaria. Se la maggior parte dei dati è una classe, un modello ML prevederà quella classe più frequentemente, solo perché ci sono più dati per essa. Il bilanciamento dei dati prende tutti i dati distorti e aiuta a rimuovere questo squilibrio.
+
+1. Ora si può controllare il numero di etichette per ingrediente:
+
+   ```python
+   print(f'new label count: {transformed_label_df.value_counts()}')
+   print(f'old label count: {df.cuisine.value_counts()}')
+   ```
+
+   il risultato si presenta così:
+
+   ```output
+   new label count: korean      799
+   chinese     799
+   indian      799
+   japanese    799
+   thai        799
+   Name: cuisine, dtype: int64
+   old label count: korean      799
+   indian      598
+   chinese     442
+   japanese    320
+   thai        289
+   Name: cuisine, dtype: int64
+   ```
+
+   I dati sono belli e puliti, equilibrati e molto deliziosi!
+
+1. L'ultimo passaggio consiste nel salvare i dati bilanciati, incluse etichette e caratteristiche, in un nuovo dataframe che può essere esportato in un file:
+
+   ```python
+   transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+   ```
+
+1. Si può dare un'altra occhiata ai dati usando `transform_df.head()` e `transform_df.info()`. Salvare una copia di questi dati per utilizzarli nelle lezioni future:
+
+   ```python
+   transformed_df.head()
+   transformed_df.info()
+   transformed_df.to_csv("../data/cleaned_cuisine.csv")
+   ```
+
+   Questo nuovo CSV può ora essere trovato nella cartella data in radice.
+
+---
+
+## 🚀 Sfida
+
+Questo programma di studi contiene diversi insiemi di dati interessanti. Esaminare le cartelle `data` e vedere se contiene insiemi di dati che sarebbero appropriati per la classificazione binaria o multiclasse. Quali domande si farebbero a questo insieme di dati?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Esplorare l'API di SMOTE. Per quali casi d'uso è meglio usarla? Quali problemi risolve?
+
+## Compito
+
+[Esplorare i metodi di classificazione](assignment.it.md)
diff --git a/4-Classification/1-Introduction/translations/README.ko.md b/4-Classification/1-Introduction/translations/README.ko.md
new file mode 100644
index 000000000..c1acee070
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+# classification 소개하기
+
+4개 강의에서, classic 머신러닝의 기본 초점인 - _classification_ 을 찾아 볼 예정입니다. 아시아와 인도의 모든 훌륭한 요리 데이터셋과 함께 다양한 classification 알고리즘을 사용할 예정입니다. 배고파보세요!
+
+![just a pinch!](../images/pinch.png)
+
+> Celebrate pan-Asian cuisines in these lessons! Image by [Jen Looper](https://twitter.com/jenlooper)
+
+Classification은 regression 기술과 공통점이 많은 [supervised learning](https://wikipedia.org/wiki/Supervised_learning)의 폼입니다. 만약 머신러닝이 데이터셋으로 사물의 값이나 이름을 예측한다면, 일반적으로 classification는 2가지 그룹으로 나누어집니다: _binary classification_ 과 _multiclass classification_.
+
+[![Introduction to classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to classification")
+
+> 🎥 이미지를 누르면 영상 시청: MIT's John Guttag introduces classification
+
+생각합니다:
+
+- **Linear regression** 변수 사이 관계를 예측하고 새로운 데이터 포인트로 라인과 엮인 위치에 대한 정확한 예측을 하도록 도움을 줍니다. 예시로, _what price a pumpkin would be in September vs. December_ 를 예측할 수 있습니다.
+- **Logistic regression** "binary categories"를 찾을 때 도와줄 수 있습니다: at this price point, _is this pumpkin orange or not-orange_?
+
+Classification은 다양한 알고리즘으로 데이터 포인트의 라벨 혹은 클래스를 결정할 다른 방식을 고릅니다. 요리 데이터로, 재료 그룹을 찾아서, 전통 요리로 결정할 수 있는지 알아보려 합니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+
+### 소개
+
+Classification은 머신러닝 연구원과 데이터 사이언티스트의 기본 활동의 하나입니다. 바이너리 값("is this email spam or not?")의 기본 classification부터, 컴퓨터 비전으로 복잡한 이미지 classification과 segmentation까지, 데이터를 클래스로 정렬하고 물어보는 것은 항상 유용합니다.
+
+보다 과학적인 방식으로 프로세스를 설명해보자면, classification 방식은 입력한 변수 사이 관계를 출력 변수에 맵핑할 수 있는 예측 모델을 만듭니다.
+
+![binary vs. multiclass classification](../images/binary-multiclass.png)
+
+> Binary vs. multiclass problems for classification algorithms to handle. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+데이터를 정리, 시각화, 그리고 ML 작업을 준비하는 프로세스를 시작하기 전, 데이터를 분류할 때 활용할 수 있는 머신러닝의 다양한 방식에 대하여 알아봅니다.
+
+[statistics](https://wikipedia.org/wiki/Statistical_classification)에서 분리된, classic 머신러닝을 사용하는 classification은, `smoker`, `weight`, 그리고 `age`처럼 _likelihood of developing X disease_ 에 대하여 결정합니다. 전에 수행한 regression 연습과 비슷한 supervised learning 기술로서, 데이터에 라벨링한 ML 알고리즘은 라벨로 데이터셋의 클래스(또는 'features')를 분류하고 예측해서 그룹 또는 결과에 할당합니다.
+
+✅ 잠시 요리 데이터셋을 상상해봅니다. multiclass 모델은 어떻게 답변할까요? 바이너리 모델은 어떻게 답변할까요? 주어진 요리에 fenugreek를 사용할 지 어떻게 확인하나요? 만약 star anise, artichokes, cauliflower, 그리고 horseradish로 가득한 식품 가방을 선물해서, 전형적 인도 요리를 만들 수 있는지, 보고 싶다면 어떻게 하나요?
+
+
+[![Crazy mystery baskets](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU "Crazy mystery baskets")
+
+> 🎥 영상을 보려면 이미지 클릭합니다. The whole premise of the show 'Chopped' is the 'mystery basket' where chefs have to make some dish out of a random choice of ingredients. Surely a ML model would have helped!
+
+## 안녕 'classifier'
+
+요리 데이터셋에 물어보고 싶은 질문은, 여러 잠재적 국민 요리를 만들 수 있기 때문에 실제로 **multiclass question**입니다. 재료가 배치되었을 때, 많은 클래스 중에 어떤 데이터가 맞을까요?
+
+Scikit-learn은 해결하고 싶은 문제의 타입에 따라서, 데이터를 분류하며 사용할 여러가지 알고리즘을 제공합니다. 다음 2가지 강의에서, 몇 알고리즘에 대하여 더 배울 예정입니다.
+
+## 연습 - 데이터 정리하며 균형잡기
+
+프로젝트를 시작하기 전, 첫번째로 해야 할 일은, 더 좋은 결과를 얻기 위해서 데이터를 정리하고 **balance** 하는 일입니다. 이 폴더의 최상단에 있는 빈 _notebook.ipynb_ 파일에서 시작합니다.
+
+먼저 설치할 것은 [imblearn](https://imbalanced-learn.org/stable/)입니다. 데이터의 균형을 잘 잡아줄 Scikit-learn 패키지입니다 (몇 분동안 배우게 됩니다). 
+
+1. 이렇게, `imblearn` 설치하고, `pip install`을 실행합니다:
+
+    ```python
+    pip install imblearn
+    ```
+
+1. 데이터를 가져오고 시각화할 때 필요한 패키지를 Import 합니다, `imblearn`의 `SMOTE`도 import 합니다.
+
+    ```python
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import matplotlib as mpl
+    import numpy as np
+    from imblearn.over_sampling import SMOTE
+    ```
+
+    지금부터 데이터를 가져와서 읽게 세팅되었습니다.
+
+1. 다음 작업으로 데이터를 가져옵니다:
+
+    ```python
+    df  = pd.read_csv('../data/cuisines.csv')
+    ```
+
+   `read_csv()`를 사용하면 _cusines.csv_ csv 파일의 컨텐츠를 읽고 `df` 변수에 놓습니다.
+
+1. 데이터의 모양을 확인합니다:
+
+    ```python
+    df.head()
+    ```
+
+   다음은 처음 5개 행입니다:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. `info()`를 불러서 데이터의 정보를 봅니다:
+
+    ```python
+    df.info()
+    ```
+
+    출력됩니다:
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 2448 entries, 0 to 2447
+    Columns: 385 entries, Unnamed: 0 to zucchini
+    dtypes: int64(384), object(1)
+    memory usage: 7.2+ MB
+    ```
+
+## 연습 - 요리에 대하여 배우기
+
+지금부터 작업이 더 흥미로워집니다. 요리별, 데이터의 분포를 알아봅니다
+
+1. `barh()`를 불러서 바 형태로 데이터를 Plot합니다:
+
+    ```python
+    df.cuisine.value_counts().plot.barh()
+    ```
+
+    ![cuisine data distribution](../images/cuisine-dist.png)
+
+    한정된 요리 갯수가 있지만, 데이터의 분포는 고르지 않습니다. 고칠 수 있습니다! 이전에, 조금 찾아봅니다.
+
+1. 요리별로 사용할 수 있는 데이터 크기를 보기 위해서 출력합니다:
+
+    ```python
+    thai_df = df[(df.cuisine == "thai")]
+    japanese_df = df[(df.cuisine == "japanese")]
+    chinese_df = df[(df.cuisine == "chinese")]
+    indian_df = df[(df.cuisine == "indian")]
+    korean_df = df[(df.cuisine == "korean")]
+    
+    print(f'thai df: {thai_df.shape}')
+    print(f'japanese df: {japanese_df.shape}')
+    print(f'chinese df: {chinese_df.shape}')
+    print(f'indian df: {indian_df.shape}')
+    print(f'korean df: {korean_df.shape}')
+    ```
+
+    이렇게 출력됩니다:
+
+    ```output
+    thai df: (289, 385)
+    japanese df: (320, 385)
+    chinese df: (442, 385)
+    indian df: (598, 385)
+    korean df: (799, 385)
+    ```
+
+## 성분 발견하기
+
+지금부터 데이터를 깊게 파서 요리별 일반적인 재료가 무엇인지 배울 수 있습니다. 요리 사이의 혼동을 일으킬 중복 데이터를 정리할 필요가 있으므로, 문제에 대하여 배우겠습니다.
+
+1. Python에서 성분 데이터프레임을 생성하기 위해서 `create_ingredient()` 함수를 만듭니다. 함수는 도움이 안되는 열을 드랍하고 카운트로 재료를 정렬하게 됩니다:
+
+    ```python
+    def create_ingredient_df(df):
+        ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+        ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False,
+        inplace=False)
+        return ingredient_df
+    ```
+
+   지금부터 함수를 사용해서 요리별 가장 인기있는 10개 재료의 아이디어를 얻을 수 있습니다.
+
+1. `create_ingredient()` 부르고 `barh()`을 부르면서 plot합니다:
+
+    ```python
+    thai_ingredient_df = create_ingredient_df(thai_df)
+    thai_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![thai](../images/thai.png)
+
+1. 일본 데이터에서 똑같이 합니다:
+
+    ```python
+    japanese_ingredient_df = create_ingredient_df(japanese_df)
+    japanese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![japanese](../images/japanese.png)
+
+1. 지금 중국 재료에서도 합니다:
+
+    ```python
+    chinese_ingredient_df = create_ingredient_df(chinese_df)
+    chinese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![chinese](../images/chinese.png)
+
+1. 인도 재료에서도 Plot 합니다:
+
+    ```python
+    indian_ingredient_df = create_ingredient_df(indian_df)
+    indian_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![indian](../images/indian.png)
+
+1. 마지막으로, 한국 재료에도 plot 합니다:
+
+    ```python
+    korean_ingredient_df = create_ingredient_df(korean_df)
+    korean_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![korean](../images/korean.png)
+
+1. 지금부터, `drop()`을 불러서, 전통 요리 사이에 혼란을 주는 가장 공통적인 재료를 드랍합니다:
+
+   모두 쌀, 마늘과 생강을 좋아합니다!
+
+    ```python
+    feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+    labels_df = df.cuisine #.unique()
+    feature_df.head()
+    ```
+
+## 데이터셋 균형 맞추기
+
+지금까지 [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html)를 사용해서, 데이터를 정리했습니다. - "Synthetic Minority Over-sampling Technique" - to balance it.
+
+1. `fit_resample()`을 부르는, 전략은 interpolation으로 새로운 샘플을 생성합니다. 
+
+    ```python
+    oversample = SMOTE()
+    transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+    ```
+
+    데이터를 균형맞추면, 분류할 때 더 좋은 결과를 냅니다. binary classification에 대하여 생각해봅니다. 만약 대부분 데이터가 한 클래스라면, ML 모델은 단지 데이터가 많다는 이유로, 해당 클래스를 더 자주 예측합니다. 데이터 균형을 맞추면 왜곡된 데이터로 불균형을 제거하는 과정을 도와줍니다.
+
+1. 지금부터 성분별 라벨의 수를 확인할 수 있습니다:
+
+    ```python
+    print(f'new label count: {transformed_label_df.value_counts()}')
+    print(f'old label count: {df.cuisine.value_counts()}')
+    ```
+
+    이렇게 출력됩니다:
+
+    ```output
+    new label count: korean      799
+    chinese     799
+    indian      799
+    japanese    799
+    thai        799
+    Name: cuisine, dtype: int64
+    old label count: korean      799
+    indian      598
+    chinese     442
+    japanese    320
+    thai        289
+    Name: cuisine, dtype: int64
+    ```
+
+    이 데이터는 훌륭하고 깔끔하고, 균형 잡히고, 그리고 매우 맛있습니다!
+
+1. 마지막 단계는 라벨과 features를 포함한, 밸런스 맞춘 데이터를 파일로 뽑을 수 있는 새로운 데이터프레임으로 저장합니다:
+
+    ```python
+    transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+    ```
+
+1. `transformed_df.head()` 와 `transformed_df.info()`로 데이터를 다시 볼 수 있습니다. 다음 강의에서 쓸 수 있도록 데이터를 복사해서 저장합니다:
+
+    ```python
+    transformed_df.head()
+    transformed_df.info()
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
+    ```
+
+    새로운 CSV는 최상단 데이터 폴더에서 찾을 수 있습니다.
+
+---
+
+## 🚀 도전
+
+해당 커리큘럼은 여러 흥미로운 데이터셋을 포함하고 있습니다. `data` 폴더를 파보면서 binary 또는 multi-class classification에 적당한 데이터셋이 포함되어 있나요? 데이터셋에 어떻게 물어보나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+
+## 검토 & 자기주도 학습
+
+SMOTE API를 찾아봅니다. 어떤 사용 케이스에 잘 사용하나요? 어떤 문제를 해결하나요?
+
+## 과제 
+
+[Explore classification methods](../assignment.md)
diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
new file mode 100644
index 000000000..e1b32ec90
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -0,0 +1,298 @@
+# Sınıflandırmaya giriş
+
+Bu dört derste klasik makine öğreniminin temel bir odağı olan _sınıflandırma_ konusunu keşfedeceksiniz. Asya ve Hindistan'ın nefis mutfağının tamamı üzerine hazırlanmış bir veri setiyle çeşitli sınıflandırma algoritmalarını kullanmanın üzerinden geçeceğiz. Umarız açsınızdır!
+
+![sadece bir tutam!](../images/pinch.png)
+
+> Bu derslerede Pan-Asya mutfağını kutlayın! Fotoğraf [Jen Looper](https://twitter.com/jenlooper) tarafından çekilmiştir.
+
+Sınıflandırma, regresyon yöntemleriyle birçok ortak özelliği olan bir [gözetimli öğrenme](https://wikipedia.org/wiki/Supervised_learning) biçimidir. Eğer makine öğrenimi tamamen veri setleri kullanarak değerleri veya nesnelere verilecek isimleri öngörmekse, sınıflandırma genellikle iki gruba ayrılır: _ikili sınıflandırma_ ve _çok sınıflı sınıflandırma_.
+
+[![Sınıflandırmaya giriş](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to classification")
+
+> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın: MIT's John Guttag introduces classification (MIT'den John Guttag sınıflandırmayı tanıtıyor)
+
+Hatırlayın:
+
+- **Doğrusal regresyon** değişkenler arasındaki ilişkileri öngörmenize ve o doğruya ilişkili olarak yeni bir veri noktasının nereye düşeceğine dair doğru öngörülerde bulunmanıza yardımcı oluyordu. Yani, _bir balkabağının fiyatının aralık ayına göre eylül ayında ne kadar olabileceğini_ öngörebilirsiniz örneğin.
+- **Lojistik regresyon** "ikili kategoriler"i keşfetmenizi sağlamıştı: bu fiyat noktasında, _bu balkabağı turuncu mudur, turuncu-değil midir?_
+
+Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemek için farklı yollar belirlemek üzere çeşitli algoritmalar kullanır. Bir grup malzemeyi gözlemleyerek kökeninin hangi mutfak olduğunu belirleyip belirleyemeyeceğimizi görmek için bu mutfak verisiyle çalışalım.
+
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=tr)
+
+### Giriş
+
+Sınıflandırma, makine öğrenimi araştırmacısının ve veri bilimcisinin temel işlerinden biridir. İkili bir değerin temel sınıflandırmasından ("Bu e-posta gereksiz (spam) midir yoksa değil midir?") bilgisayarla görüden yararlanarak karmaşık görüntü sınıflandırma ve bölütlemeye kadar, veriyi sınıf sınıf sıralayabilmek ve soru sorabilmek daima faydalıdır.
+
+Süreci daha bilimsel bir yolla ifade etmek gerekirse, sınıflandırma yönteminiz, girdi bilinmeyenlerinin arasındaki ilişkiyi çıktı bilinmeyenlerine eşlemenizi sağlayan öngörücü bir model oluşturur.
+
+![ikili ve çok sınıflı sınıflandırma karşılaştırması](../images/binary-multiclass.png)
+
+> Sınıflandırma algoritmalarının başa çıkması gereken ikili ve çok sınıflı problemler. Bilgilendirme grafiği [Jen Looper](https://twitter.com/jenlooper) tarafından hazırlanmıştır.
+
+Verimizi temizleme, görselleştirme ve makine öğrenimi görevleri için hazırlama süreçlerine başlamadan önce, veriyi sınıflandırmak için makine öğreniminin leveraj edilebileceği çeşitli yolları biraz öğrenelim.
+
+[İstatistikten](https://wikipedia.org/wiki/Statistical_classification) türetilmiş olarak, klasik makine öğrenimi kullanarak sınıflandırma, _X hastalığının gelişmesi ihtimalini_ belirlemek için `smoker`, `weight`, ve `age` gibi öznitelikler kullanır. Daha önce yaptığınız regresyon alıştırmalarına benzeyen bir gözetimli öğrenme yöntemi olarak, veriniz etiketlenir ve makine öğrenimi algoritmaları o etiketleri, sınıflandırmak ve veri setinin sınıflarını (veya 'özniteliklerini') öngörmek ve onları bir gruba veya bir sonuca atamak için kullanır.
+
+:white_check_mark: Mutfaklarla ilgili bir veri setini biraz düşünün. Çok sınıflı bir model neyi cevaplayabilir? İkili bir model neyi cevaplayabilir? Farz edelim ki verilen bir mutfağın çemen kullanmasının muhtemel olup olmadığını belirlemek istiyorsunuz. Farzedelim ki yıldız anason, enginar, karnabahar ve bayır turpu ile dolu bir alışveriş poşetinden tipik bir Hint yemeği yapıp yapamayacağınızı görmek istiyorsunuz.
+
+[![Çılgın gizem sepetleri](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU "Crazy mystery baskets")
+
+> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın. Aşçıların rastgele malzeme seçeneklerinden yemek yaptığı 'Chopped' programının tüm olayı 'gizem sepetleri'dir. Kuşkusuz, bir makine öğrenimi modeli onlara yardımcı olurdu!
+
+## Merhaba 'sınıflandırıcı'
+
+Bu mutfak veri setiyle ilgili sormak istediğimiz soru aslında bir **çok sınıflı soru**dur çünkü elimizde farklı potansiyel ulusal mutfaklar var. Verilen bir grup malzeme için, veri bu sınıflardan hangisine uyacak?
+
+Scikit-learn, veriyi sınıflandırmak için kullanmak üzere, çözmek istediğiniz problem çeşidine bağlı olarak, çeşitli farklı algoritmalar sunar. Önümüzdeki iki derste, bu algoritmalardan birkaçını öğreneceksiniz.
+
+## Alıştırma - verinizi temizleyip dengeleyin
+
+Bu projeye başlamadan önce elinizdeki ilk görev, daha iyi sonuçlar almak için, verinizi temizlemek ve **dengelemek**. Üst klasördeki boş _notebook.ipynb_ dosyasıyla başlayın.
+
+Kurmanız gereken ilk şey [imblearn](https://imbalanced-learn.org/stable/). Bu, veriyi daha iyi dengelemenizi sağlayacak bir Scikit-learn paketidir. (Bu görev hakkında birazdan daha fazla bilgi göreceksiniz.)
+
+1. `imblearn` kurun, `pip install` çalıştırın, şu şekilde:
+
+    ```python
+    pip install imblearn
+    ```
+
+1. Verinizi almak ve görselleştirmek için ihtiyaç duyacağınız paketleri alın (import edin), ayrıca `imblearn` paketinden `SMOTE` alın.
+
+    ```python
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import matplotlib as mpl
+    import numpy as np
+    from imblearn.over_sampling import SMOTE
+    ```
+
+    Şimdi okumak için hazırsınız, sonra veriyi alın.
+
+1. Sonraki görev veriyi almak olacak:
+
+    ```python
+    df  = pd.read_csv('../data/cuisines.csv')
+    ```
+
+   `read_csv()` kullanmak _cusines.csv_ csv dosyasının içeriğini okuyacak ve `df` değişkenine yerleştirecek.
+
+1. Verinin şeklini kontrol edin:
+
+    ```python
+    df.head()
+    ```
+
+   İlk beş satır şöyle görünüyor:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. `info()` fonksiyonunu çağırarak bu veri hakkında bilgi edinin:
+
+    ```python
+    df.info()
+    ```
+
+    Çıktınız şuna benzer:
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 2448 entries, 0 to 2447
+    Columns: 385 entries, Unnamed: 0 to zucchini
+    dtypes: int64(384), object(1)
+    memory usage: 7.2+ MB
+    ```
+
+## Alıştırma - mutfaklar hakkında bilgi edinmek
+
+Şimdi, işimiz daha da ilginçleşmeye başlıyor. Mutfak mutfak verinin dağılımını keşfedelim
+
+1. `barh()` fonksiyonunu çağırarak veriyi sütunlarla çizdirin:
+
+    ```python
+    df.cuisine.value_counts().plot.barh()
+    ```
+
+    ![mutfak veri dağılımı](../images/cuisine-dist.png)
+
+    Sonlu sayıda mutfak var, ancak verinin dağılımı düzensiz. Bunu düzeltebilirsiniz! Bunu yapmadan önce, biraz daha keşfedelim. 
+
+1. Her mutfak için ne kadar verinin mevcut olduğunu bulun ve yazdırın:
+
+    ```python
+    thai_df = df[(df.cuisine == "thai")]
+    japanese_df = df[(df.cuisine == "japanese")]
+    chinese_df = df[(df.cuisine == "chinese")]
+    indian_df = df[(df.cuisine == "indian")]
+    korean_df = df[(df.cuisine == "korean")]
+    
+    print(f'thai df: {thai_df.shape}')
+    print(f'japanese df: {japanese_df.shape}')
+    print(f'chinese df: {chinese_df.shape}')
+    print(f'indian df: {indian_df.shape}')
+    print(f'korean df: {korean_df.shape}')
+    ```
+
+    çıktı şöyle görünür:
+
+    ```output
+    thai df: (289, 385)
+    japanese df: (320, 385)
+    chinese df: (442, 385)
+    indian df: (598, 385)
+    korean df: (799, 385)
+    ```
+
+## Malzemeleri keşfetme
+
+Şimdi veriyi daha derinlemesine inceleyebilirsiniz ve her mutfak için tipik malzemelerin neler olduğunu öğrenebilirsiniz. Mutfaklar arasında karışıklık yaratan tekrar eden veriyi temizlemelisiniz, dolayısıyla şimdi bu problemle ilgili bilgi edinelim.
+
+1. Python'da, malzeme veri iskeleti yaratmak için `create_ingredient_df()` diye bir fonksiyon oluşturun. Bu fonksiyon, yardımcı olmayan bir sütunu temizleyerek ve sayılarına göre malzemeleri sıralayarak başlar:
+
+    ```python
+    def create_ingredient_df(df):
+        ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+        ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+        inplace=False)
+        return ingredient_df
+    ```
+
+   Şimdi bu fonksiyonu, her mutfağın en yaygın ilk on malzemesi hakkında hakkında fikir edinmek için kullanabilirsiniz.
+
+1. `create_ingredient_df()` fonksiyonunu çağırın ve `barh()` fonksiyonunu çağırarak çizdirin:
+
+    ```python
+    thai_ingredient_df = create_ingredient_df(thai_df)
+    thai_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Tayland](../images/thai.png)
+
+1. Japon verisi için de aynısını yapın:
+
+    ```python
+    japanese_ingredient_df = create_ingredient_df(japanese_df)
+    japanese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Japon](../images/japanese.png)
+
+1. Şimdi Çin malzemeleri için yapın:
+
+    ```python
+    chinese_ingredient_df = create_ingredient_df(chinese_df)
+    chinese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Çin](../images/chinese.png)
+
+1. Hint malzemelerini çizdirin:
+
+    ```python
+    indian_ingredient_df = create_ingredient_df(indian_df)
+    indian_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Hint](../images/indian.png)
+
+1. Son olarak, Kore malzemelerini çizdirin:
+
+    ```python
+    korean_ingredient_df = create_ingredient_df(korean_df)
+    korean_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Kore](../images/korean.png)
+
+1. Şimdi, `drop()` fonksiyonunu çağırarak, farklı mutfaklar arasında karışıklığa sebep olan en çok ortaklık taşıyan malzemeleri temizleyelim:
+
+   Herkes pirinci, sarımsağı ve zencefili seviyor!
+
+    ```python
+    feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+    labels_df = df.cuisine #.unique()
+    feature_df.head()
+    ```
+
+## Veri setini dengeleyin
+
+Veriyi temizlediniz, şimdi [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Synthetic Minority Over-sampling Technique" ("Sentetik Azınlık Aşırı-Örnekleme/Örneklem-Artırma Tekniği") kullanarak dengeleyelim.
+
+1. `fit_resample()` fonksiyonunu çağırın, bu strateji ara değerlemeyle yeni örnekler üretir.
+
+    ```python
+    oversample = SMOTE()
+    transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+    ```
+
+    Verinizi dengeleyerek, sınıflandırırken daha iyi sonuçlar alabileceksiniz. Bir ikili sınıflandırma düşünün. Eğer verimizin çoğu tek bir sınıfsa, bir makine öğrenimi modeli, sırf onun için daha fazla veri olduğundan o sınıfı daha sık tahmin edecektir. Veriyi dengelemek herhangi eğri veriyi alır ve bu dengesizliğin ortadan kaldırılmasına yardımcı olur.
+
+1. Şimdi, her bir malzeme için etiket sayısını kontrol edebilirsiniz:
+
+    ```python
+    print(f'new label count: {transformed_label_df.value_counts()}')
+    print(f'old label count: {df.cuisine.value_counts()}')
+    ```
+
+    Çıktınız şöyle görünür:
+
+    ```output
+    new label count: korean      799
+    chinese     799
+    indian      799
+    japanese    799
+    thai        799
+    Name: cuisine, dtype: int64
+    old label count: korean      799
+    indian      598
+    chinese     442
+    japanese    320
+    thai        289
+    Name: cuisine, dtype: int64
+    ```
+
+    Veri şimdi tertemiz, dengeli ve çok lezzetli!
+
+1. Son adım, dengelenmiş verinizi, etiket ve özniteliklerle beraber, yeni bir dosyaya gönderilebilecek yeni bir veri iskeletine kaydetmek:
+
+    ```python
+    transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+    ```
+
+1. `transformed_df.head()` ve `transformed_df.info()` fonksiyonlarını kullanarak verinize bir kez daha göz atabilirsiniz. Gelecek derslerde kullanabilmek için bu verinin bir kopyasını kaydedin:
+
+    ```python
+    transformed_df.head()
+    transformed_df.info()
+    transformed_df.to_csv("../../data/cleaned_cuisines.csv")
+
+    ```
+
+    Bu yeni CSV şimdi kök data (veri) klasöründe görülebilir.
+
+---
+
+## :rocket: Meydan okuma
+
+Bu öğretim programı farklı ilgi çekici veri setleri içermekte. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri bulunduran var mı, bakın. Bu veri seti için hangi soruları sorabilirdiniz?
+
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=tr)
+
+## Gözden Geçirme & Kendi Kendine Çalışma
+
+SMOTE'nin API'ını keşfedin. En iyi hangi durumlar için kullanılıyor? Hangi problemleri çözüyor?
+
+## Ödev
+
+[Sınıflandırma yöntemlerini keşfedin](assignment.tr.md)
diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
new file mode 100644
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@@ -0,0 +1,291 @@
+# 对分类方法的介绍
+
+在这四节课程中，你将会学习机器学习中一个基本的重点 - _分类_。 我们会在关于亚洲和印度的神奇的美食的数据集上尝试使用多种分类算法。希望你有点饿了。
+
+![一个桃子！](../images/pinch.png)
+
+> 在学习的课程中赞叹泛亚地区的美食吧！ 图片由 [Jen Looper](https://twitter.com/jenlooper) 提供
+
+分类算法是[监督学习](https://wikipedia.org/wiki/Supervised_learning)的一种。它与回归算法在很多方面都有相同之处。如果机器学习所有的目标都是使用数据集来预测数值或物品的名字，那么分类算法通常可以分为两类 _二元分类_ 和 _多元分类_。
+
+[![对分类算法的介绍](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "对分类算法的介绍")
+
+> 🎥 点击上方的图片可以跳转到一个视频-MIT 的 John 对分类算法的介绍
+
+请记住：
+
+- **线性回归**  帮助你预测变量之间的关系并对一个新的数据点会落在哪条线上做出精确的预测。因此，你可以预测 _南瓜在九月的价格和十月的价格_。
+- **逻辑回归**  帮助你发现“二元范畴”：即在当前这个价格， _这个南瓜是不是橙色_？
+
+分类方法采用多种算法来确定其他可以用来确定一个数据点的标签或类别的方法。让我们来研究一下这个数据集，看看我们能否通过观察菜肴的原料来确定它的源头。
+
+## [课程前的小问题](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+
+分类是机器学习研究者和数据科学家使用的一种基本方法。从基本的二元分类（这是不是一份垃圾邮件？）到复杂的图片分类和使用计算机视觉的分割技术，它都是将数据分类并提出相关问题的有效工具。
+
+![二元分类 vs 多元分类](../images/binary-multiclass.png)
+
+> 需要分类算法解决的二元分类和多元分类问题的对比. 信息图由 [Jen Looper](https://twitter.com/jenlooper) 提供
+
+在开始清洗数据、数据可视化和调整数据以适应机器学习的任务前，让我们来了解一下多种可用来数据分类的机器学习方法。
+
+派生自[统计数学](https://wikipedia.org/wiki/Statistical_classification)，分类算法使用经典的机器学习的一些特征，比如通过'吸烟者'、'体重'和'年龄'来推断 _罹患某种疾病的可能性_。作为一个与你刚刚实践过的回归算法很相似的监督学习算法，你的数据是被标记过的并且算法通过采集这些标签来进行分类和预测并进行输出。
+
+✅ 花一点时间来想象一下一个关于菜肴的数据集。一个多元分类的模型应该能回答什么问题？一个二元分类的模型又应该能回答什么？如果你想确定一个给定的菜肴是否会用到葫芦巴（一种植物，种子用来调味）该怎么做？如果你想知道给你一个装满了八角茴香、花椰菜和辣根的购物袋你能否做出一道代表性的印度菜又该怎么做？
+
+[![Crazy mystery baskets](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU "疯狂的神秘篮子")
+
+> 🎥 点击图像观看视频。整个'Chopped'节目的前提都是建立在神秘的篮子上，在这个节目中厨师必须利用随机给定的食材做菜。可见一个机器学习模型能起到不小的作用
+
+## 初见-分类器
+
+我们关于这个菜肴数据集想要提出的问题其实是一个 **多元问题**，因为我们有很多潜在的具有代表性的菜肴。给定一系列食材数据，数据能够符合这些类别中的哪一类？
+
+Scikit-learn 项目提供多种对数据进行分类的算法，你需要根据问题的具体类型来进行选择。在下两节课程中你会学到这些算法中的几个。
+
+## 练习 - 清洗并平衡你的数据
+
+在你开始进行这个项目前的第一个上手的任务就是清洗和 **平衡**你的数据来得到更好的结果。从当前目录的根目录中的 _nodebook.ipynb_ 开始。
+
+第一个需要安装的东西是 [imblearn](https://imbalanced-learn.org/stable/) 这是一个 Scikit-learn 项目中的一个包，它可以让你更好的平衡数据 (关于这个任务你很快你就会学到更多)。
+
+1. 安装 `imblearn`, 运行命令 `pip install`:
+
+    ```python
+    pip install imblearn
+    ```
+
+1. 为了导入和可视化数据你需要导入下面的这些包, 你还需要从 `imblearn` 导入 `SMOTE`
+
+    ```python
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import matplotlib as mpl
+    import numpy as np
+    from imblearn.over_sampling import SMOTE
+    ```
+
+    现在你已经准备好导入数据了。
+
+1. 下一项任务是导入数据:
+
+    ```python
+    df  = pd.read_csv('../data/cuisines.csv')
+    ```
+
+   使用函数 `read_csv()` 会读取 csv 文件的内容 _cusines.csv_ 并将内容放置在 变量 `df` 中。
+   
+1. 检查数据的形状是否正确:
+
+    ```python
+    df.head()
+    ```
+
+   前五行输出应该是这样的:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. 调用函数 `info()` 可以获得有关这个数据集的信息:
+
+    ```python
+    df.info()
+    ```
+
+    你的输出应该像这样：
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 2448 entries, 0 to 2447
+    Columns: 385 entries, Unnamed: 0 to zucchini
+    dtypes: int64(384), object(1)
+    memory usage: 7.2+ MB
+    ```
+
+## 练习 - 了解这些菜肴
+
+现在任务变得更有趣了，让我们来探索如何将数据分配给各个菜肴
+
+1. 调用函数 `barh()` 可以绘制出数据的条形图:
+
+    ```python
+    df.cuisine.value_counts().plot.barh()
+    ```
+
+    ![菜肴数据分配](../images/cuisine-dist.png)
+
+    这里有有限的一些菜肴，但是数据的分配是不平均的。但是你可以修正这一现象！在这样做之前再稍微探索一下。
+
+1. 找出对于每个菜肴有多少数据是有效的并将其打印出来:
+
+    ```python
+    thai_df = df[(df.cuisine == "thai")]
+    japanese_df = df[(df.cuisine == "japanese")]
+    chinese_df = df[(df.cuisine == "chinese")]
+    indian_df = df[(df.cuisine == "indian")]
+    korean_df = df[(df.cuisine == "korean")]
+    
+    print(f'thai df: {thai_df.shape}')
+    print(f'japanese df: {japanese_df.shape}')
+    print(f'chinese df: {chinese_df.shape}')
+    print(f'indian df: {indian_df.shape}')
+    print(f'korean df: {korean_df.shape}')
+    ```
+
+    输出应该是这样的 :
+
+    ```output
+    thai df: (289, 385)
+    japanese df: (320, 385)
+    chinese df: (442, 385)
+    indian df: (598, 385)
+    korean df: (799, 385)
+    ```
+## 探索有关食材的内容
+
+现在你可以在数据中探索的更深一点并了解每道菜肴的代表性食材。你需要将反复出现的、容易造成混淆的数据清理出去，那么让我们来学习解决这个问题。
+
+1. 在 Python 中创建一个函数 `create_ingredient_df()` 来创建一个食材的数据帧。这个函数会去掉数据中无用的列并按食材的数量进行分类。
+
+    ```python
+    def create_ingredient_df(df):
+        ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+        ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False,
+        inplace=False)
+        return ingredient_df
+    ```
+现在你可以使用这个函数来得到理想的每道菜肴最重要的 10 种食材。
+
+1. 调用函数 `create_ingredient_df()` 然后通过函数 `barh()` 来绘制图像:
+
+    ```python
+    thai_ingredient_df = create_ingredient_df(thai_df)
+    thai_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![thai](../images/thai.png)
+
+1. 对日本的数据进行相同的操作:
+
+    ```python
+    japanese_ingredient_df = create_ingredient_df(japanese_df)
+    japanese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![日本](../images/japanese.png)
+
+1. 现在处理中国的数据:
+
+    ```python
+    chinese_ingredient_df = create_ingredient_df(chinese_df)
+    chinese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![中国](../images/chinese.png)
+
+1. 绘制印度食材的数据:
+
+    ```python
+    indian_ingredient_df = create_ingredient_df(indian_df)
+    indian_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![印度](../images/indian.png)
+
+1. 最后，绘制韩国的食材的数据:
+
+    ```python
+    korean_ingredient_df = create_ingredient_df(korean_df)
+    korean_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![韩国](../images/korean.png)
+
+1. 现在，去除在不同的菜肴间最普遍的容易造成混乱的食材，调用函数 `drop()`: 
+
+   大家都喜欢米饭、大蒜和生姜
+
+    ```python
+    feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+    labels_df = df.cuisine #.unique()
+    feature_df.head()
+    ```
+
+## 平衡数据集
+
+现在你已经清理过数据集了, 使用 [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Synthetic Minority Over-sampling Technique" - 来平衡数据集。
+
+1. 调用函数 `fit_resample()`, 此方法通过插入数据来生成新的样本
+
+    ```python
+    oversample = SMOTE()
+    transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+    ```
+
+    通过对数据集的平衡，当你对数据进行分类时能够得到更好的结果。现在考虑一个二元分类的问题，如果你的数据集中的大部分数据都属于其中一个类别，那么机器学习的模型就会因为在那个类别的数据更多而判断那个类别更为常见。平衡数据能够去除不公平的数据点。
+
+1. 现在你可以查看每个食材的标签数量:
+
+    ```python
+    print(f'new label count: {transformed_label_df.value_counts()}')
+    print(f'old label count: {df.cuisine.value_counts()}')
+    ```
+
+    输出应该是这样的 :
+
+    ```output
+    new label count: korean      799
+    chinese     799
+    indian      799
+    japanese    799
+    thai        799
+    Name: cuisine, dtype: int64
+    old label count: korean      799
+    indian      598
+    chinese     442
+    japanese    320
+    thai        289
+    Name: cuisine, dtype: int64
+    ```
+
+    现在这个数据集不仅干净、平衡而且还很“美味” ! 
+
+1. 最后一步是保存你处理过后的平衡的数据（包括标签和特征），将其保存为一个可以被输出到文件中的数据帧。
+
+    ```python
+    transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+    ```
+
+1. 你可以通过调用函数 `transformed_df.head()` 和 `transformed_df.info()` 再检查一下你的数据。 接下来要将数据保存以供在未来的课程中使用:
+
+    ```python
+    transformed_df.head()
+    transformed_df.info()
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
+    ```
+
+    这个全新的 CSV 文件可以在数据根目录中被找到。
+
+---
+
+## 🚀小练习
+
+本项目的全部课程含有很多有趣的数据集。 探索一下 `data` 文件夹，看看这里面有没有适合二元分类、多元分类算法的数据集，再想一下你对这些数据集有没有什么想问的问题。
+
+## [课后练习](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+
+## 回顾 & 自学
+
+探索一下 SMOTE 的 API 文档。思考一下它最适合于什么样的情况、它能够解决什么样的问题。
+
+## 课后作业 
+
+[探索一下分类方法](./assignment.zh-cn.md)
diff --git a/4-Classification/1-Introduction/translations/assignment.it.md b/4-Classification/1-Introduction/translations/assignment.it.md
new file mode 100644
index 000000000..128340179
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Esplorare i metodi di classificazione
+
+## Istruzioni
+
+Nella [documentazione](https://scikit-learn.org/stable/supervised_learning.html) di Scikit-learn si troverà un ampio elenco di modi per classificare i dati. Fare una piccola caccia al tesoro in questi documenti: l'obiettivo è cercare metodi di classificazione e abbinare un insieme di dati in questo programma di studi, una domanda che si può porre e una tecnica di classificazione. Creare un foglio di calcolo o una tabella in un file .doc e spiegare come funzionerebbe l'insieme di dati con l'algoritmo di classificazione.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+|          | viene presentato un documento che riporta una panoramica di 5 algoritmi insieme a una tecnica di classificazione. La panoramica è ben spiegata e dettagliata. | viene presentato un documento che riporta una panoramica di 3 algoritmi insieme a una tecnica di classificazione. La panoramica è ben spiegata e dettagliata. | viene presentato un documento che riporta una panoramica di meno di tre algoritmi insieme a una tecnica di classificazione e la panoramica non è né ben spiegata né dettagliata. |
diff --git a/4-Classification/1-Introduction/translations/assignment.tr.md b/4-Classification/1-Introduction/translations/assignment.tr.md
new file mode 100644
index 000000000..99dfe5c26
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Sınıflandırma yöntemlerini keşfedin
+
+## Yönergeler
+
+[Scikit-learn dokümentasyonunda](https://scikit-learn.org/stable/supervised_learning.html) veriyi sınıflandırma yöntemlerini içeren büyük bir liste göreceksiniz. Bu dokümanlar arasında ufak bir çöpçü avı yapın: Hedefiniz, sınıflandırma yöntemleri aramak ve bu eğitim programındaki bir veri seti, sorabileceğiniz bir soru ve bir sınıflandırma yöntemi eşleştirmek. Bir .doc dosyasında elektronik çizelge veya tablo hazırlayın ve veri setinin sınıflandırma algoritmasıyla nasıl çalışacağını açıklayın.
+
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                                                                                            | Yeterli                                                                                                                             | Geliştirme Gerekli                                                                                                                                            |
+| -------- | ----------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+|          | Bir sınıflandırma yönteminin yanısıra 5 algoritmayı inceleyen bir doküman sunulmuş. İnceleme iyi açıklanmış ve detaylı.             | Bir sınıflandırma yönteminin yanısıra 5 algoritmayı inceleyen bir doküman sunulmuş. İnceleme iyi açıklanmış ve detaylı.             | Bir sınıflandırma yönteminin yanısıra 3'ten az algoritmayı inceleyen bir doküman sunulmuş ve inceleme iyi açıklanmış veya detaylı değil.                      |
diff --git a/4-Classification/1-Introduction/translations/assignment.zh-cn.md b/4-Classification/1-Introduction/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..83d9eaba5
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 探索分类方法
+
+## 说明
+
+在 [Scikit-learn 文档](https://scikit-learn.org/stable/supervised_learning.html) 中你会找到一大串的数据分类的方法。在这些文档中做一个寻宝游戏：你的目标是寻找分类方法，并在本课程中匹配一个数据集，一个你能对它提出的问题，以及一种分类技术。在一个 .doc 文件中创建一个电子表格或表格，并解释该数据集如何与分类算法一起工作。
+
+## 评判标准
+
+| 标准 | 优秀 | 中规中矩 | 仍需努力 |
+| ---- | --- | -------- | ------- |
+|      | 提交了一份文件，概述了 5 种算法和一种分类技术。概述解释得清楚且详细。 |  提交了一份文件，概述了 3 种算法和一种分类技术。概述解释得清楚且详细。 | 提交了一份文件，概述了少于 3 种算法和一种分类技术，而且概述既没有很好的解释也没有详细说明。 |
diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index 15800922a..68877e6ac 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -4,7 +4,7 @@ In this lesson, you will use the dataset you saved from the last lesson full of
 
 You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
 # Preparation
 
 Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
@@ -15,21 +15,20 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     ```python
     import pandas as pd
-    cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+    cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv")
     cuisines_df.head()
     ```
 
     The data looks like this:
 
-    ```output
-    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
-    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
-    | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
-    ```
+|     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+| --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+| 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+  
 
 1. Now, import several more libraries:
 
@@ -68,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     Your features look like this:
 
-    | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
-    | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
-    |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+|      | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+|    0 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    1 |      1 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    2 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    3 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    4 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      1 |        0 | 0 |
 
 Now you are ready to train your model!
 
@@ -200,13 +199,13 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
 
     The result is printed - Indian cuisine is its best guess, with good probability:
 
-    |          |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-    |   indian | 0.715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |  chinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | japanese | 0.029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |   korean | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |     thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |          |        0 |
+    | -------: | -------: |
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 |
+    |     thai | 0.007634 |
 
     ✅ Can you explain why the model is pretty sure this is an Indian cuisine?
 
@@ -217,22 +216,23 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
     print(classification_report(y_test,y_pred))
     ```
 
-    | precision    | recall | f1-score | support |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-    | chinese      | 0.73   | 0.71     | 0.72    | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | indian       | 0.91   | 0.93     | 0.92    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | japanese     | 0.70   | 0.75     | 0.72    | 220  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | korean       | 0.86   | 0.76     | 0.81    | 242  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | thai         | 0.79   | 0.85     | 0.82    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | accuracy     | 0.80   | 1199     |         |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |              | precision | recall | f1-score | support |
+    | ------------ | --------- | ------ | -------- | ------- |
+    | chinese      | 0.73      | 0.71   | 0.72     | 229     |
+    | indian       | 0.91      | 0.93   | 0.92     | 254     |
+    | japanese     | 0.70      | 0.75   | 0.72     | 220     |
+    | korean       | 0.86      | 0.76   | 0.81     | 242     |
+    | thai         | 0.79      | 0.85   | 0.82     | 254     |
+    | accuracy     | 0.80      | 1199   |          |         |
+    | macro avg    | 0.80      | 0.80   | 0.80     | 1199    |
+    | weighted avg | 0.80      | 0.80   | 0.80     | 1199    |
 
 ## 🚀Challenge
 
 In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
+
 ## Review & Self Study
 
 Dig a little more into the math behind logistic regression in [this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
diff --git a/4-Classification/2-Classifiers-1/images/parsnip.jpg b/4-Classification/2-Classifiers-1/images/parsnip.jpg
new file mode 100644
index 000000000..30678668c
Binary files /dev/null and b/4-Classification/2-Classifiers-1/images/parsnip.jpg differ
diff --git a/4-Classification/2-Classifiers-1/solution/Julia/README.md b/4-Classification/2-Classifiers-1/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..bd1aa914b
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1292 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "lesson_11-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a classification model: Delicious Asian and Indian Cuisines"
+      ],
+      "metadata": {
+        "id": "zs2woWv_HoE8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Cuisine classifiers 1\n",
+        "\n",
+        "In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.\n",
+        "\n",
+        "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)\n",
+        "\n",
+        "### **Preparation**\n",
+        "\n",
+        "This lesson builds up on our [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) where we:\n",
+        "\n",
+        "-   Made a gentle introduction to classifications using a dataset about all the brilliant cuisines of Asia and India 😋.\n",
+        "\n",
+        "-   Explored some [dplyr verbs](https://dplyr.tidyverse.org/) to prep and clean our data.\n",
+        "\n",
+        "-   Made beautiful visualizations using ggplot2.\n",
+        "\n",
+        "-   Demonstrated how to deal with imbalanced data by preprocessing it using [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+        "\n",
+        "-   Demonstrated how to `prep` and `bake` our recipe to confirm that it will work as supposed to.\n",
+        "\n",
+        "#### **Prerequisite**\n",
+        "\n",
+        "For this lesson, we'll require the following packages to clean, prep and visualize our data:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "\n",
+        "-   `themis`: The [themis package](https://themis.tidymodels.org/) provides Extra Recipes Steps for Dealing with Unbalanced Data.\n",
+        "\n",
+        "-   `nnet`: The [nnet package](https://cran.r-project.org/web/packages/nnet/nnet.pdf) provides functions for estimating feed-forward neural networks with a single hidden layer, and for multinomial logistic regression models.\n",
+        "\n",
+        "You can have them installed as:"
+      ],
+      "metadata": {
+        "id": "iDFOb3ebHwQC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+        "\n",
+        "Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ],
+      "metadata": {
+        "id": "4V85BGCjII7F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+        "\r\n",
+        "pacman::p_load(tidyverse, tidymodels, themis, here)"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Loading required package: pacman\n",
+            "\n"
+          ]
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "an5NPyyKIKNR",
+        "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's hit the ground running!\n",
+        "\n",
+        "## 1. Split the data into training and test sets.\n",
+        "\n",
+        "We'll start by picking a few steps from our previous lesson.\n",
+        "\n",
+        "### Drop the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.\n",
+        "\n",
+        "Everyone loves rice, garlic and ginger!\n"
+      ],
+      "metadata": {
+        "id": "0ax9GQLBINVv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "source": [
+        "# Load the original cuisines data\r\n",
+        "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+        "\r\n",
+        "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+        "df_select <- df %>% \r\n",
+        "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+        "  # Encode cuisine column as categorical\r\n",
+        "  mutate(cuisine = factor(cuisine))\r\n",
+        "\r\n",
+        "# Display new data set\r\n",
+        "df_select %>% \r\n",
+        "  slice_head(n = 5)\r\n",
+        "\r\n",
+        "# Display distribution of cuisines\r\n",
+        "df_select %>% \r\n",
+        "  count(cuisine) %>% \r\n",
+        "  arrange(desc(n))"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "New names:\n",
+            "* `` -> ...1\n",
+            "\n",
+            "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+            "\n",
+            "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+            "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+            "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+            "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+            "\n",
+            "\n",
+            "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+            "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+              "1 indian  0      0        0     0          0     0            0       0       \n",
+              "2 indian  1      0        0     0          0     0            0       0       \n",
+              "3 indian  0      0        0     0          0     0            0       0       \n",
+              "4 indian  0      0        0     0          0     0            0       0       \n",
+              "5 indian  0      0        0     0          0     0            0       0       \n",
+              "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+              "1 0         ⋯ 0       0           0          0                       0    0   \n",
+              "2 0         ⋯ 0       0           0          0                       0    0   \n",
+              "3 0         ⋯ 0       0           0          0                       0    0   \n",
+              "4 0         ⋯ 0       0           0          0                       0    0   \n",
+              "5 0         ⋯ 0       0           0          0                       0    0   \n",
+              "  yam yeast yogurt zucchini\n",
+              "1 0   0     0      0       \n",
+              "2 0   0     0      0       \n",
+              "3 0   0     0      0       \n",
+              "4 0   0     0      0       \n",
+              "5 0   0     1      0       "
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 5 × 381\n",
+              "\n",
+              "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+              "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+              "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 5 × 381\n",
+              "\\begin{tabular}{lllllllllllllllllllll}\n",
+              " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+              " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+              "\\hline\n",
+              "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 5 × 381</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  cuisine  n  \n",
+              "1 korean   799\n",
+              "2 indian   598\n",
+              "3 chinese  442\n",
+              "4 japanese 320\n",
+              "5 thai     289"
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 5 × 2\n",
+              "\n",
+              "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+              "|---|---|\n",
+              "| korean   | 799 |\n",
+              "| indian   | 598 |\n",
+              "| chinese  | 442 |\n",
+              "| japanese | 320 |\n",
+              "| thai     | 289 |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 5 × 2\n",
+              "\\begin{tabular}{ll}\n",
+              " cuisine & n\\\\\n",
+              " <fct> & <int>\\\\\n",
+              "\\hline\n",
+              "\t korean   & 799\\\\\n",
+              "\t indian   & 598\\\\\n",
+              "\t chinese  & 442\\\\\n",
+              "\t japanese & 320\\\\\n",
+              "\t thai     & 289\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 5 × 2</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>korean  </td><td>799</td></tr>\n",
+              "\t<tr><td>indian  </td><td>598</td></tr>\n",
+              "\t<tr><td>chinese </td><td>442</td></tr>\n",
+              "\t<tr><td>japanese</td><td>320</td></tr>\n",
+              "\t<tr><td>thai    </td><td>289</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "id": "jhCrrH22IWVR",
+        "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Perfect! Now, time to split the data such that 70% of the data goes to training and 30% goes to testing. We'll also apply a `stratification` technique when splitting the data to `maintain the proportion of each cuisine` in the training and validation datasets.\n",
+        "\n",
+        "[rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:"
+      ],
+      "metadata": {
+        "id": "AYTjVyajIdny"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "source": [
+        "# Load the core Tidymodels packages into R session\r\n",
+        "library(tidymodels)\r\n",
+        "\r\n",
+        "# Create split specification\r\n",
+        "set.seed(2056)\r\n",
+        "cuisines_split <- initial_split(data = df_select,\r\n",
+        "                                strata = cuisine,\r\n",
+        "                                prop = 0.7)\r\n",
+        "\r\n",
+        "# Extract the data in each split\r\n",
+        "cuisines_train <- training(cuisines_split)\r\n",
+        "cuisines_test <- testing(cuisines_split)\r\n",
+        "\r\n",
+        "# Print the number of cases in each split\r\n",
+        "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+        "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+        "\r\n",
+        "# Display the first few rows of the training set\r\n",
+        "cuisines_train %>% \r\n",
+        "  slice_head(n = 5)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Display distribution of cuisines in the training set\r\n",
+        "cuisines_train %>% \r\n",
+        "  count(cuisine) %>% \r\n",
+        "  arrange(desc(n))"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training cases: 1712\n",
+            "Test cases: 736"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+              "1 chinese 0      0        0     0          0     0            0       0       \n",
+              "2 chinese 0      0        0     0          0     0            0       0       \n",
+              "3 chinese 0      0        0     0          0     0            0       0       \n",
+              "4 chinese 0      0        0     0          0     0            0       0       \n",
+              "5 chinese 0      0        0     0          0     0            0       0       \n",
+              "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+              "1 0         ⋯ 0       0           0          0                       1    0   \n",
+              "2 0         ⋯ 0       0           0          0                       1    0   \n",
+              "3 0         ⋯ 0       0           0          0                       0    0   \n",
+              "4 0         ⋯ 0       0           0          0                       0    0   \n",
+              "5 0         ⋯ 0       0           0          0                       0    0   \n",
+              "  yam yeast yogurt zucchini\n",
+              "1 0   0     0      0       \n",
+              "2 0   0     0      0       \n",
+              "3 0   0     0      0       \n",
+              "4 0   0     0      0       \n",
+              "5 0   0     0      0       "
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 5 × 381\n",
+              "\n",
+              "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+              "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+              "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 5 × 381\n",
+              "\\begin{tabular}{lllllllllllllllllllll}\n",
+              " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+              " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+              "\\hline\n",
+              "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 5 × 381</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  cuisine  n  \n",
+              "1 korean   559\n",
+              "2 indian   418\n",
+              "3 chinese  309\n",
+              "4 japanese 224\n",
+              "5 thai     202"
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 5 × 2\n",
+              "\n",
+              "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+              "|---|---|\n",
+              "| korean   | 559 |\n",
+              "| indian   | 418 |\n",
+              "| chinese  | 309 |\n",
+              "| japanese | 224 |\n",
+              "| thai     | 202 |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 5 × 2\n",
+              "\\begin{tabular}{ll}\n",
+              " cuisine & n\\\\\n",
+              " <fct> & <int>\\\\\n",
+              "\\hline\n",
+              "\t korean   & 559\\\\\n",
+              "\t indian   & 418\\\\\n",
+              "\t chinese  & 309\\\\\n",
+              "\t japanese & 224\\\\\n",
+              "\t thai     & 202\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 5 × 2</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>korean  </td><td>559</td></tr>\n",
+              "\t<tr><td>indian  </td><td>418</td></tr>\n",
+              "\t<tr><td>chinese </td><td>309</td></tr>\n",
+              "\t<tr><td>japanese</td><td>224</td></tr>\n",
+              "\t<tr><td>thai    </td><td>202</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 535
+        },
+        "id": "w5FWIkEiIjdN",
+        "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Deal with imbalanced data\n",
+        "\n",
+        "As you might have noticed in the original data set as well as in our training set, there is quite an unequal distribution in the number of cuisines. Korean cuisines are *almost* 3 times Thai cuisines. Imbalanced data often has negative effects on the model performance. Many models perform best when the number of observations is equal and, thus, tend to struggle with unbalanced data.\n",
+        "\n",
+        "There are majorly two ways of dealing with imbalanced data sets:\n",
+        "\n",
+        "-   adding observations to the minority class: `Over-sampling` e.g using a SMOTE algorithm which synthetically generates new examples of the minority class using nearest neighbors of these cases.\n",
+        "\n",
+        "-   removing observations from majority class: `Under-sampling`\n",
+        "\n",
+        "In our previous lesson, we demonstrated how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis. In our case, we want to have an equal distribution in the number of our cuisines for our `training set`. Let's get right into it."
+      ],
+      "metadata": {
+        "id": "daBi9qJNIwqW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "source": [
+        "# Load themis package for dealing with imbalanced data\r\n",
+        "library(themis)\r\n",
+        "\r\n",
+        "# Create a recipe for preprocessing training data\r\n",
+        "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+        "  step_smote(cuisine)\r\n",
+        "\r\n",
+        "# Print recipe\r\n",
+        "cuisines_recipe"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Data Recipe\n",
+              "\n",
+              "Inputs:\n",
+              "\n",
+              "      role #variables\n",
+              "   outcome          1\n",
+              " predictor        380\n",
+              "\n",
+              "Operations:\n",
+              "\n",
+              "SMOTE based on cuisine"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 200
+        },
+        "id": "Az6LFBGxI1X0",
+        "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can of course go ahead and confirm (using prep+bake) that the recipe will work as you expect it - all the cuisine labels having `559` observations.\r\n",
+        "\r\n",
+        "Since we'll be using this recipe as a preprocessor for modeling, a `workflow()` will do all the prep and bake for us, so we won't have to manually estimate the recipe.\r\n",
+        "\r\n",
+        "Now we are ready to train a model 👩‍💻👨‍💻!\r\n",
+        "\r\n",
+        "## 3. Choosing your classifier\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../../images/parsnip.jpg\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n"
+      ],
+      "metadata": {
+        "id": "NBL3PqIWJBBB"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we have to decide which algorithm to use for the job 🤔.\r\n",
+        "\r\n",
+        "In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguements](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques:\r\n",
+        "\r\n",
+        "-   C5.0 Rule-Based Classification Models\r\n",
+        "\r\n",
+        "-   Flexible Discriminant Models\r\n",
+        "\r\n",
+        "-   Linear Discriminant Models\r\n",
+        "\r\n",
+        "-   Regularized Discriminant Models\r\n",
+        "\r\n",
+        "-   Logistic Regression Models\r\n",
+        "\r\n",
+        "-   Multinomial Regression Models\r\n",
+        "\r\n",
+        "-   Naive Bayes Models\r\n",
+        "\r\n",
+        "-   Support Vector Machines\r\n",
+        "\r\n",
+        "-   Nearest Neighbors\r\n",
+        "\r\n",
+        "-   Decision Trees\r\n",
+        "\r\n",
+        "-   Ensemble methods\r\n",
+        "\r\n",
+        "-   Neural Networks\r\n",
+        "\r\n",
+        "The list goes on!\r\n",
+        "\r\n",
+        "### **What classifier to go with?**\r\n",
+        "\r\n",
+        "So, which classifier should you choose? Often, running through several and looking for a good result is a way to test.\r\n",
+        "\r\n",
+        "> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)\r\n",
+        "\r\n",
+        "Also the choice of classifier depends on our problem. For instance, when the outcome can be categorized into `more than two classes`, like in our case, you must use a `multiclass classification algorithm` as opposed to `binary classification.`\r\n",
+        "\r\n",
+        "### **A better approach**\r\n",
+        "\r\n",
+        "A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../../images/cheatsheet.png\"\r\n",
+        "   width=\"500\"/>\r\n",
+        "   <figcaption>A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options</figcaption>\r\n",
+        "\r\n"
+      ],
+      "metadata": {
+        "id": "a6DLAZ3vJZ14"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Reasoning**\n",
+        "\n",
+        "Let's see if we can reason our way through different approaches given the constraints we have:\n",
+        "\n",
+        "-   **Deep Neural networks are too heavy**. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, deep neural networks are too heavyweight for this task.\n",
+        "\n",
+        "-   **No two-class classifier**. We do not use a two-class classifier, so that rules out one-vs-all.\n",
+        "\n",
+        "-   **Decision tree or logistic regression could work**. A decision tree might work, or multinomial regression/multiclass logistic regression for multiclass data.\n",
+        "\n",
+        "-   **Multiclass Boosted Decision Trees solve a different problem**. The multiclass boosted decision tree is most suitable for nonparametric tasks, e.g. tasks designed to build rankings, so it is not useful for us.\n",
+        "\n",
+        "Also, normally before embarking on more complex machine learning models e.g ensemble methods, it's a good idea to build the simplest possible model to get an idea of what is going on. So for this lesson, we'll start with a `multinomial regression` model.\n",
+        "\n",
+        "> Logistic regression is a technique used when the outcome variable is categorical (or nominal). For Binary logistic regression the number of outcome variables is two, whereas the number of outcome variables for multinomial logistic regression is more than two. See [Advanced Regression Methods](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) for further reading.\n",
+        "\n",
+        "## 4. Train and evaluate a Multinomial logistic regression model.\n",
+        "\n",
+        "In Tidymodels, `parsnip::multinom_reg()`, defines a model that uses linear predictors to predict multiclass data using the multinomial distribution. See `?multinom_reg()` for the different ways/engines you can use to fit this model.\n",
+        "\n",
+        "For this example, we'll fit a Multinomial regression model via the default [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) engine.\n",
+        "\n",
+        "> I picked a value for `penalty` sort of randomly. There are better ways to choose this value that is, by using `resampling` and `tuning` the model which we'll discuss later.\n",
+        ">\n",
+        "> See [Tidymodels: Get Started](https://www.tidymodels.org/start/tuning/) in case you want to learn more on how to tune model hyperparameters."
+      ],
+      "metadata": {
+        "id": "gWMsVcbBJemu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "source": [
+        "# Create a multinomial regression model specification\r\n",
+        "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+        "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+        "  set_mode(\"classification\")\r\n",
+        "\r\n",
+        "# Print model specification\r\n",
+        "mr_spec"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Multinomial Regression Model Specification (classification)\n",
+              "\n",
+              "Main Arguments:\n",
+              "  penalty = 1\n",
+              "\n",
+              "Engine-Specific Arguments:\n",
+              "  MaxNWts = 2086\n",
+              "\n",
+              "Computational engine: nnet \n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        },
+        "id": "Wq_fcyQiJvfG",
+        "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Great job 🥳! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data then fit the model on the preprocessed data and also allow for potential post-processing activities. In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.\n",
+        "\n",
+        "So let's bundle everything up into a workflow!📦"
+      ],
+      "metadata": {
+        "id": "NlSbzDfgJ0zh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "source": [
+        "# Bundle recipe and model specification\r\n",
+        "mr_wf <- workflow() %>% \r\n",
+        "  add_recipe(cuisines_recipe) %>% \r\n",
+        "  add_model(mr_spec)\r\n",
+        "\r\n",
+        "# Print out workflow\r\n",
+        "mr_wf"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+              "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+              "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+              "\n",
+              "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+              "1 Recipe Step\n",
+              "\n",
+              "• step_smote()\n",
+              "\n",
+              "── Model ───────────────────────────────────────────────────────────────────────\n",
+              "Multinomial Regression Model Specification (classification)\n",
+              "\n",
+              "Main Arguments:\n",
+              "  penalty = 1\n",
+              "\n",
+              "Engine-Specific Arguments:\n",
+              "  MaxNWts = 2086\n",
+              "\n",
+              "Computational engine: nnet \n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "id": "Sc1TfPA4Ke3_",
+        "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Workflows 👌👌! A **`workflow()`** can be fit in much the same way a model can. So, time to train a model!"
+      ],
+      "metadata": {
+        "id": "TNQ8i85aKf9L"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "source": [
+        "# Train a multinomial regression model\n",
+        "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+        "\n",
+        "mr_fit"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+              "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+              "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+              "\n",
+              "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+              "1 Recipe Step\n",
+              "\n",
+              "• step_smote()\n",
+              "\n",
+              "── Model ───────────────────────────────────────────────────────────────────────\n",
+              "Call:\n",
+              "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+              "    trace = FALSE)\n",
+              "\n",
+              "Coefficients:\n",
+              "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+              "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+              "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+              "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+              "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+              "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+              "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+              "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+              "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+              "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+              "             avocado       bacon baked_potato balm     banana     barley\n",
+              "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+              "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+              "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+              "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+              "         bartlett_pear      basil        bay       bean         beech\n",
+              "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+              "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+              "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+              "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+              "               beef beef_broth   beef_liver         beer        beet\n",
+              "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+              "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+              "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+              "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+              "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+              "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+              "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+              "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+              "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+              "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+              "indian               0             0.38935801   -0.4453495               0\n",
+              "japanese             0            -0.05452887   -0.5440869               0\n",
+              "korean               0            -0.03929970    0.8025454               0\n",
+              "thai                 0            -0.21498372   -0.9854806               0\n",
+              "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+              "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+              "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+              "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+              "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+              "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+              "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+              "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+              "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+              "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+              "\n",
+              "...\n",
+              "and 308 more lines."
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "GMbdfVmTKkJI",
+        "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The output shows the coefficients that the model learned during training.\n",
+        "\n",
+        "### Evaluate the Trained Model\n",
+        "\n",
+        "It's time to see how the model performed 📏 by evaluating it on a test set! Let's begin by making predictions on the test set."
+      ],
+      "metadata": {
+        "id": "tt2BfOxrKmcJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "source": [
+        "# Make predictions on the test set\n",
+        "results <- cuisines_test %>% select(cuisine) %>% \n",
+        "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+        "\n",
+        "# Print out results\n",
+        "results %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  cuisine .pred_class\n",
+              "1 indian  thai       \n",
+              "2 indian  indian     \n",
+              "3 indian  indian     \n",
+              "4 indian  indian     \n",
+              "5 indian  indian     "
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 5 × 2\n",
+              "\n",
+              "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+              "|---|---|\n",
+              "| indian | thai   |\n",
+              "| indian | indian |\n",
+              "| indian | indian |\n",
+              "| indian | indian |\n",
+              "| indian | indian |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 5 × 2\n",
+              "\\begin{tabular}{ll}\n",
+              " cuisine & .pred\\_class\\\\\n",
+              " <fct> & <fct>\\\\\n",
+              "\\hline\n",
+              "\t indian & thai  \\\\\n",
+              "\t indian & indian\\\\\n",
+              "\t indian & indian\\\\\n",
+              "\t indian & indian\\\\\n",
+              "\t indian & indian\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 5 × 2</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "CqtckvtsKqax",
+        "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Great job! In Tidymodels, evaluating model performance can be done using [yardstick](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics. As we did in our logistic regression lesson, let's begin by computing a confusion matrix."
+      ],
+      "metadata": {
+        "id": "8w5N6XsBKss7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "source": [
+        "# Confusion matrix for categorical data\n",
+        "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "          Truth\n",
+              "Prediction chinese indian japanese korean thai\n",
+              "  chinese       83      1        8     15   10\n",
+              "  indian         4    163        1      2    6\n",
+              "  japanese      21      5       73     25    1\n",
+              "  korean        15      0       11    191    0\n",
+              "  thai          10     11        3      7   70"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133
+        },
+        "id": "YvODvsLkK0iG",
+        "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "When dealing with multiple classes, it's generally more intuitive to visualize this as a heat map, like this:"
+      ],
+      "metadata": {
+        "id": "c0HfPL16Lr6U"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "source": [
+        "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+        "# Visualize confusion matrix\n",
+        "results %>% \n",
+        "  conf_mat(cuisine, .pred_class) %>% \n",
+        "  autoplot(type = \"heatmap\")"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "plot without title"
+            ],
+            "image/png": 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"
+          },
+          "metadata": {
+            "image/png": {
+              "width": 420,
+              "height": 420
+            }
+          }
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 436
+        },
+        "id": "HsAtwukyLsvt",
+        "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The darker squares in the confusion matrix plot indicate high numbers of cases, and you can hopefully see a diagonal line of darker squares indicating cases where the predicted and actual label are the same.\n",
+        "\n",
+        "Let's now calculate summary statistics for the confusion matrix."
+      ],
+      "metadata": {
+        "id": "oOJC87dkLwPr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "source": [
+        "# Summary stats for confusion matrix\n",
+        "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+        "summary()"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   .metric              .estimator .estimate\n",
+              "1  accuracy             multiclass 0.7880435\n",
+              "2  kap                  multiclass 0.7276583\n",
+              "3  sens                 macro      0.7780927\n",
+              "4  spec                 macro      0.9477598\n",
+              "5  ppv                  macro      0.7585583\n",
+              "6  npv                  macro      0.9460080\n",
+              "7  mcc                  multiclass 0.7292724\n",
+              "8  j_index              macro      0.7258524\n",
+              "9  bal_accuracy         macro      0.8629262\n",
+              "10 detection_prevalence macro      0.2000000\n",
+              "11 precision            macro      0.7585583\n",
+              "12 recall               macro      0.7780927\n",
+              "13 f_meas               macro      0.7641862"
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 13 × 3\n",
+              "\n",
+              "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+              "|---|---|---|\n",
+              "| accuracy             | multiclass | 0.7880435 |\n",
+              "| kap                  | multiclass | 0.7276583 |\n",
+              "| sens                 | macro      | 0.7780927 |\n",
+              "| spec                 | macro      | 0.9477598 |\n",
+              "| ppv                  | macro      | 0.7585583 |\n",
+              "| npv                  | macro      | 0.9460080 |\n",
+              "| mcc                  | multiclass | 0.7292724 |\n",
+              "| j_index              | macro      | 0.7258524 |\n",
+              "| bal_accuracy         | macro      | 0.8629262 |\n",
+              "| detection_prevalence | macro      | 0.2000000 |\n",
+              "| precision            | macro      | 0.7585583 |\n",
+              "| recall               | macro      | 0.7780927 |\n",
+              "| f_meas               | macro      | 0.7641862 |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 13 × 3\n",
+              "\\begin{tabular}{lll}\n",
+              " .metric & .estimator & .estimate\\\\\n",
+              " <chr> & <chr> & <dbl>\\\\\n",
+              "\\hline\n",
+              "\t accuracy             & multiclass & 0.7880435\\\\\n",
+              "\t kap                  & multiclass & 0.7276583\\\\\n",
+              "\t sens                 & macro      & 0.7780927\\\\\n",
+              "\t spec                 & macro      & 0.9477598\\\\\n",
+              "\t ppv                  & macro      & 0.7585583\\\\\n",
+              "\t npv                  & macro      & 0.9460080\\\\\n",
+              "\t mcc                  & multiclass & 0.7292724\\\\\n",
+              "\t j\\_index              & macro      & 0.7258524\\\\\n",
+              "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+              "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+              "\t precision            & macro      & 0.7585583\\\\\n",
+              "\t recall               & macro      & 0.7780927\\\\\n",
+              "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 13 × 3</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+              "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+              "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+              "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+              "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+              "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+              "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+              "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+              "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+              "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+              "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+              "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+              "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 494
+        },
+        "id": "OYqetUyzL5Wz",
+        "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "If we narrow down to some metrics such as accuracy, sensitivity, ppv, we are not badly off for a start 🥳!\n",
+        "\n",
+        "## 4. Digging Deeper\n",
+        "\n",
+        "Let's ask one subtle question: What criteria is used to settle for a given type of cuisine as the predicted outcome?\n",
+        "\n",
+        "Well, Statistical machine learning algorithms, like logistic regression, are based on `probability`; so what actually gets predicted by a classifier is a probability distribution over a set of possible outcomes. The class with the highest probability is then chosen as the most likely outcome for the given observations.\n",
+        "\n",
+        "Let's see this in action by making both hard class predictions and probabilities."
+      ],
+      "metadata": {
+        "id": "43t7vz8vMJtW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "source": [
+        "# Make hard class prediction and probabilities\n",
+        "results_prob <- cuisines_test %>%\n",
+        "  select(cuisine) %>% \n",
+        "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+        "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+        "\n",
+        "# Print out results\n",
+        "results_prob %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+              "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+              "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+              "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+              "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+              "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+              "  .pred_thai  \n",
+              "1 5.388194e-01\n",
+              "2 1.577948e-06\n",
+              "3 6.874989e-03\n",
+              "4 3.863391e-03\n",
+              "5 5.653283e-03"
+            ],
+            "text/markdown": [
+              "\n",
+              "A tibble: 5 × 7\n",
+              "\n",
+              "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+              "|---|---|---|---|---|---|---|\n",
+              "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+              "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+              "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+              "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+              "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+              "\n"
+            ],
+            "text/latex": [
+              "A tibble: 5 × 7\n",
+              "\\begin{tabular}{lllllll}\n",
+              " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+              " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+              "\\hline\n",
+              "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+              "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+              "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+              "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+              "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+              "\\end{tabular}\n"
+            ],
+            "text/html": [
+              "<table class=\"dataframe\">\n",
+              "<caption>A tibble: 5 × 7</caption>\n",
+              "<thead>\n",
+              "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+              "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+              "</tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "xdKNs-ZPMTJL",
+        "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Much better!\n",
+        "\n",
+        "✅ Can you explain why the model is pretty sure that the first observation is Thai?\n",
+        "\n",
+        "## **🚀Challenge**\n",
+        "\n",
+        "In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the [many options](https://www.tidymodels.org/find/parsnip/#models) Tidymodels provides to classify data and [other ways](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) to fit multinomial regression.\n",
+        "\n",
+        "#### THANK YOU TO:\n",
+        "\n",
+        "[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+        "\n",
+        "[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\n",
+        "\n",
+        "<br>\n",
+        "Would have thrown in some jokes but I donut understand food puns 😅.\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "Happy Learning,\n",
+        "\n",
+        "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n"
+      ],
+      "metadata": {
+        "id": "2tWVHMeLMYdM"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
new file mode 100644
index 000000000..a4221217b
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
@@ -0,0 +1,349 @@
+---
+title: 'Build a classification model: Delicious Asian and Indian Cuisines'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Cuisine classifiers 1
+
+In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
+
+### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+
+### **Preparation**
+
+This lesson builds up on our [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) where we:
+
+-   Made a gentle introduction to classifications using a dataset about all the brilliant cuisines of Asia and India 😋.
+
+-   Explored some [dplyr verbs](https://dplyr.tidyverse.org/) to prep and clean our data.
+
+-   Made beautiful visualizations using ggplot2.
+
+-   Demonstrated how to deal with imbalanced data by preprocessing it using [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).
+
+-   Demonstrated how to `prep` and `bake` our recipe to confirm that it will work as supposed to.
+
+#### **Prerequisite**
+
+For this lesson, we'll require the following packages to clean, prep and visualize our data:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `DataExplorer`: The [DataExplorer package](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) is meant to simplify and automate EDA process and report generation.
+
+-   `themis`: The [themis package](https://themis.tidymodels.org/) provides Extra Recipes Steps for Dealing with Unbalanced Data.
+
+-   `nnet`: The [nnet package](https://cran.r-project.org/web/packages/nnet/nnet.pdf) provides functions for estimating feed-forward neural networks with a single hidden layer, and for multinomial logistic regression models.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "DataExplorer", "here"))`
+
+Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman"))install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)
+```
+
+Now, let's hit the ground running!
+
+## 1. Split the data into training and test sets.
+
+We'll start by picking a few steps from our previous lesson.
+
+### Drop the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.
+
+Everyone loves rice, garlic and ginger!
+
+```{r recap_drop}
+# Load the original cuisines data
+df <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv")
+
+# Drop id column, rice, garlic and ginger from our original data set
+df_select <- df %>% 
+  select(-c(1, rice, garlic, ginger)) %>%
+  # Encode cuisine column as categorical
+  mutate(cuisine = factor(cuisine))
+
+# Display new data set
+df_select %>% 
+  slice_head(n = 5)
+
+# Display distribution of cuisines
+df_select %>% 
+  count(cuisine) %>% 
+  arrange(desc(n))
+```
+
+Perfect! Now, time to split the data such that 70% of the data goes to training and 30% goes to testing. We'll also apply a `stratification` technique when splitting the data to `maintain the proportion of each cuisine` in the training and validation datasets.
+
+[rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:
+
+```{r data_split}
+# Load the core Tidymodels packages into R session
+library(tidymodels)
+
+# Create split specification
+set.seed(2056)
+cuisines_split <- initial_split(data = df_select,
+                                strata = cuisine,
+                                prop = 0.7)
+
+# Extract the data in each split
+cuisines_train <- training(cuisines_split)
+cuisines_test <- testing(cuisines_split)
+
+# Print the number of cases in each split
+cat("Training cases: ", nrow(cuisines_train), "\n",
+    "Test cases: ", nrow(cuisines_test), sep = "")
+
+# Display the first few rows of the training set
+cuisines_train %>% 
+  slice_head(n = 5)
+
+
+# Display distribution of cuisines in the training set
+cuisines_train %>% 
+  count(cuisine) %>% 
+  arrange(desc(n))
+
+
+```
+
+## 2. Deal with imbalanced data
+
+As you might have noticed in the original data set as well as in our training set, there is quite an unequal distribution in the number of cuisines. Korean cuisines are *almost* 3 times Thai cuisines. Imbalanced data often has negative effects on the model performance. Many models perform best when the number of observations is equal and, thus, tend to struggle with unbalanced data.
+
+There are majorly two ways of dealing with imbalanced data sets:
+
+-   adding observations to the minority class: `Over-sampling` e.g using a SMOTE algorithm which synthetically generates new examples of the minority class using nearest neighbors of these cases.
+
+-   removing observations from majority class: `Under-sampling`
+
+In our previous lesson, we demonstrated how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis. In our case, we want to have an equal distribution in the number of our cuisines for our `training set`. Let's get right into it.
+
+```{r recap_balance}
+# Load themis package for dealing with imbalanced data
+library(themis)
+
+# Create a recipe for preprocessing training data
+cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% 
+  step_smote(cuisine)
+
+# Print recipe
+cuisines_recipe
+
+```
+
+You can of course go ahead and confirm (using prep+bake) that the recipe will work as you expect it - all the cuisine labels having `559` observations.
+
+Since we'll be using this recipe as a preprocessor for modeling, a `workflow()` will do all the prep and bake for us, so we won't have to manually estimate the recipe.
+
+Now we are ready to train a model 👩‍💻👨‍💻!
+
+## 3. Choosing your classifier
+
+![Artwork by \@allison_horst](../../images/parsnip.jpg){width="600"}
+
+Now we have to decide which algorithm to use for the job 🤔.
+
+In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguements](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques:
+
+-   C5.0 Rule-Based Classification Models
+
+-   Flexible Discriminant Models
+
+-   Linear Discriminant Models
+
+-   Regularized Discriminant Models
+
+-   Logistic Regression Models
+
+-   Multinomial Regression Models
+
+-   Naive Bayes Models
+
+-   Support Vector Machines
+
+-   Nearest Neighbors
+
+-   Decision Trees
+
+-   Ensemble methods
+
+-   Neural Networks
+
+The list goes on!
+
+### **What classifier to go with?**
+
+So, which classifier should you choose? Often, running through several and looking for a good result is a way to test.
+
+> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)
+
+Also the choice of classifier depends on our problem. For instance, when the outcome can be categorized into `more than two classes`, like in our case, you must use a `multiclass classification algorithm` as opposed to `binary classification.`
+
+### **A better approach**
+
+A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:
+
+![A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options](../../images/cheatsheet.png){width="500"}
+
+### **Reasoning**
+
+Let's see if we can reason our way through different approaches given the constraints we have:
+
+-   **Deep Neural networks are too heavy**. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, deep neural networks are too heavyweight for this task.
+
+-   **No two-class classifier**. We do not use a two-class classifier, so that rules out one-vs-all.
+
+-   **Decision tree or logistic regression could work**. A decision tree might work, or multinomial regression/multiclass logistic regression for multiclass data.
+
+-   **Multiclass Boosted Decision Trees solve a different problem**. The multiclass boosted decision tree is most suitable for nonparametric tasks, e.g. tasks designed to build rankings, so it is not useful for us.
+
+Also, normally before embarking on more complex machine learning models e.g ensemble methods, it's a good idea to build the simplest possible model to get an idea of what is going on. So for this lesson, we'll start with a `multinomial logistic regression` model.
+
+> Logistic regression is a technique used when the outcome variable is categorical (or nominal). For Binary logistic regression the number of outcome variables is two, whereas the number of outcome variables for multinomial logistic regression is more than two. See [Advanced Regression Methods](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) for further reading.
+
+## 4. Train and evaluate a Multinomial logistic regression model.
+
+In Tidymodels, `parsnip::multinom_reg()`, defines a model that uses linear predictors to predict multiclass data using the multinomial distribution. See `?multinom_reg()` for the different ways/engines you can use to fit this model.
+
+For this example, we'll fit a Multinomial regression model via the default [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) engine.
+
+> I picked a value for `penalty` sort of randomly. There are better ways to choose this value that is, by using `resampling` and `tuning` the model which we'll discuss later.
+>
+> See [Tidymodels: Get Started](https://www.tidymodels.org/start/tuning/) in case you want to learn more on how to tune model hyperparameters.
+
+```{r multinorm_reg}
+# Create a multinomial regression model specification
+mr_spec <- multinom_reg(penalty = 1) %>% 
+  set_engine("nnet", MaxNWts = 2086) %>% 
+  set_mode("classification")
+
+# Print model specification
+mr_spec
+
+```
+
+Great job 🥳! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data then fit the model on the preprocessed data and also allow for potential post-processing activities. In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.
+
+So let's bundle everything up into a workflow!📦
+
+```{r workflow}
+# Bundle recipe and model specification
+mr_wf <- workflow() %>% 
+  add_recipe(cuisines_recipe) %>% 
+  add_model(mr_spec)
+
+# Print out workflow
+mr_wf
+
+```
+
+Workflows 👌👌! A **`workflow()`** can be fit in much the same way a model can. So, time to train a model!
+
+```{r train}
+# Train a multinomial regression model
+mr_fit <- fit(object = mr_wf, data = cuisines_train)
+
+mr_fit
+```
+
+The output shows the coefficients that the model learned during training.
+
+### Evaluate the Trained Model
+
+It's time to see how the model performed 📏 by evaluating it on a test set! Let's begin by making predictions on the test set.
+
+```{r test}
+# Make predictions on the test set
+results <- cuisines_test %>% select(cuisine) %>% 
+  bind_cols(mr_fit %>% predict(new_data = cuisines_test))
+
+# Print out results
+results %>% 
+  slice_head(n = 5)
+
+```
+
+Great job! In Tidymodels, evaluating model performance can be done using [yardstick](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics. As we did in our logistic regression lesson, let's begin by computing a confusion matrix.
+
+```{r conf_mat}
+# Confusion matrix for categorical data
+conf_mat(data = results, truth = cuisine, estimate = .pred_class)
+
+
+```
+
+When dealing with multiple classes, it's generally more intuitive to visualize this as a heat map, like this:
+
+```{r conf_viz}
+update_geom_defaults(geom = "tile", new = list(color = "black", alpha = 0.7))
+# Visualize confusion matrix
+results %>% 
+  conf_mat(cuisine, .pred_class) %>% 
+  autoplot(type = "heatmap")
+```
+
+The darker squares in the confusion matrix plot indicate high numbers of cases, and you can hopefully see a diagonal line of darker squares indicating cases where the predicted and actual label are the same.
+
+Let's now calculate summary statistics for the confusion matrix.
+
+```{r conf_stats}
+# Summary stats for confusion matrix
+conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% summary()
+```
+
+If we narrow down to some metrics such as accuracy, sensitivity, ppv, we are not badly off for a start 🥳!
+
+## 4. Digging Deeper
+
+Let's ask one subtle question: What criteria is used to settle for a given type of cuisine as the predicted outcome?
+
+Well, Statistical machine learning algorithms, like logistic regression, are based on `probability`; so what actually gets predicted by a classifier is a probability distribution over a set of possible outcomes. The class with the highest probability is then chosen as the most likely outcome for the given observations.
+
+Let's see this in action by making both hard class predictions and probabilities.
+
+```{r pred_prob}
+# Make hard class prediction and probabilities
+results_prob <- cuisines_test %>%
+  select(cuisine) %>% 
+  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% 
+  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = "prob"))
+
+# Print out results
+results_prob %>% 
+  slice_head(n = 5)
+  
+
+```
+
+Much better!
+
+✅ Can you explain why the model is pretty sure that the first observation is Thai?
+
+## **🚀Challenge**
+
+In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the [many options](https://www.tidymodels.org/find/parsnip/#models) Tidymodels provides to classify data and [other ways](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) to fit multinomial regression.
+
+#### THANK YOU TO:
+
+[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).
+
+[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️
+
+Happy Learning,
+
+[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.
diff --git a/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
index a819dbe5b..770ac85c7 100644
--- a/4-Classification/2-Classifiers-1/solution/notebook.ipynb
+++ b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },
diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md
new file mode 100644
index 000000000..4128c5103
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/README.it.md
@@ -0,0 +1,241 @@
+# Classificatori di cucina 1
+
+In questa lezione, si utilizzerà l'insieme di dati salvati dall'ultima lezione, pieno di dati equilibrati e puliti relativi alle cucine.
+
+Si utilizzerà questo insieme di dati con una varietà di classificatori per _prevedere una determinata cucina nazionale in base a un gruppo di ingredienti_. Mentre si fa questo, si imparerà di più su alcuni dei modi in cui gli algoritmi possono essere sfruttati per le attività di classificazione.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=it)
+# Preparazione
+
+Supponendo che la [Lezione 1](../1-Introduction/README.md) sia stata completata, assicurarsi che _esista_ un file clean_cuisines.csv nella cartella in radice `/data` per queste quattro lezioni.
+
+## Esercizio - prevedere una cucina nazionale
+
+1. Lavorando con il  _notebook.ipynb_ di questa lezione nella cartella radice, importare quel file insieme alla libreria Pandas:
+
+   ```python
+   import pandas as pd
+   cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+   cuisines_df.head()
+   ```
+
+   I dati si presentano così:
+
+   ```output
+   |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+   | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+   | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+   ```
+
+1. Ora importare molte altre librerie:
+
+   ```python
+   from sklearn.linear_model import LogisticRegression
+   from sklearn.model_selection import train_test_split, cross_val_score
+   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+   from sklearn.svm import SVC
+   import numpy as np
+   ```
+
+1. Dividere le coordinate X e y in due dataframe per l'addestramento. `cuisine` può essere il dataframe delle etichette:
+
+   ```python
+   cuisines_label_df = cuisines_df['cuisine']
+   cuisines_label_df.head()
+   ```
+
+   Apparirà così
+
+   ```output
+   0    indian
+   1    indian
+   2    indian
+   3    indian
+   4    indian
+   Name: cuisine, dtype: object
+   ```
+
+1. Scartare la colonna `Unnamed: 0` e la colonna `cuisine` , chiamando `drop()`. Salvare il resto dei dati come caratteristiche addestrabili:
+
+   ```python
+   cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+   cuisines_feature_df.head()
+   ```
+
+   Le caratteristiche sono così:
+
+    | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
+    | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
+    |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+
+Ora si è pronti per addestrare il modello!
+
+## Scegliere il classificatore
+
+Ora che i dati sono puliti e pronti per l'addestramento, si deve decidere quale algoritmo utilizzare per il lavoro.
+
+Scikit-learn raggruppa la classificazione in Supervised Learning, e in quella categoria si troveranno molti modi per classificare. [La varietà](https://scikit-learn.org/stable/supervised_learning.html) è piuttosto sconcertante a prima vista. I seguenti metodi includono tutti tecniche di classificazione:
+
+- Modelli Lineari 
+- Macchine a Vettori di Supporto
+- Discesa stocastica del gradiente
+- Nearest Neighbors
+- Processi Gaussiani
+- Alberi di Decisione
+- Apprendimento ensemble (classificatore di voto)
+- Algoritmi multiclasse e multioutput (classificazione multiclasse e multietichetta, classificazione multiclasse-multioutput)
+
+> Si possono anche usare [le reti neurali per classificare i dati](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), ma questo esula dall'ambito di questa lezione.
+
+### Con quale classificatore andare?
+
+Quale classificatore si dovrebbe scegliere? Spesso, scorrerne diversi e cercare un buon risultato è un modo per testare. Scikit-learn offre un [confronto fianco](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) a fianco su un insieme di dati creato, confrontando KNeighbors, SVC in due modi, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB e QuadraticDiscrinationAnalysis, mostrando i risultati visualizzati:
+
+![confronto di classificatori](../images/comparison.png)
+> Grafici generati sulla documentazione di Scikit-learn
+
+> AutoML risolve questo problema in modo ordinato eseguendo questi confronti nel cloud, consentendo di scegliere l'algoritmo migliore per i propri dati. Si può provare [qui](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)
+
+### Un approccio migliore
+
+Un modo migliore che indovinare a caso, tuttavia, è seguire le idee su questo [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa) scaricabile. Qui si scopre che, per questo problema multiclasse, si dispone di  alcune scelte:
+
+![cheatsheet per problemi multiclasse](../images/cheatsheet.png)
+> Una sezione dell'Algorithm Cheat Sheet di Microsoft, che descrive in dettaglio le opzioni di classificazione multiclasse
+
+✅ Scaricare questo cheat sheet, stamparlo e appenderlo alla parete!
+
+### Motivazione
+
+Si prova a ragionare attraverso diversi approcci dati i vincoli presenti:
+
+- **Le reti neurali sono troppo pesanti**. Dato l'insieme di dati pulito, ma minimo, e il fatto che si sta eseguendo l'addestramento localmente tramite notebook, le reti neurali sono troppo pesanti per questo compito.
+- **Nessun classificatore a due classi**. Non si usa un classificatore a due classi, quindi questo esclude uno contro tutti.
+- L'**albero decisionale o la regressione logistica potrebbero funzionare**. Potrebbe funzionare un albero decisionale o una regressione logistica per dati multiclasse.
+- **Gli alberi decisionali potenziati multiclasse risolvono un problema diverso**. L'albero decisionale potenziato multiclasse è più adatto per attività non parametriche, ad esempio attività progettate per costruire classifiche, quindi non è utile in questo caso.
+
+### Utilizzo di Scikit-learn
+
+Si userà Scikit-learn per analizzare i  dati. Tuttavia, ci sono molti modi per utilizzare la regressione logistica in Scikit-learn. Dare un'occhiata ai [parametri da passare](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Essenzialmente ci sono due importanti parametri `multi_class` e `solver`, che occorre specificare, quando si chiede a Scikit-learn di eseguire una regressione logistica. Il valore `multi_class` si applica un certo comportamento. Il valore del risolutore è quale algoritmo utilizzare. Non tutti i risolutori possono essere associati a tutti i valori `multi_class` .
+
+Secondo la documentazione, nel caso multiclasse, l'algoritmo di addestramento:
+
+- **Utilizza lo schema one-vs-rest (OvR)** - uno contro tutti, se l'opzione `multi_class` è impostata su `ovr`
+- **Utilizza la perdita di entropia incrociata**, se l 'opzione `multi_class` è impostata su `multinomial`. (Attualmente l'opzione multinomiale è supportata solo dai solutori 'lbfgs', 'sag', 'saga' e 'newton-cg')."
+
+> 🎓 Lo 'schema' qui può essere 'ovr' (one-vs-rest) - uno contro tutti - o 'multinomiale'. Poiché la regressione logistica è realmente progettata per supportare la classificazione binaria, questi schemi consentono di gestire meglio le attività di classificazione multiclasse. [fonte](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> 🎓 Il 'solver' è definito come "l'algoritmo da utilizzare nel problema di ottimizzazione". [fonte](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Scikit-learn offre questa tabella per spiegare come i risolutori gestiscono le diverse sfide presentate da diversi tipi di strutture dati:
+
+![risolutori](../images/solvers.png)
+
+## Esercizio: dividere i dati
+
+Ci si può concentrare sulla regressione logistica per la prima prova di addestramento poiché di recente si è appreso di quest'ultima in una lezione precedente.
+Dividere i dati in gruppi di addestramento e test chiamando `train_test_split()`:
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## Esercizio: applicare la regressione logistica
+
+Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_ utilizzare e quale _solutore_ impostare. Usare LogisticRegression con un'impostazione multiclasse e il solutore **liblinear** da addestrare.
+
+1. Creare una regressione logistica con multi_class impostato su `ovr` e il risolutore impostato su `liblinear`:
+
+   ```python
+   lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+   model = lr.fit(X_train, np.ravel(y_train))
+
+   accuracy = model.score(X_test, y_test)
+   print ("Accuracy is {}".format(accuracy))
+   ```
+
+   ✅ Provare un risolutore diverso come `lbfgs`, che è spesso impostato come predefinito
+
+   > Nota, usare la funzione [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) di Pandas per appiattire i dati quando necessario.
+
+   La precisione è buona oltre l'**80%**!
+
+1. Si può vedere questo modello in azione testando una riga di dati (#50):
+
+   ```python
+   print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+   print(f'cuisine: {y_test.iloc[50]}')
+   ```
+
+   Il risultato viene stampato:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   ✅ Provare un numero di riga diverso e controllare i risultati
+
+1. Scavando più a fondo, si può verificare l'accuratezza di questa previsione:
+
+   ```python
+   test= X_test.iloc[50].values.reshape(-1, 1).T
+   proba = model.predict_proba(test)
+   classes = model.classes_
+   resultdf = pd.DataFrame(data=proba, columns=classes)
+
+   topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+   topPrediction.head()
+   ```
+
+   Il risultato è stampato: la cucina indiana è la sua ipotesi migliore, con buone probabilità:
+
+   |            |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | ---------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+   |    indiano | 0,715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |     cinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Giapponese | 0,029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |    Coreano | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |       thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+
+   ✅ Si è in grado di spiegare perché il modello è abbastanza sicuro che questa sia una cucina indiana?
+
+1. Ottenere maggiori dettagli stampando un rapporto di classificazione, come fatto nelle lezioni di regressione:
+
+   ```python
+   y_pred = model.predict(X_test)
+   print(classification_report(y_test,y_pred))
+   ```
+
+   | precisione      | recall | punteggio f1 | supporto |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | --------------- | ------ | ------------ | -------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+   | cinese          | 0,73   | 0,71         | 0,72     | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | indiano         | 0,91   | 0,93         | 0,92     | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Giapponese      | 0.70   | 0,75         | 0,72     | 220  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Coreano         | 0,86   | 0,76         | 0,81     | 242  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | thai            | 0,79   | 0,85         | 0.82     | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | accuratezza     | 0,80   | 1199         |          |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | macro media     | 0,80   | 0,80         | 0,80     | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Media ponderata | 0,80   | 0,80         | 0,80     | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+
+## 🚀 Sfida
+
+In questa lezione, sono stati utilizzati dati puliti per creare un modello di apprendimento automatico in grado di prevedere una cucina nazionale basata su una serie di ingredienti. Si prenda del tempo per leggere le numerose opzioni fornite da Scikit-learn per classificare i dati. Approfondire il concetto di "risolutore" per capire cosa succede dietro le quinte.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=it)
+## Revisione e Auto Apprendimento
+
+Approfondire un po' la matematica alla base della regressione logistica in [questa lezione](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
+## Compito
+
+[Studiare i risolutori](assignment.it.md)
diff --git a/4-Classification/2-Classifiers-1/translations/README.ko.md b/4-Classification/2-Classifiers-1/translations/README.ko.md
new file mode 100644
index 000000000..e2c5c9597
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/README.ko.md
@@ -0,0 +1,243 @@
+# 요리 classifiers 1
+
+이 강의에서는, 요리에 대하여 균형적이고, 깔끔한 데이터로 채운 저번 강의에서 저장했던 데이터셋을 사용합니다.
+
+다양한 classifiers와 데이터셋을 사용해서 _재료 그룹 기반으로 주어진 국민 요리를 예측_ 합니다. 이러는 동안, classification 작업에 알고리즘을 활용할 몇 방식에 대해 자세히 배워볼 예정입니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+
+## 준비하기
+
+[Lesson 1](../../1-Introduction/README.md)을 완료했다고 가정하고, 4가지 강의의 최상단 `/data` 폴더에서 _cleaned_cuisines.csv_ 파일이 있는지 확인합니다.
+
+## 연습 - 국민 요리 예측하기
+
+1. 강의의 _notebook.ipynb_ 폴더에서 작업하고, Pandas 라이브러리와 이 파일을 가져옵니다:
+
+    ```python
+    import pandas as pd
+    cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv")
+    cuisines_df.head()
+    ```
+
+    데이터는 이렇게 보입니다:
+
+
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+
+
+1. 지금부터, 여러가지 라이브러리를 가져옵니다:
+
+    ```python
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    from sklearn.svm import SVC
+    import numpy as np
+    ```
+
+1. 훈련을 위한 2가지 데이터프레임으로 X 와 y 좌표를 나눕니다. `cuisine`은 라벨 프레임일 수 있습니다:
+
+    ```python
+    cuisines_label_df = cuisines_df['cuisine']
+    cuisines_label_df.head()
+    ```
+
+    이렇게 보일 예정입니다:
+
+    ```output
+    0    indian
+    1    indian
+    2    indian
+    3    indian
+    4    indian
+    Name: cuisine, dtype: object
+    ```
+
+1. `drop()`을 불러서 `Unnamed: 0` 열과 `cuisine` 열을 드랍합니다. 훈련 가능한 features로 남긴 데이터를 저장합니다:
+
+    ```python
+    cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+    cuisines_feature_df.head()
+    ```
+
+    features는 이렇게 보입니다:
+
+|      | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+|    0 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    1 |      1 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    2 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    3 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    4 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      1 |        0 | 0 |
+
+지금부터 모델을 훈련할 준비가 되었습니다!
+
+## classifier 고르기
+
+이제 데이터를 정리하고 훈련할 준비가 되었으므로, 작업에 사용할 알고리즘을 정해야 합니다.
+
+Scikit-learn은 Supervised Learning 아래에 classification 그룹으로 묶여있고, 이 카테고리에서 다양한 분류 방식을 찾을 수 있습니다. [The variety](https://scikit-learn.org/stable/supervised_learning.html)는 처음에 꽤 당황스럽습니다. 다음 방식에 모든 classification 기술이 포함되어 있습니다:
+
+- Linear 모델
+- Support Vector Machines
+- Stochastic Gradient Descent
+- Nearest Neighbors
+- Gaussian Processes
+- Decision Trees
+- Ensemble methods (voting Classifier)
+- Multiclass 와 multioutput algorithms (multiclass 와 multilabel classification, multiclass-multioutput classification)
+
+> [neural networks to classify data](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification)를 사용할 수 있지만, 이 강의의 범위를 벗어납니다.
+
+### 어떠한 classifier 사용하나요?
+
+그래서, 어떤 classifier를 골라야 하나요? 자주, 여러가지로 실행하며 좋은 결과를 보는 게 테스트 방식입니다. Scikit-learn은 KNeighbors, SVC 두 방식으로 GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB 그리고 QuadraticDiscrinationAnalysis 와 비교하여 만든 데이터셋에 대한 [side-by-side comparison](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)을 제공하고, 시각화된 결과를 보입니다:
+
+![comparison of classifiers](../images/comparison.png)
+> Plots generated on Scikit-learn's documentation
+
+> AutoML은 클라우드에서 comparisons을 실행해서 이러한 문제를 깔끔하게 해결했으며, 데이터에 적당한 알고리즘을 고를 수 있습니다. [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)에서 시도해봅니다.
+
+### 더 괜찮은 접근법
+
+그러나, 성급히 추측하기보다 더 괜찮은 방식으로, 내려받을 수 있는 [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)의 아이디어를 따르는 것입니다. 여기, multiclass 문제에 대하여, 몇 선택 사항을 볼 수 있습니다:
+
+![cheatsheet for multiclass problems](../images/cheatsheet.png)
+> multiclass classification 옵션을 잘 설명하는, Microsoft의 알고리즘 치트 시트의 섹션
+
+✅ 치트 시트를 내려받고, 출력해서, 벽에 겁니다!
+
+### 추리하기
+
+만약 주어진 제약 사항을 감안해서 다른 접근 방식으로 추론할 수 있는지 봅니다:
+
+- **Neural networks 매우 무겁습니다**. 깔끔하지만, 최소 데이터셋과, 노트북으로 로컬에서 훈련했다는 사실을 보면, 이 작업에서 neural networks는 매우 무겁습니다.
+- **two-class classifier 아닙니다**. one-vs-all를 빼기 위해서, two-class classifier를 사용하지 않습니다.
+- **Decision tree 또는 logistic regression 작동할 수 있습니다**. decision tree 또는, multiclass를 위한 logistic regression이 작동할 수 있습니다.
+- **Multiclass Boosted Decision Trees 다른 문제를 해결합니다**. multiclass boosted decision tree는 nonparametric 작업에 가장 적당합니다. 예시로. 랭킹을 만드려고 디자인 했으므로, 유용하지 않습니다.
+
+### Scikit-learn 사용하기
+
+Scikit-learn으로 데이터를 분석할 예정입니다. 그러나, Scikit-learn에는 logistic regression을 사용할 많은 방식이 존재합니다. [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)를 찾아봅니다.
+
+기본적으로 Scikit-learn에서 logistic regression을 하도록 요청할 때 지정할 필요가 있는, `multi_class` 와 `solver` 중요한 두 개의 파라미터가 있습니다. `multi_class` 값은 특정 동작을 적용합니다. solver의 값은 사용할 알고리즘입니다. 모든 solver가 모든 `multi_class` 값들을 연결하지 않습니다.
+
+문서에 따르면, multiclass 케이스인 경우, 훈련 알고리즘은 아래와 같습니다:
+
+- **one-vs-rest (OvR) 스키마를 사용합니다**, `multi_class` 옵션을 `ovr`로 한 경우
+- **cross-entropy loss를 사용합니다**, `multi_class` 옵션을 `multinomial`로 한 경우. (현재 `multinomial` 옵션은 ‘lbfgs’, ‘sag’, ‘saga’ 그리고 ‘newton-cg’ solvers에서 지원됩니다.)"
+
+> 🎓 'scheme'는 여기에서 'ovr' (one-vs-rest) 혹은 'multinomial'일 것입니다. logistic regression은 binary classification을 잘 지원할 수 있도록 디자인 되었으므로, 스키마를 사용하면 multiclass classification 작업을 잘 핸들링할 수 있습니다. [source](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> 🎓 'solver'는 "the algorithm to use in the optimization problem"로 정의됩니다. [source](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Scikit-learn은 solvers가 다양한 데이터 구조에 제시된 다른 문제 방식을 설명하고자 이 표를 제공합니다:
+
+![solvers](../images/solvers.png)
+
+## 연습 - 데이터 나누기
+
+지난 강의에서 최근에 배웠으므로 첫 훈련 시도에 대한 logistic regression으로 집중할 수 있습니다. 
+`train_test_split()` 불러서 데이터를 훈련과 테스트 그룹으로 나눕니다:
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## 연습 - logistic regression 적용하기
+
+multiclass 케이스로, 사용할 _scheme_ 와 설정할 _solver_ 를 선택해야 합니다. 훈련할 multiclass 세팅과 **liblinear** solver와 함께 LogisticRegression을 사용합니다.
+
+1. multi_class를 `ovr`로 설정하고 solver도 `liblinear`로 설정해서 logistic regression을 만듭니다:
+
+    ```python
+    lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+    model = lr.fit(X_train, np.ravel(y_train))
+    
+    accuracy = model.score(X_test, y_test)
+    print ("Accuracy is {}".format(accuracy))
+    ```
+
+    ✅ 가끔 기본적으로 설정되는, `lbfgs`처럼 다른 solver를 시도합니다
+
+    > 노트, Pandas [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 함수를 사용해서 필요한 순간에 데이터를 평평하게 핍니다.
+
+    정확도는 **80%** 보다 좋습니다!
+
+1. 하나의 행 데이터 (#50)를 테스트하면 모델이 작동하는 것을 볼 수 있습니다:
+
+    ```python
+    print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+    print(f'cuisine: {y_test.iloc[50]}')
+    ```
+
+    결과가 출력됩니다:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   ✅ 다른 행 번호로 시도해보고 결과를 확인합니다.
+
+1. 깊게 파보면, 예측 정확도를 확인할 수 있습니다:
+
+    ```python
+    test= X_test.iloc[50].values.reshape(-1, 1).T
+    proba = model.predict_proba(test)
+    classes = model.classes_
+    resultdf = pd.DataFrame(data=proba, columns=classes)
+    
+    topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+    topPrediction.head()
+    ```
+
+    결과가 출력됩니다 - 인도 요리가 가장 좋은 확률에 최선으로 추측됩니다.
+
+    |          |        0 |
+    | -------: | -------: |
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 |
+    |     thai | 0.007634 |
+
+    ✅ 모델이 이를 인도 요리라고 확신하는 이유를 설명할 수 있나요?
+
+1. regression 강의에서 했던 행동처럼, classification 리포트를 출력해서 자세한 정보를 얻습니다:
+
+    ```python
+    y_pred = model.predict(X_test)
+    print(classification_report(y_test,y_pred))
+    ```
+
+    |              | precision | recall | f1-score | support |
+    | ------------ | --------- | ------ | -------- | ------- |
+    | chinese      | 0.73      | 0.71   | 0.72     | 229     |
+    | indian       | 0.91      | 0.93   | 0.92     | 254     |
+    | japanese     | 0.70      | 0.75   | 0.72     | 220     |
+    | korean       | 0.86      | 0.76   | 0.81     | 242     |
+    | thai         | 0.79      | 0.85   | 0.82     | 254     |
+    | accuracy     | 0.80      | 1199   |          |         |
+    | macro avg    | 0.80      | 0.80   | 0.80     | 1199    |
+    | weighted avg | 0.80      | 0.80   | 0.80     | 1199    |
+
+## 🚀 도전
+
+이 강의에서, 정리된 데이터로 재료의 시리즈를 기반으로 국민 요리를 예측할 수 있는 머신러닝 모델을 만들었습니다. 시간을 투자해서 Scikit-learn이 데이터를 분류하기 위해 제공하는 다양한 옵션을 읽어봅니다. 무대 뒤에서 생기는 일을 이해하기 위해서 'solver'의 개념을 깊게 파봅니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
+## 검토 & 자기주도 학습
+
+[this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)에서 logistic regression 뒤의 수학에 대해서 더 자세히 파봅니다.
+
+## 과제 
+
+[Study the solvers](../assignment.md)
diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md
new file mode 100644
index 000000000..30f36b133
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/README.tr.md
@@ -0,0 +1,241 @@
+# Mutfak sınıflandırıcıları 1
+
+Bu derste, mutfaklarla ilgili dengeli ve temiz veriyle dolu, geçen dersten kaydettiğiniz veri setini kullanacaksınız.
+
+Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak verilen bir ulusal mutfağı öngörmek_ için kullanacaksınız. Bunu yaparken, sınıflandırma görevleri için algoritmaların leveraj edilebileceği yollardan bazıları hakkında daha fazla bilgi edineceksiniz.
+
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=tr)
+# Hazırlık
+
+[Birinci dersi](../../1-Introduction/README.md) tamamladığınızı varsayıyoruz, dolayısıyla bu dört ders için _cleaned_cuisines.csv_ dosyasının kök `/data` klasöründe var olduğundan emin olun.
+
+## Alıştırma - ulusal bir mutfağı öngörün
+
+1. Bu dersin _notebook.ipynb_ dosyasında çalışarak, Pandas kütüphanesiyle beraber o dosyayı da alın:
+
+    ```python
+    import pandas as pd
+    cuisines_df = pd.read_csv("../data/cleaned_cuisines.csv")
+    cuisines_df.head()
+    ```
+
+    Veri şöyle görünüyor:
+
+|     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+| --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+| 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+  
+
+1. Şimdi, birkaç kütüphane daha alın:
+
+    ```python
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    from sklearn.svm import SVC
+    import numpy as np
+    ```
+
+1. X ve y koordinatlarını eğitme için iki veri iskeletine bölün. `cuisine` etiket veri iskeleti olabilir:
+
+    ```python
+    cuisines_label_df = cuisines_df['cuisine']
+    cuisines_label_df.head()
+    ```
+
+    Şöyle görünecek:
+
+    ```output
+    0    indian
+    1    indian
+    2    indian
+    3    indian
+    4    indian
+    Name: cuisine, dtype: object
+    ```
+
+1. `Unnamed: 0` ve `cuisine` sütunlarını, `drop()` fonksiyonunu çağırarak temizleyin. Kalan veriyi eğitilebilir öznitelikler olarak kaydedin:
+
+    ```python
+    cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+    cuisines_feature_df.head()
+    ```
+
+    Öznitelikleriniz şöyle görünüyor:
+
+| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0 |
+
+Şimdi modelinizi eğitmek için hazırsınız!
+
+## Sınıflandırıcınızı seçme
+
+Veriniz temiz ve eğitme için hazır, şimdi bu iş için hangi algoritmanın kullanılması gerektiğine karar vermelisiniz.
+
+Scikit-learn, sınıflandırmayı gözetimli öğrenme altında grupluyor. Bu kategoride sınıflandırma için birçok yöntem görebilirsiniz. [Çeşitlilik](https://scikit-learn.org/stable/supervised_learning.html) ilk bakışta oldukça şaşırtıcı. Aşağıdaki yöntemlerin hepsi sınıflandırma yöntemlerini içermektedir:
+
+- Doğrusal Modeller
+- Destek Vektör Makineleri
+- Stokastik Gradyan İnişi
+- En Yakın Komşu
+- Gauss Süreçleri
+- Karar Ağaçları
+- Topluluk Metotları (Oylama Sınıflandırıcısı)
+- Çok sınıflı ve çok çıktılı algoritmalar (çok sınıflı ve çok etiketli sınıflandırma, çok sınıflı-çok çıktılı sınıflandırma)
+
+> [Verileri sınıflandırmak için sinir ağlarını](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification) da kullanabilirsiniz, ancak bu, bu dersin kapsamı dışındadır.
+
+### Hangi sınıflandırıcıyı kullanmalı?
+
+Şimdi, hangi sınıflandırıcıyı seçmelisiniz? Genellikle, birçoğunu gözden geçirmek ve iyi bir sonuç aramak deneme yollarından biridir. Scikit-learn, oluşturulmuş bir veri seti üzerinde KNeighbors, iki yolla SVC, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB ve QuadraticDiscrinationAnalysis karşılaştırmaları yapan ve sonuçları görsel olarak gösteren bir [yan yana karşılaştırma](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) sunar:
+
+![sınıflandırıcıların karşılaştırılması](../images/comparison.png)
+> Grafikler Scikit-learn dokümantasyonlarında oluşturulmuştur.
+
+> AutoML, bu karşılaştırmaları bulutta çalıştırarak bu problemi muntazam bir şekilde çözer ve veriniz için en iyi algoritmayı seçmenizi sağlar. [Buradan](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) deneyin.
+
+### Daha iyi bir yaklaşım
+
+Böyle tahminlerle çözmekten daha iyi bir yol ise, indirilebilir [ML Kopya kağıdı](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa) içindeki fikirlere bakmaktır. Burada, bizim çok sınıflı problemimiz için bazı seçenekler olduğunu görüyoruz:
+
+![çok sınıflı problemler için kopya kağıdı](../images/cheatsheet.png)
+> Microsoft'un Algoritma Kopya Kağıdı'ndan, çok sınıflı sınıflandırma seçeneklerini detaylandıran bir bölüm
+
+:white_check_mark: Bu kopya kağıdını indirin, yazdırın ve duvarınıza asın!
+
+### Akıl yürütme
+
+Elimizdeki kısıtlamalarla farklı yaklaşımlar üzerine akıl yürütelim:
+
+- **Sinir ağları çok ağır**. Temiz ama minimal veri setimizi ve eğitimi not defterleriyle yerel makinelerde çalıştırdığımızı göz önünde bulundurursak, sinir ağları bu görev için çok ağır oluyor.
+- **İki sınıflı sınıflandırıcısı yok**. İki sınıflı sınıflandırıcı kullanmıyoruz, dolayısıyla bire karşı hepsi (one-vs-all) yöntemi eleniyor.
+- **Karar ağacı veya lojistik regresyon işe yarayabilirdi**. Bir karar ağacı veya çok sınıflı veri için lojistik regresyon işe yarayabilir.
+- **Çok Sınıf Artırmalı Karar Ağaçları farklı bir problemi çözüyor**. Çok sınıf artırmalı karar ağacı, parametrik olmayan görevler için en uygunu, mesela sıralama (ranking) oluşturmak için tasarlanan görevler. Yani, bizim için kullanışlı değil.
+
+### Scikit-learn kullanımı
+
+Verimizi analiz etmek için Scikit-learn kullanacağız. Ancak, Scikit-learn içerisinde lojistik regresyonu kullanmanın birçok yolu var. [Geçirilecek parametreler](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression) göz atın.
+
+Aslında, Scikit-learn'den lojistik regresyon yapmasını beklediğimizde belirtmemiz gereken `multi_class` ve `solver` diye iki önemli parametre var. `multi_class` değeri belli bir davranış uygular. Çözücünün değeri, hangi algoritmanın kullanılacağını gösterir. Her çözücü her `multi_class` değeriyle eşleştirilemez.
+
+Dokümanlara göre, çok sınıflı durumunda eğitme algoritması:
+
+- Eğer `multi_class` seçeneği `ovr` olarak ayarlanmışsa, **bire karşı diğerleri (one-vs-rest, OvR) şemasını kullanır**
+- Eğer `multi_class` seçeneği `multinomial` olarak ayarlanmışsa, **çapraz düzensizlik yitimini/kaybını kullanır**. (Güncel olarak `multinomial` seçeneği yalnızca ‘lbfgs’, ‘sag’, ‘saga’ ve ‘newton-cg’ çözücüleriyle destekleniyor.)
+
+> :mortar_board: Buradaki 'şema' ya 'ovr' (one-vs-rest, yani bire karşı diğerleri) ya da 'multinomial' olabilir. Lojistik regresyon aslında ikili sınıflandırmayı desteklemek için tasarlandığından, bu şemalar onun çok sınıflı sınıflandırma görevlerini daha iyi ele alabilmesini sağlıyor. [kaynak](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> :mortar_board: 'Çözücü', "eniyileştirme probleminde kullanılacak algoritma" olarak tanımlanır. [kaynak](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)
+
+Scikit-learn, çözücülerin, farklı tür veri yapıları tarafından sunulan farklı meydan okumaları nasıl ele aldığını açıklamak için bu tabloyu sunar:
+
+![çözücüler](../images/solvers.png)
+
+## Alıştırma - veriyi bölün
+
+İkincisini önceki derte öğrendiğinizden, ilk eğitme denememiz için lojistik regresyona odaklanabiliriz.
+`train_test_split()` fonksiyonunu çağırarak verilerinizi eğitme ve sınama gruplarına bölün:
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## Alıştırma - lojistik regresyon uygulayın
+
+Çok sınıflı durumu kullandığınız için, hangi _şemayı_ kullanacağınızı ve hangi _çözücüyü_ ayarlayacağınızı seçmeniz gerekiyor. Eğitme için, bir çok sınıflı ayarında LogisticRegression ve **liblinear** çözücüsünü kullanın.
+
+1. multi_class'ı `ovr` ve solver'ı `liblinear` olarak ayarlayarak bir lojistik regresyon oluşturun:
+
+    ```python
+    lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+    model = lr.fit(X_train, np.ravel(y_train))
+    
+    accuracy = model.score(X_test, y_test)
+    print ("Accuracy is {}".format(accuracy))
+    ```
+
+    :white_check_mark: Genelde varsayılan olarak ayarlanan `lbfgs` gibi farklı bir çözücü deneyin.
+
+    > Not olarak, gerektiğinde verinizi düzleştirmek için Pandas [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) fonksiyonunu kullanın.
+
+    Doğruluk **%80** üzerinde iyidir!
+
+1. Bir satır veriyi (#50) sınayarak bu modeli eylem halinde görebilirsiniz:
+
+    ```python
+    print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+    print(f'cuisine: {y_test.iloc[50]}')
+    ```
+
+    Sonuç bastırılır:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   :white_check_mark: Farklı bir satır sayısı deneyin ve sonuçları kontrol edin
+
+1. Daha derinlemesine inceleyerek, bu öngörünün doğruluğunu kontrol edebilirsiniz:
+
+    ```python
+    test= X_test.iloc[50].values.reshape(-1, 1).T
+    proba = model.predict_proba(test)
+    classes = model.classes_
+    resultdf = pd.DataFrame(data=proba, columns=classes)
+    
+    topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+    topPrediction.head()
+    ```
+
+    Sonuç bastırılır - Hint mutfağı iyi olasılıkla en iyi öngörü:
+
+    |          |        0 |
+    | -------: | -------: |
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 |
+    |     thai | 0.007634 |
+
+    :while_check_mark: Modelin, bunun bir Hint mutfağı olduğundan nasıl emin olduğunu açıklayabilir misiniz?
+
+1. Regresyon derslerinde yaptığınız gibi, bir sınıflandırma raporu bastırarak daha fazla detay elde edin:
+
+    ```python
+    y_pred = model.predict(X_test)
+    print(classification_report(y_test,y_pred))
+    ```
+
+    |              | precision | recall | f1-score | support |
+    | ------------ | --------- | ------ | -------- | ------- |
+    | chinese      | 0.73      | 0.71   | 0.72     | 229     |
+    | indian       | 0.91      | 0.93   | 0.92     | 254     |
+    | japanese     | 0.70      | 0.75   | 0.72     | 220     |
+    | korean       | 0.86      | 0.76   | 0.81     | 242     |
+    | thai         | 0.79      | 0.85   | 0.82     | 254     |
+    | accuracy     | 0.80      | 1199   |          |         |
+    | macro avg    | 0.80      | 0.80   | 0.80     | 1199    |
+    | weighted avg | 0.80      | 0.80   | 0.80     | 1199    |
+
+## :rocket: Meydan Okuma
+
+Bu derste, bir grup malzemeyi baz alarak bir ulusal mutfağı öngörebilen bir makine öğrenimi modeli oluşturmak için temiz verinizi kullandınız. Scikit-learn'ün veri sınıflandırmak için sağladığı birçok yöntemi okumak için biraz vakit ayırın. Arka tarafta neler olduğunu anlamak için 'çözücü' kavramını derinlemesine inceleyin.
+
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=tr)
+
+## Gözden geçirme & kendi kendine çalışma
+
+[Bu deste](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf) lojistik regresyonun arkasındaki matematiği derinlemesine inceleyin.
+## Ödev
+
+[Çözücüleri çalışın](assignment.tr.md)
diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
new file mode 100644
index 000000000..7bf2bb601
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -0,0 +1,243 @@
+# 菜品分类器 1
+
+本节课程将使用你在上一个课程中所保存的全部经过均衡和清洗的菜品数据。
+
+你将使用此数据集和各种分类器，_根据一组配料预测这是哪一国家的美食_。在此过程中，你将学到更多用来权衡分类任务算法的方法  
+
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+
+# 准备工作
+
+假如你已经完成了[课程 1](../../1-Introduction/translations/README.zh-cn.md), 确保在根目录的 `/data` 文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。
+
+## 练习 - 预测某国的菜品
+
+1. 在本节课的 _notebook.ipynb_ 文件中，导入 Pandas，并读取相应的数据文件：
+
+    ```python
+    import pandas as pd
+    cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+    cuisines_df.head()
+    ```
+
+    数据如下所示:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. 现在，再多导入一些库：
+
+    ```python
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    from sklearn.svm import SVC
+    import numpy as np
+    ```
+
+1. 接下来需要将数据分为训练模型所需的 X（译者注：代表特征数据）和 y（译者注：代表标签数据）两个 dataframe。首先可将 `cuisine` 列的数据单独保存为的一个 dataframe 作为标签（label）。
+
+    ```python
+    cuisines_label_df = cuisines_df['cuisine']
+    cuisines_label_df.head()
+    ```
+
+    输出如下:
+
+    ```output
+    0    indian
+    1    indian
+    2    indian
+    3    indian
+    4    indian
+    Name: cuisine, dtype: object
+    ```
+
+1. 调用 `drop()` 方法将 `Unnamed: 0` 和 `cuisine` 列删除，并将余下的数据作为可以用于训练的特证（feature）数据:
+
+    ```python
+    cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+    cuisines_feature_df.head()
+    ```
+
+    你的特征集看上去将会是这样:
+
+    |      | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+    | ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------- |
+    |    0 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    1 |      1 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    2 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    3 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    4 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      1 | 0        |
+
+现在，你已经准备好可以开始训练你的模型了！
+
+## 选择你的分类器
+
+你的数据已经清洗干净并已经准备好可以进行训练了，现在需要决定你想要使用的算法来完成这项任务。
+
+Scikit_learn 将分类任务归在了监督学习类别中，在这个类别中你可以找到很多可以用来分类的方法。乍一看上去，有点[琳琅满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些算法都可以用于分类：
+
+- 线性模型（Linear Models）
+- 支持向量机（Support Vector Machines）
+- 随机梯度下降（Stochastic Gradient Descent）
+- 最近邻（Nearest Neighbors）
+- 高斯过程（Gaussian Processes）
+- 决策树（Decision Trees）
+- 集成方法（投票分类器）（Ensemble methods（voting classifier）） 
+- 多类别多输出算法（多类别多标签分类，多类别多输出分类）（Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification)）
+
+> 你也可以使用[神经网络来分类数据](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), 但这对于本课程来说有点超纲了。
+
+### 如何选择分类器?
+
+那么，你应该如何从中选择分类器呢？一般来说，可以选择多个分类器并对比他们的运行结果。Scikit-learn 提供了各种算法（包括 KNeighbors、 SVC two ways、 GaussianProcessClassifier、 DecisionTreeClassifier、 RandomForestClassifier、 MLPClassifier、 AdaBoostClassifier、 GaussianNB 以及 QuadraticDiscrinationAnalysis）的[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将结果进行了可视化的展示：
+
+![各分类器比较](../images/comparison.png)
+> 图表来源于 Scikit-learn 的官方文档
+
+> AutoML 通过在云端运行这些算法并进行了对比，非常巧妙地解决的算法选择的问题，能帮助你根据数据集的特点来选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。
+
+### 另外一种效果更佳的分类器选择方法
+
+比起无脑地猜测，你可以下载这份[机器学习速查表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)。这里面将各算法进行了比较，能更有效地帮助我们选择算法。根据这份速查表，我们可以找到要完成本课程中涉及的多类型的分类任务，可以有以下这些选择：
+
+![多类型问题速查表](../images/cheatsheet.png)
+> 微软算法小抄中部分关于多类型分类任务可选算法
+
+✅ 下载这份小抄，并打印出来，挂在你的墙上吧！
+
+### 选择的流程
+
+让我们根据所有限制条件依次对各种算法的可行性进行判断：
+
+- **神经网络（Neural Network）太过复杂了**。我们的数据很清晰但数据量比较小，此外我们是通过 notebook 在本地进行训练的，神经网络对于这个任务来说过于复杂了。
+- **二分类法（two-class classifier）是不可行的**。我们不能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 
+- **可以选择决策树以及逻辑回归算法**。决策树应该是可行的，此外也可以使用逻辑回归来处理多类型数据。
+- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适合的是非参数化的任务，即任务目标是建立一个排序，这对我们当前的任务并没有作用。
+
+### 使用 Scikit-learn 
+
+我们将会使用 Scikit-learn 来对我们的数据进行分析。然而在 Scikit-learn 中使用逻辑回归也有很多方法。可以先了解一下逻辑回归算法需要[传递的参数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。
+
+当我们需要 Scikit-learn 进行逻辑回归运算时，`multi_class` 以及 `solver`是最重要的两个参数，因此我们需要特别说明一下。 `multi_class` 是分类方式选择参数，而`solver`优化算法选择参数。值得注意的是，并不是所有的 solvers 都可以与`multi_class`参数进行匹配的。
+
+根据官方文档，在多类型分类问题中:
+
+- 当 `multi_class` 被设置为 `ovr` 时，将使用 **“一对其余”(OvR)策略（scheme）**。
+- 当 `multi_class` 被设置为 `multinomial` 时，则使用的是**交叉熵损失（cross entropy loss）** 作为损失函数。(注意，目前`multinomial`只支持‘lbfgs’, ‘sag’, ‘saga’以及‘newton-cg’等 solver 作为损失函数的优化方法)
+
+> 🎓 在本课程的任务中“scheme”可以是“ovr(one-vs-rest)”也可以是“multinomial”。因为逻辑回归本来是设计来用于进行二分类任务的，这两个 scheme 参数的选择都可以使得逻辑回归很好的完成多类型分类任务。[来源](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> 🎓 “solver”被定义为是"用于解决优化问题的算法"。[来源](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Scikit-learn提供了以下这个表格来解释各种solver是如何应对的不同的数据结构所带来的不同的挑战的:
+
+![solvers](../images/solvers.png)
+
+## 练习 - 分割数据
+
+因为你刚刚在上一节课中学习了逻辑回归，我们这里就通过逻辑回归算法，来演练一下如何进行你的第一个机器学习模型的训练。首先，需要通过调用`train_test_split()`方法可以把你的数据分割成训练集和测试集：
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## 练习 - 调用逻辑回归算法
+
+接下来，你需要决定选用什么 _scheme_ 以及 _solver_ 来进行我们这个多类型分类的案例。在这里我们使用 LogisticRegression 方法，并设置相应的 multi_class 参数，同时将 solver 设置为 **liblinear** 来进行模型训练。
+
+1. 创建一个逻辑回归模型，并将 multi_class 设置为 `ovr`，同时将 solver 设置为 `liblinear`:
+
+    ```python
+    lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+    model = lr.fit(X_train, np.ravel(y_train))
+    
+    accuracy = model.score(X_test, y_test)
+    print ("Accuracy is {}".format(accuracy))
+    ```
+
+    ✅ 也可以试试其他 solver 比如 `lbfgs`, 这也是默认参数
+
+    > 注意, 使用 Pandas 的 [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 方法可以在需要的时候将你的数据进行降维
+
+    运算之后，可以看到准确率高达 **80%**!
+
+1. 你也可以通过查看某一行数据（比如第 50 行）来观测到模型运行的情况:
+
+    ```python
+    print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+    print(f'cuisine: {y_test.iloc[50]}')
+    ```
+
+    运行后的输出如下:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   ✅ 试试不同的行索引来检查一下计算的结果吧
+
+1. 我们可以再进行一部深入的研究，检查一下本轮预测结果的准确率:
+
+    ```python
+    test= X_test.iloc[50].values.reshape(-1, 1).T
+    proba = model.predict_proba(test)
+    classes = model.classes_
+    resultdf = pd.DataFrame(data=proba, columns=classes)
+    
+    topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+    topPrediction.head()
+    ```
+
+    运行后的输出如下———可以发现这是一道印度菜的可能性最大，是最合理的猜测:
+
+    |          |        0 |
+    | -------: | -------: |
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 |
+    |     thai | 0.007634 |
+
+    ✅ 你能解释下为什么模型会如此确定这是一道印度菜么？
+
+1. 和你在之前的回归的课程中所做的一样，我们也可以通过输出分类的报告得到关于模型的更多的细节：
+
+    ```python
+    y_pred = model.predict(X_test)
+    print(classification_report(y_test,y_pred))
+    ```
+
+    | precision    | recall | f1-score | support |      |
+    | ------------ | ------ | -------- | ------- | ---- |
+    | chinese      | 0.73   | 0.71     | 0.72    | 229  |
+    | indian       | 0.91   | 0.93     | 0.92    | 254  |
+    | japanese     | 0.70   | 0.75     | 0.72    | 220  |
+    | korean       | 0.86   | 0.76     | 0.81    | 242  |
+    | thai         | 0.79   | 0.85     | 0.82    | 254  |
+    | accuracy     | 0.80   | 1199     |         |      |
+    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |
+    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |
+
+## 挑战
+
+在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，这个模型能够根据输入的一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下 Scikit-learn 所提供的关于可以用来分类数据的其他方法的资料。此外，你也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。
+
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
+
+## 回顾与自学
+
+[这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背后的数学原理进行更加深入的讲解
+
+## 作业 
+
+[学习 solver](assignment.md)
diff --git a/4-Classification/2-Classifiers-1/translations/assignment.it.md b/4-Classification/2-Classifiers-1/translations/assignment.it.md
new file mode 100644
index 000000000..80d1c5e16
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/assignment.it.md
@@ -0,0 +1,10 @@
+# Studiare i risolutori
+## Istruzioni
+
+In questa lezione si è imparato a conoscere i vari risolutori che associano algoritmi a un processo di machine learning per creare un modello accurato. Esaminare i risolutori elencati nella lezione e sceglierne due. Con parole proprie, confrontare questi due risolutori. Che tipo di problema affrontano? Come funzionano con varie strutture di dati? Perché se ne dovrebbe sceglierne uno piuttosto che un altro?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------ | ---------------------------- |
+|          | Viene presentato un file .doc con due paragrafi, uno su ciascun risolutore, confrontandoli attentamente. | Un file .doc viene presentato con un solo paragrafo | Il compito è incompleto |
diff --git a/4-Classification/2-Classifiers-1/translations/assignment.tr.md b/4-Classification/2-Classifiers-1/translations/assignment.tr.md
new file mode 100644
index 000000000..10d4c64f3
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/assignment.tr.md
@@ -0,0 +1,9 @@
+# Çözücüleri çalışın
+## Yönergeler
+
+Bu derste, doğru bir model yaratmak için algoritmaları bir makine öğrenimi süreciyle eşleştiren çeşitli çözücüleri öğrendiniz. Derste sıralanan çözücüleri inceleyin ve iki tanesini seçin. Kendi cümlelerinizle, bu iki çözücünün benzerliklerini ve farklılıklarını bulup yazın. Ne tür problemleri ele alıyorlar? Çeşitli veri yapılarıyla nasıl çalışıyorlar? Birini diğerine neden tercih ederdiniz?
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                                                                             | Yeterli                                          | Geliştirme Gerekli           |
+| -------- | -------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ | ---------------------------- |
+|          | Her biri bir çözücü üzerine yazılmış, onları dikkatle karşılaştıran ve iki paragraf içeren bir .doc dosyası sunulmuş | Bir paragraf içeren bir .doc dosyası sunulmuş    | Görev tamamlanmamış          |
diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index dd25926e1..291b3a751 100644
--- a/4-Classification/3-Classifiers-2/README.md
+++ b/4-Classification/3-Classifiers-2/README.md
@@ -2,11 +2,11 @@
 
 In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
 
 ### Prerequisite
 
-We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisine.csv_ in the root of this 4-lesson folder.
+We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisines.csv_ in the root of this 4-lesson folder.
 
 ### Preparation
 
@@ -224,7 +224,7 @@ This method of Machine Learning "combines the predictions of several base estima
 
 Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/3-Classifiers-2/images/r_learners_sm.jpeg b/4-Classification/3-Classifiers-2/images/r_learners_sm.jpeg
new file mode 100644
index 000000000..ff8d2945d
Binary files /dev/null and b/4-Classification/3-Classifiers-2/images/r_learners_sm.jpeg differ
diff --git a/4-Classification/3-Classifiers-2/images/svm.png b/4-Classification/3-Classifiers-2/images/svm.png
new file mode 100644
index 000000000..f4f042461
Binary files /dev/null and b/4-Classification/3-Classifiers-2/images/svm.png differ
diff --git a/4-Classification/3-Classifiers-2/notebook.ipynb b/4-Classification/3-Classifiers-2/notebook.ipynb
index f4dec474d..4659a7b62 100644
--- a/4-Classification/3-Classifiers-2/notebook.ipynb
+++ b/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },
diff --git a/4-Classification/3-Classifiers-2/solution/Julia/README.md b/4-Classification/3-Classifiers-2/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
new file mode 100644
index 000000000..d1a6fbf2b
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,645 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "lesson_12-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jsFutf_ygqSx"
+      },
+      "source": [
+        "# Build a classification model: Delicious Asian and Indian Cuisines"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HD54bEefgtNO"
+      },
+      "source": [
+        "## Cuisine classifiers 2\n",
+        "\n",
+        "In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.\n",
+        "\n",
+        "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)\n",
+        "\n",
+        "### **Prerequisite**\n",
+        "\n",
+        "We assume that you have completed the previous lessons since we will be carrying forward some concepts we learned before.\n",
+        "\n",
+        "For this lesson, we'll require the following packages:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "-   `themis`: The [themis package](https://themis.tidymodels.org/) provides Extra Recipes Steps for Dealing with Unbalanced Data.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+        "\n",
+        "Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vZ57IuUxgyQt"
+      },
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+        "\n",
+        "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z22M-pj4g07x"
+      },
+      "source": [
+        "Now, let's hit the ground running!\n",
+        "\n",
+        "## **1. A classification map**\n",
+        "\n",
+        "In our [previous lesson](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1), we tried to address the question: how do we choose between multiple models? To a great extent, it depends on the characteristics of the data and the type of problem we want to solve (for instance classification or regression?)\n",
+        "\n",
+        "Previously, we learned about the various options you have when classifying data using Microsoft's cheat sheet. Python's Machine Learning framework, Scikit-learn, offers a similar but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/map.png\"\n",
+        "   width=\"700\"/>\n",
+        "   <figcaption></figcaption>\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u1i3xRIVg7vG"
+      },
+      "source": [
+        "> Tip: [visit this map online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) and click along the path to read documentation.\n",
+        ">\n",
+        "> The [Tidymodels reference site](https://www.tidymodels.org/find/parsnip/#models) also provides an excellent documentation about different types of model.\n",
+        "\n",
+        "### **The plan** 🗺️\n",
+        "\n",
+        "This map is very helpful once you have a clear grasp of your data, as you can 'walk' along its paths to a decision:\n",
+        "\n",
+        "-   We have \\>50 samples\n",
+        "\n",
+        "-   We want to predict a category\n",
+        "\n",
+        "-   We have labeled data\n",
+        "\n",
+        "-   We have fewer than 100K samples\n",
+        "\n",
+        "-   ✨ We can choose a Linear SVC\n",
+        "\n",
+        "-   If that doesn't work, since we have numeric data\n",
+        "\n",
+        "    -   We can try a ✨ KNeighbors Classifier\n",
+        "\n",
+        "        -   If that doesn't work, try ✨ SVC and ✨ Ensemble Classifiers\n",
+        "\n",
+        "This is a very helpful trail to follow. Now, let's get right into it using the [tidymodels](https://www.tidymodels.org/) modelling framework: a consistent and flexible collection of R packages developed to encourage good statistical practice 😊.\n",
+        "\n",
+        "## 2. Split the data and deal with imbalanced data set.\n",
+        "\n",
+        "From our previous lessons, we learnt that there were a set of common ingredients across our cuisines. Also, there was quite an unequal distribution in the number of cuisines.\n",
+        "\n",
+        "We'll deal with these by\n",
+        "\n",
+        "-   Dropping the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.\n",
+        "\n",
+        "-   Use a `recipe` that preprocesses the data to get it ready for modelling by applying an `over-sampling` algorithm.\n",
+        "\n",
+        "We already looked at the above in the previous lesson so this should be a breeze 🥳!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6tj_rN00hClA"
+      },
+      "source": [
+        "# Load the core Tidyverse and Tidymodels packages\n",
+        "library(tidyverse)\n",
+        "library(tidymodels)\n",
+        "\n",
+        "# Load the original cuisines data\n",
+        "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+        "\n",
+        "# Drop id column, rice, garlic and ginger from our original data set\n",
+        "df_select <- df %>% \n",
+        "  select(-c(1, rice, garlic, ginger)) %>%\n",
+        "  # Encode cuisine column as categorical\n",
+        "  mutate(cuisine = factor(cuisine))\n",
+        "\n",
+        "\n",
+        "# Create data split specification\n",
+        "set.seed(2056)\n",
+        "cuisines_split <- initial_split(data = df_select,\n",
+        "                                strata = cuisine,\n",
+        "                                prop = 0.7)\n",
+        "\n",
+        "# Extract the data in each split\n",
+        "cuisines_train <- training(cuisines_split)\n",
+        "cuisines_test <- testing(cuisines_split)\n",
+        "\n",
+        "# Display distribution of cuisines in the training set\n",
+        "cuisines_train %>% \n",
+        "  count(cuisine) %>% \n",
+        "  arrange(desc(n))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zFin5yw3hHb1"
+      },
+      "source": [
+        "### Deal with imbalanced data\n",
+        "\n",
+        "Imbalanced data often has negative effects on the model performance. Many models perform best when the number of observations is equal and, thus, tend to struggle with unbalanced data.\n",
+        "\n",
+        "There are majorly two ways of dealing with imbalanced data sets:\n",
+        "\n",
+        "-   adding observations to the minority class: `Over-sampling` e.g using a SMOTE algorithm which synthetically generates new examples of the minority class using nearest neighbors of these cases.\n",
+        "\n",
+        "-   removing observations from majority class: `Under-sampling`\n",
+        "\n",
+        "In our previous lesson, we demonstrated how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis. In our case, we want to have an equal distribution in the number of our cuisines for our `training set`. Let's get right into it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cRzTnHolhLWd"
+      },
+      "source": [
+        "# Load themis package for dealing with imbalanced data\n",
+        "library(themis)\n",
+        "\n",
+        "# Create a recipe for preprocessing training data\n",
+        "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+        "  step_smote(cuisine) \n",
+        "\n",
+        "# Print recipe\n",
+        "cuisines_recipe"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KxOQ2ORhhO81"
+      },
+      "source": [
+        "Now we are ready to train models 👩‍💻👨‍💻!\n",
+        "\n",
+        "## 3. Beyond multinomial regression models\n",
+        "\n",
+        "In our previous lesson, we looked at multinomial regression models. Let's explore some more flexible models for classification.\n",
+        "\n",
+        "### Support Vector Machines.\n",
+        "\n",
+        "In the context of classification, `Support Vector Machines` is a machine learning technique that tries to find a *hyperplane* that \"best\" separates the classes. Let's look at a simple example:\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/svm.png\"\n",
+        "   width=\"300\"/>\n",
+        "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C4Wsd0vZhXYu"
+      },
+      "source": [
+        "H1~ does not separate the classes. H2~ does, but only with a small margin. H3~ separates them with the maximal margin.\n",
+        "\n",
+        "#### Linear Support Vector Classifier\n",
+        "\n",
+        "Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques. In SVC, the hyperplane is chosen to correctly separate `most` of the training observations, but `may misclassify` a few observations. By allowing some points to be on the wrong side, the SVM becomes more robust to outliers hence better generalization to new data. The parameter that regulates this violation is referred to as `cost` which has a default value of 1 (see `help(\"svm_poly\")`).\n",
+        "\n",
+        "Let's create a linear SVC by setting `degree = 1` in a polynomial SVM model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vJpp6nuChlBz"
+      },
+      "source": [
+        "# Make a linear SVC specification\n",
+        "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+        "  set_engine(\"kernlab\") %>% \n",
+        "  set_mode(\"classification\")\n",
+        "\n",
+        "# Bundle specification and recipe into a worklow\n",
+        "svc_linear_wf <- workflow() %>% \n",
+        "  add_recipe(cuisines_recipe) %>% \n",
+        "  add_model(svc_linear_spec)\n",
+        "\n",
+        "# Print out workflow\n",
+        "svc_linear_wf"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rDs8cWNkhoqu"
+      },
+      "source": [
+        "Now that we have captured the preprocessing steps and model specification into a *workflow*, we can go ahead and train the linear SVC and evaluate results while at it. For performance metrics, let's create a metric set that will evaluate: `accuracy`, `sensitivity`, `Positive Predicted Value` and `F Measure`\n",
+        "\n",
+        "> `augment()` will add column(s) for predictions to the given data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "81wiqcwuhrnq"
+      },
+      "source": [
+        "# Train a linear SVC model\n",
+        "svc_linear_fit <- svc_linear_wf %>% \n",
+        "  fit(data = cuisines_train)\n",
+        "\n",
+        "# Create a metric set\n",
+        "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+        "\n",
+        "\n",
+        "# Make predictions and Evaluate model performance\n",
+        "svc_linear_fit %>% \n",
+        "  augment(new_data = cuisines_test) %>% \n",
+        "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0UFQvHf-huo3"
+      },
+      "source": [
+        "#### Support Vector Machine\n",
+        "\n",
+        "The support vector machine (SVM) is an extension of the support vector classifier in order to accommodate a non-linear boundary between the classes. In essence, SVMs use the *kernel trick* to enlarge the feature space to adapt to nonlinear relationships between classes. One popular and extremely flexible kernel function used by SVMs is the *Radial basis function.* Let's see how it will perform on our data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-KX4S8mzhzmp"
+      },
+      "source": [
+        "set.seed(2056)\n",
+        "\n",
+        "# Make an RBF SVM specification\n",
+        "svm_rbf_spec <- svm_rbf() %>% \n",
+        "  set_engine(\"kernlab\") %>% \n",
+        "  set_mode(\"classification\")\n",
+        "\n",
+        "# Bundle specification and recipe into a worklow\n",
+        "svm_rbf_wf <- workflow() %>% \n",
+        "  add_recipe(cuisines_recipe) %>% \n",
+        "  add_model(svm_rbf_spec)\n",
+        "\n",
+        "\n",
+        "# Train an RBF model\n",
+        "svm_rbf_fit <- svm_rbf_wf %>% \n",
+        "  fit(data = cuisines_train)\n",
+        "\n",
+        "\n",
+        "# Make predictions and Evaluate model performance\n",
+        "svm_rbf_fit %>% \n",
+        "  augment(new_data = cuisines_test) %>% \n",
+        "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QBFSa7WSh4HQ"
+      },
+      "source": [
+        "Much better 🤩!\n",
+        "\n",
+        "> ✅ Please see:\n",
+        ">\n",
+        "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+        ">\n",
+        "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+        ">\n",
+        "> for further reading.\n",
+        "\n",
+        "### Nearest Neighbor classifiers\n",
+        "\n",
+        "*K*-nearest neighbor (KNN) is an algorithm in which each observation is predicted based on its *similarity* to other observations.\n",
+        "\n",
+        "Let's fit one to our data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "k4BxxBcdh9Ka"
+      },
+      "source": [
+        "# Make a KNN specification\n",
+        "knn_spec <- nearest_neighbor() %>% \n",
+        "  set_engine(\"kknn\") %>% \n",
+        "  set_mode(\"classification\")\n",
+        "\n",
+        "# Bundle recipe and model specification into a workflow\n",
+        "knn_wf <- workflow() %>% \n",
+        "  add_recipe(cuisines_recipe) %>% \n",
+        "  add_model(knn_spec)\n",
+        "\n",
+        "# Train a boosted tree model\n",
+        "knn_wf_fit <- knn_wf %>% \n",
+        "  fit(data = cuisines_train)\n",
+        "\n",
+        "\n",
+        "# Make predictions and Evaluate model performance\n",
+        "knn_wf_fit %>% \n",
+        "  augment(new_data = cuisines_test) %>% \n",
+        "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HaegQseriAcj"
+      },
+      "source": [
+        "It appears that this model is not performing that well. Probably changing the model's arguments (see `help(\"nearest_neighbor\")` will improve model performance. Be sure to try it out.\n",
+        "\n",
+        "> ✅ Please see:\n",
+        ">\n",
+        "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+        ">\n",
+        "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+        ">\n",
+        "> to learn more about *K*-Nearest Neighbors classifiers.\n",
+        "\n",
+        "### Ensemble classifiers\n",
+        "\n",
+        "Ensemble algorithms work by combining multiple base estimators to produce an optimal model either by:\n",
+        "\n",
+        "`bagging`: applying an *averaging function* to a collection of base models\n",
+        "\n",
+        "`boosting`: building a sequence of models that build on one another to improve predictive performance.\n",
+        "\n",
+        "Let's start by trying out a Random Forest model, which builds a large collection of decision trees then applies an averaging function to for a better overall model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "49DPoVs6iK1M"
+      },
+      "source": [
+        "# Make a random forest specification\n",
+        "rf_spec <- rand_forest() %>% \n",
+        "  set_engine(\"ranger\") %>% \n",
+        "  set_mode(\"classification\")\n",
+        "\n",
+        "# Bundle recipe and model specification into a workflow\n",
+        "rf_wf <- workflow() %>% \n",
+        "  add_recipe(cuisines_recipe) %>% \n",
+        "  add_model(rf_spec)\n",
+        "\n",
+        "# Train a random forest model\n",
+        "rf_wf_fit <- rf_wf %>% \n",
+        "  fit(data = cuisines_train)\n",
+        "\n",
+        "\n",
+        "# Make predictions and Evaluate model performance\n",
+        "rf_wf_fit %>% \n",
+        "  augment(new_data = cuisines_test) %>% \n",
+        "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RGVYwC_aiUWc"
+      },
+      "source": [
+        "Good job 👏!\n",
+        "\n",
+        "Let's also experiment with a Boosted Tree model.\n",
+        "\n",
+        "Boosted Tree defines an ensemble method that creates a series of sequential decision trees where each tree depends on the results of previous trees in an attempt to incrementally reduce the error. It focuses on the weights of incorrectly classified items and adjusts the fit for the next classifier to correct.\n",
+        "\n",
+        "There are different ways to fit this model (see `help(\"boost_tree\")`). In this example, we'll fit Boosted trees via `xgboost` engine."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Py1YWo-micWs"
+      },
+      "source": [
+        "# Make a boosted tree specification\n",
+        "boost_spec <- boost_tree(trees = 200) %>% \n",
+        "  set_engine(\"xgboost\") %>% \n",
+        "  set_mode(\"classification\")\n",
+        "\n",
+        "# Bundle recipe and model specification into a workflow\n",
+        "boost_wf <- workflow() %>% \n",
+        "  add_recipe(cuisines_recipe) %>% \n",
+        "  add_model(boost_spec)\n",
+        "\n",
+        "# Train a boosted tree model\n",
+        "boost_wf_fit <- boost_wf %>% \n",
+        "  fit(data = cuisines_train)\n",
+        "\n",
+        "\n",
+        "# Make predictions and Evaluate model performance\n",
+        "boost_wf_fit %>% \n",
+        "  augment(new_data = cuisines_test) %>% \n",
+        "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zNQnbuejigZM"
+      },
+      "source": [
+        "\n",
+        "> ✅ Please see:\n",
+        ">\n",
+        "> -   [Machine Learning for Social Scientists](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+        ">\n",
+        "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+        ">\n",
+        "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+        ">\n",
+        "> -   <https://algotech.netlify.app/blog/xgboost/> - Explores the AdaBoost model which is a good alternative to xgboost.\n",
+        ">\n",
+        "> to learn more about Ensemble classifiers.\n",
+        "\n",
+        "## 4. Extra - comparing multiple models\n",
+        "\n",
+        "We have fitted quite a number of models in this lab 🙌. It can become tedious or onerous to create a lot of workflows from different sets of preprocessors and/or model specifications and then calculate the performance metrics one by one.\n",
+        "\n",
+        "Let's see if we can address this by creating a function that fits a list of workflows on the training set then returns the performance metrics based on the test set. We'll get to use `map()` and `map_dfr()` from the [purrr](https://purrr.tidyverse.org/) package to apply functions to each element in list.\n",
+        "\n",
+        "> [`map()`](https://purrr.tidyverse.org/reference/map.html) functions allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the [`map()`](https://purrr.tidyverse.org/reference/map.html) functions is the [iteration chapter](http://r4ds.had.co.nz/iteration.html) in R for data science."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Qzb7LyZnimd2"
+      },
+      "source": [
+        "set.seed(2056)\n",
+        "\n",
+        "# Create a metric set\n",
+        "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+        "\n",
+        "# Define a function that returns performance metrics\n",
+        "compare_models <- function(workflow_list, train_set, test_set){\n",
+        "  \n",
+        "  suppressWarnings(\n",
+        "    # Fit each model to the train_set\n",
+        "    map(workflow_list, fit, data = train_set) %>% \n",
+        "    # Make predictions on the test set\n",
+        "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+        "    # Select desired columns\n",
+        "      select(model, cuisine, .pred_class) %>% \n",
+        "    # Evaluate model performance\n",
+        "      group_by(model) %>% \n",
+        "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+        "      ungroup()\n",
+        "  )\n",
+        "  \n",
+        "} # End of function"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fwa712sNisDA"
+      },
+      "source": [
+        "Let's call our function and compare the accuracy across the models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3i4VJOi2iu-a"
+      },
+      "source": [
+        "# Make a list of workflows\n",
+        "workflow_list <- list(\n",
+        "  \"svc\" = svc_linear_wf,\n",
+        "  \"svm\" = svm_rbf_wf,\n",
+        "  \"knn\" = knn_wf,\n",
+        "  \"random_forest\" = rf_wf,\n",
+        "  \"xgboost\" = boost_wf)\n",
+        "\n",
+        "# Call the function\n",
+        "set.seed(2056)\n",
+        "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+        "\n",
+        "# Print out performance metrics\n",
+        "perf_metrics %>% \n",
+        "  group_by(.metric) %>% \n",
+        "  arrange(desc(.estimate)) %>% \n",
+        "  slice_head(n=7)\n",
+        "\n",
+        "# Compare accuracy\n",
+        "perf_metrics %>% \n",
+        "  filter(.metric == \"accuracy\") %>% \n",
+        "  arrange(desc(.estimate))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KuWK_lEli4nW"
+      },
+      "source": [
+        "\n",
+        "[**workflowset**](https://workflowsets.tidymodels.org/) package allow users to create and easily fit a large number of models but is mostly designed to work with resampling techniques such as `cross-validation`, an approach we are yet to cover.\n",
+        "\n",
+        "## **🚀Challenge**\n",
+        "\n",
+        "Each of these techniques has a large number of parameters that you can tweak for instance `cost` in SVMs, `neighbors` in KNN, `mtry` (Randomly Selected Predictors) in Random Forest.\n",
+        "\n",
+        "Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.\n",
+        "\n",
+        "To find out more about a particular model and its parameters, use: `help(\"model\")` e.g `help(\"rand_forest\")`\n",
+        "\n",
+        "> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.\n",
+        "\n",
+        "### [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)\n",
+        "\n",
+        "### **Review & Self Study**\n",
+        "\n",
+        "There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) of useful terminology!\n",
+        "\n",
+        "#### THANK YOU TO:\n",
+        "\n",
+        "[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+        "\n",
+        "[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\n",
+        "\n",
+        "Happy Learning,\n",
+        "\n",
+        "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+        "   width=\"569\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
new file mode 100644
index 000000000..3a6f6ba43
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
@@ -0,0 +1,452 @@
+---
+title: 'Build a classification model: Delicious Asian and Indian Cuisines'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Cuisine classifiers 2
+
+In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.
+
+### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
+
+### **Prerequisite**
+
+We assume that you have completed the previous lessons since we will be carrying forward some concepts we learned before.
+
+For this lesson, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `themis`: The [themis package](https://themis.tidymodels.org/) provides Extra Recipes Steps for Dealing with Unbalanced Data.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "kernlab", "themis", "ranger", "xgboost", "kknn"))`
+
+Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman"))install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)
+```
+
+Now, let's hit the ground running!
+
+## **1. A classification map**
+
+In our [previous lesson](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1), we tried to address the question: how do we choose between multiple models? To a great extent, it depends on the characteristics of the data and the type of problem we want to solve (for instance classification or regression?)
+
+Previously, we learned about the various options you have when classifying data using Microsoft's cheat sheet. Python's Machine Learning framework, Scikit-learn, offers a similar but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):
+
+![](../../images/map.png){width="650"}\
+
+> Tip: [visit this map online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) and click along the path to read documentation.
+>
+> The [Tidymodels reference site](https://www.tidymodels.org/find/parsnip/#models) also provides an excellent documentation about different types of model.
+
+### **The plan** 🗺️
+
+This map is very helpful once you have a clear grasp of your data, as you can 'walk' along its paths to a decision:
+
+-   We have \>50 samples
+
+-   We want to predict a category
+
+-   We have labeled data
+
+-   We have fewer than 100K samples
+
+-   ✨ We can choose a Linear SVC
+
+-   If that doesn't work, since we have numeric data
+
+    -   We can try a ✨ KNeighbors Classifier
+
+        -   If that doesn't work, try ✨ SVC and ✨ Ensemble Classifiers
+
+This is a very helpful trail to follow. Now, let's get right into it using the [tidymodels](https://www.tidymodels.org/) modelling framework: a consistent and flexible collection of R packages developed to encourage good statistical practice 😊.
+
+## 2. Split the data and deal with imbalanced data set.
+
+From our previous lessons, we learnt that there were a set of common ingredients across our cuisines. Also, there was quite an unequal distribution in the number of cuisines.
+
+We'll deal with these by
+
+-   Dropping the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.
+
+-   Use a `recipe` that preprocesses the data to get it ready for modelling by applying an `over-sampling` algorithm.
+
+We already looked at the above in the previous lesson so this should be a breeze 🥳!
+
+```{r clean_imbalance}
+# Load the core Tidyverse and Tidymodels packages
+library(tidyverse)
+library(tidymodels)
+
+# Load the original cuisines data
+df <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv")
+
+# Drop id column, rice, garlic and ginger from our original data set
+df_select <- df %>% 
+  select(-c(1, rice, garlic, ginger)) %>%
+  # Encode cuisine column as categorical
+  mutate(cuisine = factor(cuisine))
+
+
+# Create data split specification
+set.seed(2056)
+cuisines_split <- initial_split(data = df_select,
+                                strata = cuisine,
+                                prop = 0.7)
+
+# Extract the data in each split
+cuisines_train <- training(cuisines_split)
+cuisines_test <- testing(cuisines_split)
+
+# Display distribution of cuisines in the training set
+cuisines_train %>% 
+  count(cuisine) %>% 
+  arrange(desc(n))
+
+
+```
+
+### Deal with imbalanced data
+
+Imbalanced data often has negative effects on the model performance. Many models perform best when the number of observations is equal and, thus, tend to struggle with unbalanced data.
+
+There are majorly two ways of dealing with imbalanced data sets:
+
+-   adding observations to the minority class: `Over-sampling` e.g using a SMOTE algorithm which synthetically generates new examples of the minority class using nearest neighbors of these cases.
+
+-   removing observations from majority class: `Under-sampling`
+
+In our previous lesson, we demonstrated how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis. In our case, we want to have an equal distribution in the number of our cuisines for our `training set`. Let's get right into it.
+
+```{r recap_balance}
+# Load themis package for dealing with imbalanced data
+library(themis)
+
+# Create a recipe for preprocessing training data
+cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%
+  step_smote(cuisine) 
+
+# Print recipe
+cuisines_recipe
+
+```
+
+Now we are ready to train models 👩‍💻👨‍💻!
+
+## 3. Beyond multinomial regression models
+
+In our previous lesson, we looked at multinomial regression models. Let's explore some more flexible models for classification.
+
+### Support Vector Machines.
+
+In the context of classification, `Support Vector Machines` is a machine learning technique that tries to find a *hyperplane* that "best" separates the classes. Let's look at a simple example:
+
+![By User:ZackWeinberg:This file was derived from: <https://commons.wikimedia.org/w/index.php?curid=22877598>](../../images/svm.png){width="300"}
+
+H~1~ does not separate the classes. H~2~ does, but only with a small margin. H~3~ separates them with the maximal margin.
+
+#### Linear Support Vector Classifier
+
+Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques. In SVC, the hyperplane is chosen to correctly separate `most` of the training observations, but `may misclassify` a few observations. By allowing some points to be on the wrong side, the SVM becomes more robust to outliers hence better generalization to new data. The parameter that regulates this violation is referred to as `cost` which has a default value of 1 (see `help("svm_poly")`).
+
+Let's create a linear SVC by setting `degree = 1` in a polynomial SVM model.
+
+```{r svc_spec}
+# Make a linear SVC specification
+svc_linear_spec <- svm_poly(degree = 1) %>% 
+  set_engine("kernlab") %>% 
+  set_mode("classification")
+
+# Bundle specification and recipe into a worklow
+svc_linear_wf <- workflow() %>% 
+  add_recipe(cuisines_recipe) %>% 
+  add_model(svc_linear_spec)
+
+# Print out workflow
+svc_linear_wf
+```
+
+Now that we have captured the preprocessing steps and model specification into a *workflow*, we can go ahead and train the linear SVC and evaluate results while at it. For performance metrics, let's create a metric set that will evaluate: `accuracy`, `sensitivity`, `Positive Predicted Value` and `F Measure`
+
+> `augment()` will add column(s) for predictions to the given data.
+
+```{r svc_train}
+# Train a linear SVC model
+svc_linear_fit <- svc_linear_wf %>% 
+  fit(data = cuisines_train)
+
+# Create a metric set
+eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)
+
+
+# Make predictions and Evaluate model performance
+svc_linear_fit %>% 
+  augment(new_data = cuisines_test) %>% 
+  eval_metrics(truth = cuisine, estimate = .pred_class)
+  
+
+
+```
+
+#### 
+
+#### Support Vector Machine
+
+The support vector machine (SVM) is an extension of the support vector classifier in order to accommodate a non-linear boundary between the classes. In essence, SVMs use the *kernel trick* to enlarge the feature space to adapt to nonlinear relationships between classes. One popular and extremely flexible kernel function used by SVMs is the *Radial basis function.* Let's see how it will perform on our data.
+
+```{r svm_rbf}
+set.seed(2056)
+
+# Make an RBF SVM specification
+svm_rbf_spec <- svm_rbf() %>% 
+  set_engine("kernlab") %>% 
+  set_mode("classification")
+
+# Bundle specification and recipe into a worklow
+svm_rbf_wf <- workflow() %>% 
+  add_recipe(cuisines_recipe) %>% 
+  add_model(svm_rbf_spec)
+
+
+# Train an RBF model
+svm_rbf_fit <- svm_rbf_wf %>% 
+  fit(data = cuisines_train)
+
+
+# Make predictions and Evaluate model performance
+svm_rbf_fit %>% 
+  augment(new_data = cuisines_test) %>% 
+  eval_metrics(truth = cuisine, estimate = .pred_class)
+```
+
+Much better 🤩!
+
+> ✅ Please see:
+>
+> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R
+>
+> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R
+>
+> for further reading.
+
+### Nearest Neighbor classifiers
+
+*K*-nearest neighbor (KNN) is an algorithm in which each observation is predicted based on its *similarity* to other observations.
+
+Let's fit one to our data.
+
+```{r knn}
+# Make a KNN specification
+knn_spec <- nearest_neighbor() %>% 
+  set_engine("kknn") %>% 
+  set_mode("classification")
+
+# Bundle recipe and model specification into a workflow
+knn_wf <- workflow() %>% 
+  add_recipe(cuisines_recipe) %>% 
+  add_model(knn_spec)
+
+# Train a boosted tree model
+knn_wf_fit <- knn_wf %>% 
+  fit(data = cuisines_train)
+
+
+# Make predictions and Evaluate model performance
+knn_wf_fit %>% 
+  augment(new_data = cuisines_test) %>% 
+  eval_metrics(truth = cuisine, estimate = .pred_class)
+```
+
+It appears that this model is not performing that well. Probably changing the model's arguments (see `help("nearest_neighbor")` will improve model performance. Be sure to try it out.
+
+> ✅ Please see:
+>
+> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)
+>
+> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)
+>
+> to learn more about *K*-Nearest Neighbors classifiers.
+
+### Ensemble classifiers
+
+Ensemble algorithms work by combining multiple base estimators to produce an optimal model either by:
+
+`bagging`: applying an *averaging function* to a collection of base models
+
+`boosting`: building a sequence of models that build on one another to improve predictive performance.
+
+Let's start by trying out a Random Forest model, which builds a large collection of decision trees then applies an averaging function to for a better overall model.
+
+```{r rf}
+# Make a random forest specification
+rf_spec <- rand_forest() %>% 
+  set_engine("ranger") %>% 
+  set_mode("classification")
+
+# Bundle recipe and model specification into a workflow
+rf_wf <- workflow() %>% 
+  add_recipe(cuisines_recipe) %>% 
+  add_model(rf_spec)
+
+# Train a random forest model
+rf_wf_fit <- rf_wf %>% 
+  fit(data = cuisines_train)
+
+
+# Make predictions and Evaluate model performance
+rf_wf_fit %>% 
+  augment(new_data = cuisines_test) %>% 
+  eval_metrics(truth = cuisine, estimate = .pred_class)
+  
+
+```
+
+Good job 👏!
+
+Let's also experiment with a Boosted Tree model.
+
+Boosted Tree defines an ensemble method that creates a series of sequential decision trees where each tree depends on the results of previous trees in an attempt to incrementally reduce the error. It focuses on the weights of incorrectly classified items and adjusts the fit for the next classifier to correct.
+
+There are different ways to fit this model (see `help("boost_tree")`). In this example, we'll fit Boosted trees via `xgboost` engine.
+
+```{r boosted_tree}
+# Make a boosted tree specification
+boost_spec <- boost_tree(trees = 200) %>% 
+  set_engine("xgboost") %>% 
+  set_mode("classification")
+
+# Bundle recipe and model specification into a workflow
+boost_wf <- workflow() %>% 
+  add_recipe(cuisines_recipe) %>% 
+  add_model(boost_spec)
+
+# Train a boosted tree model
+boost_wf_fit <- boost_wf %>% 
+  fit(data = cuisines_train)
+
+
+# Make predictions and Evaluate model performance
+boost_wf_fit %>% 
+  augment(new_data = cuisines_test) %>% 
+  eval_metrics(truth = cuisine, estimate = .pred_class)
+```
+
+> ✅ Please see:
+>
+> -   [Machine Learning for Social Scientists](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)
+>
+> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)
+>
+> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)
+>
+> -   <https://algotech.netlify.app/blog/xgboost/> - Explores the AdaBoost model which is a good alternative to xgboost.
+>
+> to learn more about Ensemble classifiers.
+
+## 4. Extra - comparing multiple models
+
+We have fitted quite a number of models in this lab 🙌. It can become tedious or onerous to create a lot of workflows from different sets of preprocessors and/or model specifications and then calculate the performance metrics one by one.
+
+Let's see if we can address this by creating a function that fits a list of workflows on the training set then returns the performance metrics based on the test set. We'll get to use `map()` and `map_dfr()` from the [purrr](https://purrr.tidyverse.org/) package to apply functions to each element in list.
+
+> [`map()`](https://purrr.tidyverse.org/reference/map.html) functions allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the [`map()`](https://purrr.tidyverse.org/reference/map.html) functions is the [iteration chapter](http://r4ds.had.co.nz/iteration.html) in R for data science.
+
+```{r compare_models}
+set.seed(2056)
+
+# Create a metric set
+eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)
+
+# Define a function that returns performance metrics
+compare_models <- function(workflow_list, train_set, test_set){
+  
+  suppressWarnings(
+    # Fit each model to the train_set
+    map(workflow_list, fit, data = train_set) %>% 
+    # Make predictions on the test set
+      map_dfr(augment, new_data = test_set, .id = "model") %>%
+    # Select desired columns
+      select(model, cuisine, .pred_class) %>% 
+    # Evaluate model performance
+      group_by(model) %>% 
+      eval_metrics(truth = cuisine, estimate = .pred_class) %>% 
+      ungroup()
+  )
+  
+} # End of function
+
+
+```
+
+Let's call our function and compare the accuracy across the models.
+
+```{r call_fn}
+# Make a list of workflows
+workflow_list <- list(
+  "svc" = svc_linear_wf,
+  "svm" = svm_rbf_wf,
+  "knn" = knn_wf,
+  "random_forest" = rf_wf,
+  "xgboost" = boost_wf)
+
+# Call the function
+set.seed(2056)
+perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)
+
+# Print out performance metrics
+perf_metrics %>% 
+  group_by(.metric) %>% 
+  arrange(desc(.estimate)) %>% 
+  slice_head(n=7)
+
+# Compare accuracy
+perf_metrics %>% 
+  filter(.metric == "accuracy") %>% 
+  arrange(desc(.estimate))
+
+```
+
+[**workflowset**](https://workflowsets.tidymodels.org/) package allow users to create and easily fit a large number of models but is mostly designed to work with resampling techniques such as `cross-validation`, an approach we are yet to cover.
+
+## **🚀Challenge**
+
+Each of these techniques has a large number of parameters that you can tweak for instance `cost` in SVMs, `neighbors` in KNN, `mtry` (Randomly Selected Predictors) in Random Forest.
+
+Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
+
+To find out more about a particular model and its parameters, use: `help("model")` e.g `help("rand_forest")`
+
+> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.
+
+### [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
+
+### **Review & Self Study**
+
+There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) of useful terminology!
+
+#### THANK YOU TO:
+
+[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).
+
+[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️
+
+Happy Learning,
+
+[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.
+
+![Artwork by \@allison_horst](../../images/r_learners_sm.jpeg)
diff --git a/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/4-Classification/3-Classifiers-2/solution/notebook.ipynb
index d953c603d..a089b21fa 100644
--- a/4-Classification/3-Classifiers-2/solution/notebook.ipynb
+++ b/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },
diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md
new file mode 100644
index 000000000..5294a7063
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/README.it.md
@@ -0,0 +1,235 @@
+# Classificatori di cucina 2
+
+In questa seconda lezione sulla classificazione, si esploreranno più modi per classificare i dati numerici. Si Impareranno anche le ramificazioni per la scelta di un classificatore rispetto all'altro.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=it)
+
+### Prerequisito
+
+Si parte dal presupposto che siano state completate le lezioni precedenti e si disponga di un insieme di dati pulito nella cartella `data` chiamato _clean_cuisine.csv_ nella radice di questa cartella di 4 lezioni.
+
+### Preparazione
+
+Il file _notebook.ipynb_ è stato caricato con l'insieme di dati pulito ed è stato diviso in dataframe di dati X e y, pronti per il processo di creazione del modello.
+
+## Una mappa di classificazione
+
+In precedenza, si sono apprese le varie opzioni a disposizione durante la classificazione dei dati utilizzando il cheat sheet di Microsoft. Scikit-learn offre un cheat sheet simile, ma più granulare che può aiutare ulteriormente a restringere i propri stimatori (un altro termine per i classificatori):
+
+![Mappa ML da Scikit-learn](../images/map.png)
+> Suggerimento: [visitare questa mappa online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) e fare clic lungo il percorso per leggere la documentazione.
+
+### Il piano
+
+Questa mappa è molto utile una volta che si ha una chiara comprensione dei propri dati, poiché si può "camminare" lungo i suoi percorsi verso una decisione:
+
+- Ci sono >50 campioni
+- Si vuole pronosticare una categoria
+- I dati sono etichettati
+- Ci sono meno di 100K campioni
+- ✨ Si può scegliere un SVC lineare
+- Se non funziona, visto che ci sono dati numerici
+   - Si può provare un ✨ KNeighbors Classifier
+      - Se non funziona, si prova ✨ SVC e ✨ Classificatori di ensemble
+
+Questo è un percorso molto utile da seguire.
+
+## Esercizio: dividere i dati
+
+Seguendo questo percorso, si dovrebbe iniziare importando alcune librerie da utilizzare.
+
+1. Importare le librerie necessarie:
+
+   ```python
+   from sklearn.neighbors import KNeighborsClassifier
+   from sklearn.linear_model import LogisticRegression
+   from sklearn.svm import SVC
+   from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+   from sklearn.model_selection import train_test_split, cross_val_score
+   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+   import numpy as np
+   ```
+
+1. Dividere i dati per allenamento e test:
+
+   ```python
+   X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+   ```
+
+## Classificatore lineare SVC
+
+Il clustering Support-Vector (SVC) è figlio della famiglia di tecniche ML Support-Vector (ulteriori informazioni su queste di seguito). In questo metodo, si può scegliere un "kernel" per decidere come raggruppare le etichette. Il parametro 'C' si riferisce alla 'regolarizzazione' che regola l'influenza dei parametri. Il kernel può essere uno dei [tanti](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC); qui si imposta su 'lineare' per assicurarsi di sfruttare l'SVC lineare. Il valore predefinito di probabilità è 'false'; qui si imposta su 'true' per raccogliere stime di probabilità. Si imposta lo stato casuale su "0" per mescolare i dati per ottenere le probabilità.
+
+### Esercizio: applicare una SVC lineare
+
+Iniziare creando un array di classificatori. Si aggiungerà progressivamente a questo array durante il test.
+
+1. Iniziare con un SVC lineare:
+
+   ```python
+   C = 10
+   # Create different classifiers.
+   classifiers = {
+       'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0)
+   }
+   ```
+
+2. Addestrare il modello utilizzando Linear SVC e stampare un rapporto:
+
+   ```python
+   n_classifiers = len(classifiers)
+
+   for index, (name, classifier) in enumerate(classifiers.items()):
+       classifier.fit(X_train, np.ravel(y_train))
+
+       y_pred = classifier.predict(X_test)
+       accuracy = accuracy_score(y_test, y_pred)
+       print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
+       print(classification_report(y_test,y_pred))
+   ```
+
+   Il risultato è abbastanza buono:
+
+   ```output
+   Accuracy (train) for Linear SVC: 78.6%
+                 precision    recall  f1-score   support
+
+        chinese       0.71      0.67      0.69       242
+         indian       0.88      0.86      0.87       234
+       japanese       0.79      0.74      0.76       254
+         korean       0.85      0.81      0.83       242
+           thai       0.71      0.86      0.78       227
+
+       accuracy                           0.79      1199
+      macro avg       0.79      0.79      0.79      1199
+   weighted avg       0.79      0.79      0.79      1199
+   ```
+
+## Classificatore K-Neighbors
+
+K-Neighbors fa parte della famiglia dei metodi ML "neighbors" (vicini), che possono essere utilizzati sia per l'apprendimento supervisionato che non supervisionato. In questo metodo, viene creato un numero predefinito di punti e i dati vengono raccolti attorno a questi punti in modo tale da poter prevedere etichette generalizzate per i dati.
+
+### Esercizio: applicare il classificatore K-Neighbors
+
+Il classificatore precedente era buono e funzionava bene con i dati, ma forse si può ottenere una maggiore precisione. Provare un classificatore K-Neighbors.
+
+1. Aggiungere una riga all'array classificatore (aggiungere una virgola dopo l'elemento Linear SVC):
+
+   ```python
+   'KNN classifier': KNeighborsClassifier(C),
+   ```
+
+   Il risultato è un po' peggio:
+
+   ```output
+   Accuracy (train) for KNN classifier: 73.8%
+                 precision    recall  f1-score   support
+
+        chinese       0.64      0.67      0.66       242
+         indian       0.86      0.78      0.82       234
+       japanese       0.66      0.83      0.74       254
+         korean       0.94      0.58      0.72       242
+           thai       0.71      0.82      0.76       227
+
+       accuracy                           0.74      1199
+      macro avg       0.76      0.74      0.74      1199
+   weighted avg       0.76      0.74      0.74      1199
+   ```
+
+   ✅ Scoprire [K-Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#neighbors)
+
+## Classificatore Support Vector
+
+I classificatori Support-Vector fanno parte della famiglia di metodi ML [Support-Vector Machine](https://it.wikipedia.org/wiki/Macchine_a_vettori_di_supporto) utilizzati per le attività di classificazione e regressione. Le SVM "mappano esempi di addestramento in punti nello spazio" per massimizzare la distanza tra due categorie. I dati successivi vengono mappati in questo spazio in modo da poter prevedere la loro categoria.
+
+### Esercizio: applicare un classificatore di vettori di supporto
+
+Si prova a ottenere una precisione leggermente migliore con un classificatore di vettori di supporto.
+
+1. Aggiungere una virgola dopo l'elemento K-Neighbors, quindi aggiungere questa riga:
+
+   ```python
+   'SVC': SVC(),
+   ```
+
+   Il risultato è abbastanza buono!
+
+   ```output
+   Accuracy (train) for SVC: 83.2%
+                 precision    recall  f1-score   support
+
+        chinese       0.79      0.74      0.76       242
+         indian       0.88      0.90      0.89       234
+       japanese       0.87      0.81      0.84       254
+         korean       0.91      0.82      0.86       242
+           thai       0.74      0.90      0.81       227
+
+       accuracy                           0.83      1199
+      macro avg       0.84      0.83      0.83      1199
+   weighted avg       0.84      0.83      0.83      1199
+   ```
+
+   ✅ Scoprire i vettori di [supporto](https://scikit-learn.org/stable/modules/svm.html#svm)
+
+## Classificatori ensamble
+
+Si segue il percorso fino alla fine, anche se il test precedente è stato abbastanza buono. Si provano un po' di classificatori di ensemble, nello specifico Random Forest e AdaBoost:
+
+```python
+'RFST': RandomForestClassifier(n_estimators=100),
+  'ADA': AdaBoostClassifier(n_estimators=100)
+```
+
+Il risultato è molto buono, soprattutto per Random Forest:
+
+```output
+Accuracy (train) for RFST: 84.5%
+              precision    recall  f1-score   support
+
+     chinese       0.80      0.77      0.78       242
+      indian       0.89      0.92      0.90       234
+    japanese       0.86      0.84      0.85       254
+      korean       0.88      0.83      0.85       242
+        thai       0.80      0.87      0.83       227
+
+    accuracy                           0.84      1199
+   macro avg       0.85      0.85      0.84      1199
+weighted avg       0.85      0.84      0.84      1199
+
+Accuracy (train) for ADA: 72.4%
+              precision    recall  f1-score   support
+
+     chinese       0.64      0.49      0.56       242
+      indian       0.91      0.83      0.87       234
+    japanese       0.68      0.69      0.69       254
+      korean       0.73      0.79      0.76       242
+        thai       0.67      0.83      0.74       227
+
+    accuracy                           0.72      1199
+   macro avg       0.73      0.73      0.72      1199
+weighted avg       0.73      0.72      0.72      1199
+```
+
+✅ Ulteriori informazioni sui [classificatori di ensemble](https://scikit-learn.org/stable/modules/ensemble.html)
+
+Questo metodo di Machine Learning "combina le previsioni di diversi stimatori di base" per migliorare la qualità del modello. In questo  esempio, si è utilizzato Random Trees e AdaBoost.
+
+- [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest), un metodo di calcolo della media, costruisce una "foresta" di "alberi decisionali" infusi di casualità per evitare il sovradattamento. Il parametro n_estimators è impostato sul numero di alberi.
+
+- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) adatta un classificatore a un insieme di dati e quindi adatta le copie di quel classificatore allo stesso insieme di dati. Si concentra sui pesi degli elementi classificati in modo errato e regola l'adattamento per il successivo classificatore da correggere.
+
+---
+
+## 🚀 Sfida
+
+Ognuna di queste tecniche ha un gran numero di parametri che si possono modificare. Ricercare i parametri predefiniti di ciascuno e pensare a cosa significherebbe modificare questi parametri per la qualità del modello.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+C'è molto gergo in queste lezioni, quindi si prenda un minuto per rivedere [questo elenco](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) di terminologia utile!
+
+## Compito
+
+[Giocore coi parametri](assignment.it.md)
diff --git a/4-Classification/3-Classifiers-2/translations/README.ko.md b/4-Classification/3-Classifiers-2/translations/README.ko.md
new file mode 100644
index 000000000..9438c4308
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/README.ko.md
@@ -0,0 +1,235 @@
+# 요리 classifiers 2
+
+두번째 classification 강의에서, 숫자 데이터를 분류하는 더 많은 방식을 알아봅니다. 다른 것보다 하나의 classifier를 선택하는 파급효과도 배우게 됩니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
+
+### 필요 조건
+
+직전 강의를 완료하고 4강 폴더의 최상단 `data` 폴더에 _cleaned_cuisines.csv_ 라고 불리는 정리된 데이터셋이 있다고 가정합니다.
+
+### 준비하기
+
+정리된 데이터셋과 _notebook.ipynb_ 파일을 불러오고 X 와 y 데이터프레임으로 나누면, 모델 제작 프로세스를 준비하게 됩니다.
+
+## Classification map
+
+이전에, Microsoft 치트 시트를 사용해서 데이터를 분류할 때 다양한 옵션을 배울 수 있었습니다. Scikit-learn은 estimators (classifiers)를 좁히는 데 더 도움을 받을 수 있었고, 보다 세분화된 치트 시트를 비슷하게 제공합니다:
+
+![ML Map from Scikit-learn](../images/map.png)
+> 팁: [visit this map online](https://scikit-learn.org/stable/tutorial/machine_learning_map/)으로 경로를 따라 클릭해서 문서를 읽어봅니다.
+
+### 계획
+
+지도는 데이터를 명쾌하게 파악하면 정한 길을 따라 'walk'할 수 있으므르 매우 도움이 됩니다:
+
+- 샘플을 >50개 가지고 있습니다
+- 카테고리를 예측하고 싶습니다
+- 라벨링된 데이터를 가지고 있습니다
+- 100K개 보다 적은 샘플을 가지고 있습니다
+- ✨ Linear SVC를 고를 수 있습니다
+- 동작하지 않을 때, 숫자 데이터를 가지고 있으므로
+    - ✨ KNeighbors Classifier를 시도할 수 있습니다
+      - 만약 그것도 동작하지 않는다면, ✨ SVC 와 ✨ Ensemble Classifiers를 시도합니다.
+
+따라가면 도움을 받을 수 있습니다.
+
+## 연습 - 데이터 나누기
+
+경로를 따라서, 사용할 라이브러리를 가져오기 시작해야 합니다.
+
+1. 필요한 라이브러리를 Import 합니다:
+
+    ```python
+    from sklearn.neighbors import KNeighborsClassifier
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.svm import SVC
+    from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    import numpy as np
+    ```
+
+1. 훈련과 테스트 데이터로 나눕니다:
+
+    ```python
+    X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+    ```
+
+## Linear SVC classifier
+
+Support-Vector clustering (SVC)는 ML 기술 중에서 Support-Vector machines의 하위입니다 (아래에서 자세히 알아봅니다). 이 메소드에서, 'kernel'을 선택하고 라벨을 클러스터하는 방식을 결정할 수 있습니다. 'C' 파라미터는 파라미터의 영향을 규제할 'regularization'을 나타냅니다. 커널은 [several](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) 중에서 있을 수 있습니다. 여기는 linear SVC를 활용하도록 'linear'로 설정합니다. 확률은 'false'가 기본입니다; 하지만 확률을 추정하기 위해서 'true'로 설정합니다. 확률을 얻으려면 데이터를 섞어서 랜덤 상태 '0'으로 설정합니다.
+
+### 연습 - linear SVC 적용하기
+
+classifiers의 배열을 만들기 시작합니다. 테스트하며 배열에 점차 추가할 예정입니다.
+
+1. Linear SVC로 시작합니다:
+
+    ```python
+    C = 10
+    # Create different classifiers.
+    classifiers = {
+        'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0)
+    }
+    ```
+
+2. Linear SVC로 모델을 훈련하고 리포트도 출력합니다:
+
+    ```python
+    n_classifiers = len(classifiers)
+    
+    for index, (name, classifier) in enumerate(classifiers.items()):
+        classifier.fit(X_train, np.ravel(y_train))
+    
+        y_pred = classifier.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+        print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
+        print(classification_report(y_test,y_pred))
+    ```
+
+    결과는 멋집니다:
+
+    ```output
+    Accuracy (train) for Linear SVC: 78.6% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.71      0.67      0.69       242
+          indian       0.88      0.86      0.87       234
+        japanese       0.79      0.74      0.76       254
+          korean       0.85      0.81      0.83       242
+            thai       0.71      0.86      0.78       227
+    
+        accuracy                           0.79      1199
+       macro avg       0.79      0.79      0.79      1199
+    weighted avg       0.79      0.79      0.79      1199
+    ```
+
+## K-Neighbors classifier
+
+K-Neighbors는 supervised 와 unsupervised learning에서 사용하는 ML 방식 중 "neighbors" 계열의 일부분입니다. 이 메소드에서, 미리 정의한 수의 포인트를 만들고 포인트 주변의 데이터를 수집하면 데이터에 대한 일반화된 라벨을 예측할 수 있습니다.
+
+### 연습 - K-Neighbors classifier 적용하기
+
+이전 classifier는 좋았고, 데이터도 잘 동작했지만, 더 정확도를 높일 수 있을 수 있습니다. K-Neighbors classifier를 시도해봅니다.
+
+1. classifier 배열에 라인을 추가합니다 (Linear SVC 아이템 뒤에 컴마를 추가합니다):
+
+    ```python
+    'KNN classifier': KNeighborsClassifier(C),
+    ```
+
+    결과는 조금 나쁩니다:
+
+    ```output
+    Accuracy (train) for KNN classifier: 73.8% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.64      0.67      0.66       242
+          indian       0.86      0.78      0.82       234
+        japanese       0.66      0.83      0.74       254
+          korean       0.94      0.58      0.72       242
+            thai       0.71      0.82      0.76       227
+    
+        accuracy                           0.74      1199
+       macro avg       0.76      0.74      0.74      1199
+    weighted avg       0.76      0.74      0.74      1199
+    ```
+
+    ✅ [K-Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#neighbors)에 대하여 알아봅니다
+
+## Support Vector Classifier
+
+Support-Vector classifiers는 classification 과 regression 작업에 사용하는 ML 방식 중에서 [Support-Vector Machine](https://wikipedia.org/wiki/Support-vector_machine) 계열의 일부분입니다. SVMs은 두 카테고리 사이의 거리를 최대로 하려고 "공간의 포인트에 훈련 예시를 맵핑"합니다. 차후 데이터는 카테고리를 예측할 수 있게 이 공간에 맵핑됩니다.
+
+### 연습 - Support Vector Classifier 적용하기
+
+Support Vector Classifier로 정확도를 조금 더 올립니다.
+
+1. K-Neighbors 아이템 뒤로 컴마를 추가하고, 라인을 추가합니다:
+
+    ```python
+    'SVC': SVC(),
+    ```
+
+    결과는 꽤 좋습니다!
+
+    ```output
+    Accuracy (train) for SVC: 83.2% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.79      0.74      0.76       242
+          indian       0.88      0.90      0.89       234
+        japanese       0.87      0.81      0.84       254
+          korean       0.91      0.82      0.86       242
+            thai       0.74      0.90      0.81       227
+    
+        accuracy                           0.83      1199
+       macro avg       0.84      0.83      0.83      1199
+    weighted avg       0.84      0.83      0.83      1199
+    ```
+
+    ✅ [Support-Vectors](https://scikit-learn.org/stable/modules/svm.html#svm)에 대하여 알아봅니다
+
+## Ensemble Classifiers
+
+지난 테스트에서 꽤 좋았지만, 경로를 끝까지 따라갑니다. Ensemble Classifiers, 구체적으로 Random Forest 와 AdaBoost를 시도합니다:
+
+```python
+'RFST': RandomForestClassifier(n_estimators=100),
+  'ADA': AdaBoostClassifier(n_estimators=100)
+```
+
+특별하게 Random Forest는, 결과가 매우 좋습니다:
+
+```output
+Accuracy (train) for RFST: 84.5% 
+              precision    recall  f1-score   support
+
+     chinese       0.80      0.77      0.78       242
+      indian       0.89      0.92      0.90       234
+    japanese       0.86      0.84      0.85       254
+      korean       0.88      0.83      0.85       242
+        thai       0.80      0.87      0.83       227
+
+    accuracy                           0.84      1199
+   macro avg       0.85      0.85      0.84      1199
+weighted avg       0.85      0.84      0.84      1199
+
+Accuracy (train) for ADA: 72.4% 
+              precision    recall  f1-score   support
+
+     chinese       0.64      0.49      0.56       242
+      indian       0.91      0.83      0.87       234
+    japanese       0.68      0.69      0.69       254
+      korean       0.73      0.79      0.76       242
+        thai       0.67      0.83      0.74       227
+
+    accuracy                           0.72      1199
+   macro avg       0.73      0.73      0.72      1199
+weighted avg       0.73      0.72      0.72      1199
+```
+
+✅ [Ensemble Classifiers](https://scikit-learn.org/stable/modules/ensemble.html)에 대해 배웁니다
+
+머신러닝의 방식 "여러 기본 estimators의 에측을 합쳐"서 모델의 품질을 향상시킵니다. 예시로, Random Trees 와 AdaBoost를 사용합니다.
+
+- 평균 방식인 [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest)는, 오버피팅을 피하려 랜덤성이 들어간 'decision trees'의 'forest'를 만듭니다. n_estimators 파라미터는 트리의 수로 설정합니다.
+
+- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html)는 데이터셋을 classifier로 맞추고 classifier의 카피를 같은 데이터셋에 맞춥니다.잘 못 분류된 아이템의 가중치에 집중하고 다음 classifier를 교정하도록 맞춥니다.
+
+---
+
+## 🚀 도전
+
+각 기술에는 트윅할 수 있는 많은 수의 파라미터가 존재합니다. 각 기본 파라미터를 조사하고 파라미터를 조절헤서 모델 품질에 어떤 의미가 부여되는지 생각합니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
+
+## 검토 & 자기주도 학습
+
+강의에서 많은 특수 용어가 있어서, 잠시 시간을 투자해서 유용한 용어의 [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa)를 검토합니다!
+
+## 과제 
+
+[Parameter play](../assignment.md)
diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md
new file mode 100644
index 000000000..56c1e11e9
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/README.tr.md
@@ -0,0 +1,235 @@
+# Mutfak sınıflandırıcıları 2
+
+Bu ikinci sınıflandırma dersinde, sayısal veriyi sınıflandırmak için daha fazla yöntem öğreneceksiniz. Ayrıca, bir sınıflandırıcıyı diğerlerine tercih etmenin sonuçlarını da öğreneceksiniz.
+
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=tr)
+
+### Ön koşul
+
+Önceki dersleri tamamladığınızı ve bu 4-ders klasörünün kökündeki `data` klasörünüzdeki _cleaned_cuisines.csv_ adlı veri setini temizlediğinizi varsayıyoruz.
+
+### Hazırlık
+
+Temizlenmiş veri setiyle _notebook.ipynb_ dosyanızı yükledik ve model oluşturma sürecine hazır olması için X ve y veri iskeletlerine böldük.
+
+## Bir sınıflandırma haritası
+
+Daha önce, Microsoft'un kopya kağıdını kullanarak veri sınıflandırmanın çeşitli yollarını öğrendiniz. Scikit-learn de buna benzer, öngörücülerinizi (sınıflandırıcı) sınırlandırmanıza ilaveten yardım edecek bir kopya kağıdı sunar.
+
+![Scikit-learn'den Makine Öğrenimi Haritası](../images/map.png)
+> Tavsiye: [Bu haritayı çevrim içi ziyaret edin](https://scikit-learn.org/stable/tutorial/machine_learning_map/) ve rotayı seyrederken dokümantasyonu okumak için tıklayın.
+
+### Plan
+
+Verinizi iyice kavradığınızda bu harita çok faydalı olacaktır, çünkü karara ulaşırken rotalarında 'yürüyebilirsiniz':
+
+- >50 adet örneğimiz var
+- Bir kategori öngörmek istiyoruz
+- Etiketlenmiş veri var
+- 100 binden az örneğimiz var
+- :sparkles: Bir Linear SVC (Doğrusal Destek Vektör Sınıflandırma) seçebiliriz
+- Eğer bu işe yaramazsa, verimiz sayısal olduğundan
+    - :sparkles: Bir KNeighbors (K Komşu) Sınıflandırıcı deneyebiliriz
+      - Eğer bu işe yaramazsa, :sparkles: SVC (Destek Vektör Sınıflandırma) ve :sparkles: Ensemble (Topluluk) Sınıflandırıcılarını deneyin
+
+Bu çok faydalı bir yol.
+
+## Alıştırma - veriyi bölün
+
+Bu yolu takip ederek, kullanmak için bazı kütüphaneleri alarak başlamalıyız.
+
+1. Gerekli kütüphaneleri alın:
+
+    ```python
+    from sklearn.neighbors import KNeighborsClassifier
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.svm import SVC
+    from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    import numpy as np
+    ```
+
+1. Eğitme ve sınama verinizi bölün:
+
+    ```python
+    X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+    ```
+
+## Linear SVC Sınıflandırıcısı
+
+Destek Vektör kümeleme (SVC), makine öğrenimi yöntemlerinden Destek Vektör Makinelerinin (Aşağıda bunun hakkında daha fazla bilgi edineceksiniz.) alt dallarından biridir. Bu yöntemde, etiketleri nasıl kümeleyeceğinize karar vermek için bir 'kernel' seçebilirsiniz. 'C' parametresi 'düzenlileştirme'yi ifade eder ve parametrelerin etkilerini düzenler. Kernel (çekirdek) [birçoğundan](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) biri olabilir; burada, doğrusal SVC leveraj ettiğimizden emin olmak için, 'linear' olarak ayarlıyoruz. Olasılık varsayılan olarak 'false' olarak ayarlıdır; burada, olasılık öngörülerini toplamak için, 'true' olarak ayarlıyoruz. Rastgele durumu (random state), olasılıkları elde etmek için veriyi karıştırmak (shuffle) üzere, '0' olarak ayarlıyoruz.
+
+### Alıştırma - doğrusal SVC uygulayın
+
+Sınıflandırıcıardan oluşan bir dizi oluşturarak başlayın. Sınadıkça bu diziye ekleme yapacağız.
+
+1. Liner SVC ile başlayın:
+
+    ```python
+    C = 10
+    # Create different classifiers.
+    classifiers = {
+        'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0)
+    }
+    ```
+
+2. Linear SVC kullanarak modelinizi eğitin ve raporu bastırın:
+
+    ```python
+    n_classifiers = len(classifiers)
+    
+    for index, (name, classifier) in enumerate(classifiers.items()):
+        classifier.fit(X_train, np.ravel(y_train))
+    
+        y_pred = classifier.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+        print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
+        print(classification_report(y_test,y_pred))
+    ```
+
+    Sonuç oldukça iyi:
+
+    ```output
+    Accuracy (train) for Linear SVC: 78.6% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.71      0.67      0.69       242
+          indian       0.88      0.86      0.87       234
+        japanese       0.79      0.74      0.76       254
+          korean       0.85      0.81      0.83       242
+            thai       0.71      0.86      0.78       227
+    
+        accuracy                           0.79      1199
+       macro avg       0.79      0.79      0.79      1199
+    weighted avg       0.79      0.79      0.79      1199
+    ```
+
+## K-Komşu sınıflandırıcısı
+
+K-Komşu, makine öğrenimi yöntemlerinden "neighbors" (komşular) ailesinin bir parçasıdır ve gözetimli ve gözetimsiz öğrenmenin ikisinde de kullanılabilir. Bu yöntemde, önceden tanımlanmış sayıda nokta üretilir ve veri bu noktalar etrafında, genelleştirilmiş etiketlerin veriler için öngörülebileceği şekilde toplanır.
+
+### Alıştırma - K-Komşu sınıflandırıcısını uygulayın
+
+Önceki sınıflandırıcı iyiydi ve veriyle iyi çalıştı, ancak belki daha iyi bir doğruluk elde edebiliriz. K-Komşu sınıflandırıcısını deneyin.
+
+1. Sınıflandırıcı dizinize bir satır ekleyin (Linear SVC ögesinden sonra bir virgül ekleyin):
+
+    ```python
+    'KNN classifier': KNeighborsClassifier(C),
+    ```
+
+    Sonuç biraz daha kötü:
+
+    ```output
+    Accuracy (train) for KNN classifier: 73.8% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.64      0.67      0.66       242
+          indian       0.86      0.78      0.82       234
+        japanese       0.66      0.83      0.74       254
+          korean       0.94      0.58      0.72       242
+            thai       0.71      0.82      0.76       227
+    
+        accuracy                           0.74      1199
+       macro avg       0.76      0.74      0.74      1199
+    weighted avg       0.76      0.74      0.74      1199
+    ```
+
+    :white_check_mark: [K-Komşu](https://scikit-learn.org/stable/modules/neighbors.html#neighbors) hakkında bilgi edinin
+
+## Destek Vektör Sınıflandırıcısı
+
+Destek Vektör sınıflandırıcıları, makine öğrenimi yöntemlerinden [Destek Vektörü Makineleri](https://wikipedia.org/wiki/Support-vector_machine) ailesinin bir parçasıdır ve sınıflandırma ve regresyon görevlerinde kullanılır. SVM'ler (Destek Vektör Makineleri), iki kategori arasındaki uzaklığı en yükseğe getirmek için eğitme örneklerini boşluktaki noktalara eşler. Sonraki veri, kategorisinin öngörülebilmesi için bu boşluğa eşlenir.
+
+### Alıştırma - bir Destek Vektör Sınıflandırıcısı uygulayın
+
+Bir Destek Vektör Sınıflandırıcısı ile daha iyi bir doğruluk elde etmeye çalışalım.
+
+1. K-Neighbors ögesinden sonra bir virgül ekleyin, sonra bu satırı ekleyin:
+
+    ```python
+    'SVC': SVC(),
+    ```
+
+    Sonuç oldukça iyi!
+
+    ```output
+    Accuracy (train) for SVC: 83.2% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.79      0.74      0.76       242
+          indian       0.88      0.90      0.89       234
+        japanese       0.87      0.81      0.84       254
+          korean       0.91      0.82      0.86       242
+            thai       0.74      0.90      0.81       227
+    
+        accuracy                           0.83      1199
+       macro avg       0.84      0.83      0.83      1199
+    weighted avg       0.84      0.83      0.83      1199
+    ```
+
+    :white_check_mark: [Destek Vektörleri](https://scikit-learn.org/stable/modules/svm.html#svm) hakkında bilgi edinin
+
+## Topluluk Sınıflandırıcıları
+
+Önceki sınamanın oldukça iyi olmasına rağmen rotayı sonuna kadar takip edelim. Bazı Topluluk Sınıflandırıcılarını deneyelim, özellikle Random Forest ve AdaBoost'u:
+
+```python
+'RFST': RandomForestClassifier(n_estimators=100),
+  'ADA': AdaBoostClassifier(n_estimators=100)
+```
+
+Sonuç çok iyi, özellikle Random Forest sonuçları:
+
+```output
+Accuracy (train) for RFST: 84.5% 
+              precision    recall  f1-score   support
+
+     chinese       0.80      0.77      0.78       242
+      indian       0.89      0.92      0.90       234
+    japanese       0.86      0.84      0.85       254
+      korean       0.88      0.83      0.85       242
+        thai       0.80      0.87      0.83       227
+
+    accuracy                           0.84      1199
+   macro avg       0.85      0.85      0.84      1199
+weighted avg       0.85      0.84      0.84      1199
+
+Accuracy (train) for ADA: 72.4% 
+              precision    recall  f1-score   support
+
+     chinese       0.64      0.49      0.56       242
+      indian       0.91      0.83      0.87       234
+    japanese       0.68      0.69      0.69       254
+      korean       0.73      0.79      0.76       242
+        thai       0.67      0.83      0.74       227
+
+    accuracy                           0.72      1199
+   macro avg       0.73      0.73      0.72      1199
+weighted avg       0.73      0.72      0.72      1199
+```
+
+:white_check_mark: [Topluluk Sınıflandırıcıları](https://scikit-learn.org/stable/modules/ensemble.html) hakkında bilgi edinin
+
+Makine Öğreniminin bu yöntemi, modelin kalitesini artırmak için, "birçok temel öngörücünün öngörülerini birleştirir." Bizim örneğimizde, Random Trees ve AdaBoost kullandık.
+
+- [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest) bir ortalama alma yöntemidir, aşırı öğrenmeden kaçınmak için rastgelelikle doldurulmuş 'karar ağaçları'ndan oluşan bir 'orman' oluşturur. n_estimators parametresi, ağaç sayısı olarak ayarlanmaktadır.
+
+- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html), bir sınıflandıcıyı bir veri setine uydurur ve sonra o sınıflandırıcının kopyalarını aynı veri setine uydurur. Yanlış sınıflandırılmış ögelerin ağırlıklarına odaklanır ve bir sonraki sınıflandırıcının düzeltmesi için uydurma/oturtmayı ayarlar.
+
+---
+
+## :rocket: Meydan okuma
+
+Bu yöntemlerden her biri değiştirebileceğiniz birsürü parametre içeriyor. Her birinin varsayılan parametrelerini araştırın ve bu parametreleri değiştirmenin modelin kalitesi için ne anlama gelebileceği hakkında düşünün.
+
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=tr)
+
+## Gözden Geçirme & Kendi Kendine Çalışma
+
+Bu derslerde çok fazla jargon var, bu yüzden yararlı terminoloji içeren [bu listeyi](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) incelemek için bir dakika ayırın.
+
+## Ödev
+
+[Parametre oyunu](assignment.tr.md)
\ No newline at end of file
diff --git a/4-Classification/3-Classifiers-2/translations/assignment.it.md b/4-Classification/3-Classifiers-2/translations/assignment.it.md
new file mode 100644
index 000000000..472cdb114
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Giocore coi parametri
+
+## Istruzioni
+
+Ci sono molti parametri impostati in modalità predefinita quando si lavora con questi classificatori. Intellisense in VS Code può aiutare a scavare in loro. Adottare una delle tecniche di classificazione ML in questa lezione e riaddestrare i modelli modificando i vari valori dei parametri. Costruire un notebook spiegando perché alcune modifiche aiutano la qualità del modello mentre altre la degradano. La risposta sia dettagliata.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ----------------------------- |
+|          | Un notebook viene presentato con un classificatore completamente costruito e i suoi parametri ottimizzati e le modifiche spiegate nelle caselle di testo | Un quaderno è presentato parzialmente o spiegato male | Un notebook contiene errori o è difettoso |
diff --git a/4-Classification/3-Classifiers-2/translations/assignment.tr.md b/4-Classification/3-Classifiers-2/translations/assignment.tr.md
new file mode 100644
index 000000000..fbc740927
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Parametre Oyunu
+
+## Yönergeler
+
+Bu sınıflandırıcılarla çalışırken varsayılan olarak ayarlanmış birçok parametre var. VS Code'daki Intellisense, onları derinlemesine incelemenize yardımcı olabilir. Bu dersteki Makine Öğrenimi Sınıflandırma Yöntemlerinden birini seçin ve çeşitli parametre değerlerini değiştirerek modelleri yeniden eğitin. Neden bazı değişikliklerin modelin kalitesini artırdığını ve bazılarının azalttığını açıklayan bir not defteri yapın. Cevabınız açıklayıcı olmalı.
+
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                                                                                           | Yeterli                                               | Geliştirme Gerekli              |
+| -------- | ---------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------- |
+|          | Bir sınıflandırıcının tamamen oluşturulduğu ve parametrelerinin değiştirilip yazı kutularında açıklandığı bir not defteri sunulmuş | Not defteri kısmen sunulmuş veya az açıklanmış        | Not defteri hatalı veya kusurlu |
\ No newline at end of file
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 773271a1e..b6fb5450b 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -8,7 +8,7 @@ One of the most useful practical uses of machine learning is building recommenda
 
 > 🎥 Click the image above for a video: Andrew Ng introduces recommendation system design
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
 
 In this lesson you will learn:
 
@@ -40,7 +40,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
 1. Then, work with your data in the same way you did in previous lessons, by reading a CSV file using `read_csv()`:
 
     ```python
-    data = pd.read_csv('../data/cleaned_cuisine.csv')
+    data = pd.read_csv('../data/cleaned_cuisines.csv')
     data.head()
     ```
 
@@ -219,7 +219,7 @@ You can use your model directly in a web app. This architecture also allows you
 1. First, import the [Onnx Runtime](https://www.onnxruntime.ai/):
 
     ```html
-    <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script> 
+    <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.9.09/dist/ort.min.js"></script> 
     ```
 
     > Onnx Runtime is used to enable running your Onnx models across a wide range of hardware platforms, including optimizations and an API to use.
@@ -312,7 +312,7 @@ In this code, there are several things happening:
 
 ## Test your application
 
-Open a terminal session in Visual Studio Code in the folder where your index.html file resides. Ensure that you have `[http-server](https://www.npmjs.com/package/http-server)` installed globally, and type `http-server` at the prompt. A localhost should open and you can view your web app. Check what cuisine is recommended based on various ingredients:
+Open a terminal session in Visual Studio Code in the folder where your index.html file resides. Ensure that you have [http-server](https://www.npmjs.com/package/http-server) installed globally, and type `http-server` at the prompt. A localhost should open and you can view your web app. Check what cuisine is recommended based on various ingredients:
 
 ![ingredient web app](images/web-app.png)
 
@@ -321,7 +321,7 @@ Congratulations, you have created a 'recommendation' web app  with a few fields.
 
 Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/4-Applied/solution/index.html b/4-Classification/4-Applied/solution/index.html
index ccea31e4b..3ba07707e 100644
--- a/4-Classification/4-Applied/solution/index.html
+++ b/4-Classification/4-Applied/solution/index.html
@@ -45,7 +45,7 @@
             <button onClick="startInference()">What kind of cuisine can you make?</button>
         </div>      
         <!-- import ONNXRuntime Web from CDN -->
-        <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script>
+        <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.9.0/dist/ort.min.js"></script>
         <script>
             const ingredients = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
             
diff --git a/4-Classification/4-Applied/solution/notebook.ipynb b/4-Classification/4-Applied/solution/notebook.ipynb
index 5ed9da520..b388d2ca8 100644
--- a/4-Classification/4-Applied/solution/notebook.ipynb
+++ b/4-Classification/4-Applied/solution/notebook.ipynb
@@ -115,7 +115,7 @@
     }
    ],
    "source": [
-    "data = pd.read_csv('../../data/cleaned_cuisine.csv')\n",
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
     "data.head()"
    ]
   },
diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md
new file mode 100644
index 000000000..6ed628abe
--- /dev/null
+++ b/4-Classification/4-Applied/translations/README.it.md
@@ -0,0 +1,336 @@
+# Costruire un'App Web per Consigliare una Cucina
+
+In questa lezione si creerà un modello di classificazione utilizzando alcune delle tecniche apprese nelle lezioni precedenti e con il delizioso insieme di dati sulla cucina utilizzato in questa serie. Inoltre, si creerà una piccola app web per utilizzare un modello salvato, sfruttando il runtime web di Onnx.
+
+Uno degli usi pratici più utili dell'apprendimento automatico è la creazione di sistemi di raccomandazione e oggi si può fare il primo passo in quella direzione!
+
+[![Introduzione ai Sistemi di Raccomandazione](https://img.youtube.com/vi/giIXNoiqO_U/0.jpg)](https://youtu.be/giIXNoiqO_U "Introduzione ai Sistemi di Raccomandazione")
+
+> 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng introduce la progettazione di un sistema di raccomandazione
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=it)
+
+In questa lezione, si imparerà:
+
+- Come costruire un modello e salvarlo come modello Onnx
+- Come usare Netron per ispezionare il modello
+- Come utilizzare il modello in un'app web per l'inferenza
+
+## Costruire il modello
+
+La creazione di sistemi ML applicati è una parte importante dell'utilizzo di queste tecnologie per i sistemi aziendali. Si possono utilizzare i modelli all'interno delle proprie applicazioni web (e quindi utilizzarli in un contesto offline se necessario) utilizzando Onnx.
+
+In una [lezione precedente](../../../3-Web-App/1-Web-App/translations/README.it.md) si è costruito un modello di regressione sugli avvistamenti di UFO, è stato serializzato e lo si è utilizzato in un'app Flask. Sebbene questa architettura sia molto utile da conoscere, è un'app Python completa e i requisiti potrebbero includere l'uso di un'applicazione JavaScript.
+
+In questa lezione si può creare un sistema di inferenza di base utilizzando JavaScript. Prima, tuttavia, è necessario addestrare un modello e convertirlo per l'utilizzo con Onnx.
+
+## Esercizio - modello di classificazione di addestramento
+
+Innanzitutto, addestrare un modello di classificazione utilizzando l'insieme di dati pulito delle cucine precedentemente usato.
+
+1. Iniziare importando librerie utili:
+
+   ```python
+   !pip install skl2onnx
+   import pandas as pd
+   ```
+
+   Serve '[skl2onnx](https://onnx.ai/sklearn-onnx/)' per poter convertire il modello di Scikit-learn in formato Onnx.
+
+1. Quindi si  lavora con i dati nello stesso modo delle lezioni precedenti, leggendo un file CSV usando `read_csv()`:
+
+   ```python
+   data = pd.read_csv('../data/cleaned_cuisine.csv')
+   data.head()
+   ```
+
+1. Rimuovere le prime due colonne non necessarie e salvare i dati rimanenti come "X":
+
+   ```python
+   X = data.iloc[:,2:]
+   X.head()
+   ```
+
+1. Salvare le etichette come "y":
+
+   ```python
+   y = data[['cuisine']]
+   y.head()
+
+   ```
+
+### Iniziare la routine di addestramento
+
+Verrà usata la libreria 'SVC' che ha una buona precisione.
+
+1. Importare le librerie appropriate da Scikit-learn:
+
+   ```python
+   from sklearn.model_selection import train_test_split
+   from sklearn.svm import SVC
+   from sklearn.model_selection import cross_val_score
+   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
+   ```
+
+1. Separare gli insiemi di allenamento e test:
+
+   ```python
+   X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
+   ```
+
+1. Costruire un modello di classificazione SVC come fatto nella lezione precedente:
+
+   ```python
+   model = SVC(kernel='linear', C=10, probability=True,random_state=0)
+   model.fit(X_train,y_train.values.ravel())
+   ```
+
+1. Ora provare il modello, chiamando `predict()`:
+
+   ```python
+   y_pred = model.predict(X_test)
+   ```
+
+1. Stampare un rapporto di classificazione per verificare la qualità del modello:
+
+   ```python
+   print(classification_report(y_test,y_pred))
+   ```
+
+   Come visto prima, la precisione è buona:
+
+   ```output
+                   precision    recall  f1-score   support
+
+        chinese       0.72      0.69      0.70       257
+         indian       0.91      0.87      0.89       243
+       japanese       0.79      0.77      0.78       239
+         korean       0.83      0.79      0.81       236
+           thai       0.72      0.84      0.78       224
+
+       accuracy                           0.79      1199
+      macro avg       0.79      0.79      0.79      1199
+   weighted avg       0.79      0.79      0.79      1199
+   ```
+
+### Convertire il modello in Onnx
+
+Assicurarsi di eseguire la conversione con il numero tensore corretto. Questo insieme di dati ha 380 ingredienti elencati, quindi è necessario annotare quel numero in `FloatTensorType`:
+
+1. Convertire usando un numero tensore di 380.
+
+   ```python
+   from skl2onnx import convert_sklearn
+   from skl2onnx.common.data_types import FloatTensorType
+
+   initial_type = [('float_input', FloatTensorType([None, 380]))]
+   options = {id(model): {'nocl': True, 'zipmap': False}}
+   ```
+
+1. Creare l'onx e salvarlo come file **model.onnx**:
+
+   ```python
+   onx = convert_sklearn(model, initial_types=initial_type, options=options)
+   with open("./model.onnx", "wb") as f:
+       f.write(onx.SerializeToString())
+   ```
+
+   > Nota, si possono passare le[opzioni](https://onnx.ai/sklearn-onnx/parameterized.html) nello  script di conversione. In questo caso, si è passato 'nocl' come True e 'zipmap' come False. Poiché questo è un modello di classificazione, si ha la possibilità di rimuovere ZipMap che produce un elenco di dizionari (non necessario). `nocl` si riferisce alle informazioni sulla classe incluse nel modello. Ridurre le dimensioni del modello impostando `nocl` su 'True'.
+
+L'esecuzione dell'intero notebook ora creerà un modello Onnx e lo salverà in questa cartella.
+
+## Visualizzare il modello
+
+I modelli Onnx non sono molto visualizzabili in Visual Studio code, ma c'è un ottimo software gratuito che molti ricercatori usano per visualizzare il modello per assicurarsi che sia costruito correttamente. Scaricare [Netron](https://github.com/lutzroeder/Netron) e aprire il file model.onnx. Si può vedere il modello semplice visualizzato, con i suoi 380 input e classificatore elencati:
+
+![Vista Netron ](../images/netron.png)
+
+Netron è uno strumento utile per visualizzare i modelli.
+
+Ora si è pronti per utilizzare questo modello accurato in un'app web. Si costruisce un'app che tornerà utile quando si guarda nel frigorifero e si prova a capire quale combinazione di ingredienti avanzati si può usare per cucinare una determinata tipologia di cucina, come determinato dal  modello.
+
+## Creare un'applicazione web di raccomandazione
+
+Si può utilizzare il modello direttamente in un'app web. Questa architettura consente anche di eseguirlo localmente e anche offline se necessario. Iniziare creando un file `index.html` nella stessa cartella in cui si è salvato il file `model.onnx`.
+
+1. In questo file _index.html_, aggiungere il seguente codice markup:
+
+   ```html
+   <!DOCTYPE html>
+   <html>
+       <header>
+           <title>Cuisine Matcher</title>
+       </header>
+       <body>
+           ...
+       </body>
+   </html>
+   ```
+
+1. Ora, lavorando all'interno del tag `body` , aggiungere un piccolo markup per mostrare un elenco di caselle di controllo che riflettono alcuni ingredienti:
+
+   ```html
+   <h1>Check your refrigerator. What can you create?</h1>
+           <div id="wrapper">
+               <div class="boxCont">
+                   <input type="checkbox" value="4" class="checkbox">
+                   <label>apple</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="247" class="checkbox">
+                   <label>pear</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="77" class="checkbox">
+                   <label>cherry</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="126" class="checkbox">
+                   <label>fenugreek</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="302" class="checkbox">
+                   <label>sake</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="327" class="checkbox">
+                   <label>soy sauce</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="112" class="checkbox">
+                   <label>cumin</label>
+               </div>
+           </div>
+           <div style="padding-top:10px">
+               <button onClick="startInference()">What kind of cuisine can you make?</button>
+           </div>
+   ```
+
+   Notare che a ogni casella di controllo viene assegnato un valore. Questo riflette l'indice in cui si trova l'ingrediente in base all'insieme di dati. Apple, ad esempio, in questo elenco alfabetico, occupa la quinta colonna, quindi il suo valore è "4" poiché si inizia a contare da 0. Si può consultare il [foglio di calcolo degli ingredienti](../../data/ingredient_indexes.csv) per scoprire l'indice di un determinato ingrediente.
+
+   Continuando il lavoro nel file index.html, aggiungere un blocco di script in cui viene chiamato il modello dopo la chiusura del tag `</div>` finale.
+
+1. Innanzitutto, importare il [runtime Onnx](https://www.onnxruntime.ai/):
+
+   ```html
+   <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script>
+   ```
+
+   > Onnx Runtime viene utilizzato per consentire l'esecuzione dei modelli Onnx su un'ampia gamma di piattaforme hardware, comprese le ottimizzazioni e un'API da utilizzare.
+
+1. Una volta che il Runtime è a posto, lo si può chiamare:
+
+   ```javascript
+   <script>
+               const ingredients = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+               const checks = [].slice.call(document.querySelectorAll('.checkbox'));
+
+               // use an async context to call onnxruntime functions.
+               function init() {
+
+                   checks.forEach(function (checkbox, index) {
+                       checkbox.onchange = function () {
+                           if (this.checked) {
+                               var index = checkbox.value;
+
+                               if (index !== -1) {
+                                   ingredients[index] = 1;
+                               }
+                               console.log(ingredients)
+                           }
+                           else {
+                               var index = checkbox.value;
+
+                               if (index !== -1) {
+                                   ingredients[index] = 0;
+                               }
+                               console.log(ingredients)
+                           }
+                       }
+                   })
+               }
+
+               function testCheckboxes() {
+                       for (var i = 0; i < checks.length; i++)
+                           if (checks[i].type == "checkbox")
+                               if (checks[i].checked)
+                                   return true;
+                       return false;
+               }
+
+               async function startInference() {
+
+                   let checked = testCheckboxes()
+
+                   if (checked) {
+
+                   try {
+                       // create a new session and load the model.
+
+                       const session = await ort.InferenceSession.create('./model.onnx');
+
+                       const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
+                       const feeds = { float_input: input };
+
+                       // feed inputs and run
+
+                       const results = await session.run(feeds);
+
+                       // read from results
+                       alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
+
+                   } catch (e) {
+                       console.log(`failed to inference ONNX model: ${e}.`);
+                   }
+               }
+               else alert("Please check an ingredient")
+
+               }
+       init();
+
+           </script>
+   ```
+
+In questo codice, accadono diverse cose:
+
+1. Si è creato un array di 380 possibili valori (1 o 0) da impostare e inviare al modello per l'inferenza, a seconda che una casella di controllo dell'ingrediente sia selezionata.
+2. Si è creata una serie di caselle di controllo e un modo per determinare se sono state selezionate in una funzione `init` chiamata all'avvio dell'applicazione. Quando una casella di controllo è selezionata, l 'array `ingredients` viene modificato per riflettere l'ingrediente scelto.
+3. Si è creata una funzione `testCheckboxes` che controlla se una casella di controllo è stata selezionata.
+4. Si utilizza quella funzione quando si preme il pulsante e, se una casella di controllo è selezionata, si avvia l'inferenza.
+5. La routine di inferenza include:
+   1. Impostazione di un caricamento asincrono del modello
+   2. Creazione di una struttura tensoriale da inviare al modello
+   3. Creazione di "feed" che riflettano l'input `float_input` creato durante l'addestramento del modello (si può usare Netron per verificare quel nome)
+   4. Invio di questi "feed" al modello e attesa di una risposta
+
+## Verificare l'applicazione
+
+Aprire una sessione terminale in Visual Studio Code nella cartella in cui risiede il file index.html. Assicurarsi di avere [http-server](https://www.npmjs.com/package/http-server) installato globalmente e digitare `http-server` al prompt. Dovrebbe aprirsi nel browser un localhost e si può visualizzare l'app web. Controllare quale cucina è consigliata in base ai vari ingredienti:
+
+![app web degli ingredienti](../images/web-app.png)
+
+Congratulazioni, si è creato un'app web di "raccomandazione" con pochi campi. Si prenda del tempo per costruire questo sistema!
+## 🚀 Sfida
+
+L'app web è molto minimale, quindi continuare a costruirla usando gli ingredienti e i loro indici dai dati [ingredient_indexes](../../data/ingredient_indexes.csv) . Quali combinazioni di sapori funzionano per creare un determinato piatto nazionale?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Sebbene questa lezione abbia appena toccato l'utilità di creare un sistema di raccomandazione per gli ingredienti alimentari, quest'area delle applicazioni ML è molto ricca di esempi. Leggere di più su come sono costruiti questi sistemi:
+
+- https://www.sciencedirect.com/topics/computer-science/recommendation-engine
+- https://www.technologyreview.com/2014/08/25/171547/the-ultimate-challenge-for-recommendation-engines/
+- https://www.technologyreview.com/2015/03/23/168831/everything-is-a-recommendation/
+
+## Compito
+
+[Creare un nuovo sistema di raccomandazione](assignment.it.md)
diff --git a/4-Classification/4-Applied/translations/README.ko.md b/4-Classification/4-Applied/translations/README.ko.md
new file mode 100644
index 000000000..d1684ddf6
--- /dev/null
+++ b/4-Classification/4-Applied/translations/README.ko.md
@@ -0,0 +1,337 @@
+# 요리 추천 Web App 만들기
+
+이 강의에서, 이전 강의에서 배웠던 몇 기술과 이 계열에서 사용했던 맛있는 요리 데이터셋으로 classification 모델을 만들 예정입니다. 추가로, Onnx의 웹 런타임을 활용해서, 저장된 모델로 작은 웹 앱을 만들 것입니다.
+
+머신러닝의 유용하고 실용적인 사용 방식 중에 하나인 recommendation system을 만들고, 오늘 이 쪽으로 처음 걷습니다!
+
+[![Recommendation Systems Introduction](https://img.youtube.com/vi/giIXNoiqO_U/0.jpg)](https://youtu.be/giIXNoiqO_U "Recommendation Systems Introduction")
+
+> 🎥 영상 보려면 이미지 클릭: Andrew Ng introduces recommendation system design
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
+
+이 강의에서 다음을 배우게 됩니다:
+
+- 모델을 만들고 Onnx 모델로 저장하는 방식
+- Netron 사용해서 모델 검사하는 방식
+- 추론을 위한 웹 앱에서 모델을 사용하는 방식
+
+## 모델 만들기
+
+Applied ML 시스템을 만드는 것은 비지니스 시스템에서 이 기술을 활용하는 부분이 중요합니다. Onnx로 웹 애플리케이션에서 (필요하면 오프라인 컨텍스트에서 사용하기도 합니다) 모델을 사용할 수 있습니다.
+
+[previous lesson](../../../3-Web-App/1-Web-App/README.md)에서, UFO 목격에 대한 Regression 모델을 만들었고, "pickled" 한 것을, Flask 앱에서 사용했습니다. 이 구조는 알고 있다면 매우 유용하지만, full-stack Python 앱이므로, JavaScript 애플리케이션을 포함해야 된다고 요구될 수 있습니다.
+
+이 강의에서, 추론할 기초 JavaScript-기반 시스템을 만듭니다. 그러나 먼저, 모델을 훈련하고 Onnx와 같이 사용하기 위해서 변환할 필요가 있습니다.
+
+## 연습 - classification 모델 훈련
+
+먼저, 이미 사용했던 깨끗한 요리 데이터셋으로 classification 모델을 훈련합니다.
+
+1. 유용한 라이브러리를 가져와서 시작합니다:
+
+    ```python
+    !pip install skl2onnx
+    import pandas as pd 
+    ```
+
+    Scikit-learn 모델을 Onnx 포맷으로 변환할 때 도움을 주는 '[skl2onnx](https://onnx.ai/sklearn-onnx/)' 가 필요합니다.
+
+1. 그리고, `read_csv()` 사용해서 CSV 파일을 읽어보면, 이전 강의에서 했던 같은 방식으로 데이터를 작업합니다:
+
+    ```python
+    data = pd.read_csv('../data/cleaned_cuisines.csv')
+    data.head()
+    ```
+
+1. 첫 2개의 필요없는 열을 제거하고 'X'로 나머지 데이터를 저장합니다:
+
+    ```python
+    X = data.iloc[:,2:]
+    X.head()
+    ```
+
+1. 'y'로 라벨을 저장합니다:
+
+    ```python
+    y = data[['cuisine']]
+    y.head()
+    
+    ```
+
+### 훈련 루틴 개시하기
+
+좋은 정확도의 'SVC' 라이브러리를 사용할 예정입니다.
+
+1. Scikit-learn에서 적합한 라이브러리를 Import 합니다:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.svm import SVC
+    from sklearn.model_selection import cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
+    ```
+
+1. 훈련과 테스트 셋으로 가릅니다:
+
+    ```python
+    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
+    ```
+
+1. 이전 강의에서 했던 것처럼 SVC Classification model 모델을 만듭니다:
+
+    ```python
+    model = SVC(kernel='linear', C=10, probability=True,random_state=0)
+    model.fit(X_train,y_train.values.ravel())
+    ```
+
+1. 지금부터, `predict()`를 불러서, 모델을 테스트합니다:
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+1. 모델의 품질을 확인하기 위해서 classification 리포트를 출력합니다:
+
+    ```python
+    print(classification_report(y_test,y_pred))
+    ```
+
+    전에 본 것처럼, 정확도는 좋습니다:
+
+    ```output
+                    precision    recall  f1-score   support
+    
+         chinese       0.72      0.69      0.70       257
+          indian       0.91      0.87      0.89       243
+        japanese       0.79      0.77      0.78       239
+          korean       0.83      0.79      0.81       236
+            thai       0.72      0.84      0.78       224
+    
+        accuracy                           0.79      1199
+       macro avg       0.79      0.79      0.79      1199
+    weighted avg       0.79      0.79      0.79      1199
+    ```
+
+### 모델을 Onnx로 변환하기
+
+적절한 Tensor 숫자로 변환할 수 있어야 합니다. 데이터셋은 380개 성분이 나열되며, `FloatTensorType`에 숫자를 적어야 합니다:
+
+1. 380개의 tensor 숫자로 변환하기.
+
+    ```python
+    from skl2onnx import convert_sklearn
+    from skl2onnx.common.data_types import FloatTensorType
+    
+    initial_type = [('float_input', FloatTensorType([None, 380]))]
+    options = {id(model): {'nocl': True, 'zipmap': False}}
+    ```
+
+1. onx를 생성하고 **model.onnx** 파일로 저장합니다:
+
+    ```python
+    onx = convert_sklearn(model, initial_types=initial_type, options=options)
+    with open("./model.onnx", "wb") as f:
+        f.write(onx.SerializeToString())
+    ```
+
+    > 노트, 변환 스크립트에서 [options](https://onnx.ai/sklearn-onnx/parameterized.html)을 줄 수 있습니다. 이 케이스에서, 'nocl'는 True, 'zipmap'은 False로 줬습니다. (필수는 아니지만) classification 모델이라서, 사전의 리스트를 만드는 ZipMap을 지울 옵션이 있습니다. `nocl`은 모델에 있는 클래스 정보를 나타냅니다. `nocl`을 'True'로 설정해서 모델의 크기를 줄입니다.
+
+전체 노트북을 실행하면 Onnx 모델이 만들어지고 폴더에 저장됩니다.
+
+## 모델 보기
+
+Onnx 모델은 Visual Studio code에서 잘 볼 수 없지만, 많은 연구원들이 모델을 잘 만들었는지 보고 싶어서 모델을 시각화할 때 사용하기 매우 좋은 자유 소프트웨어가 있습니다. [Netron](https://github.com/lutzroeder/Netron)을 내려받고 model.onnx 파일을 엽니다. 380개 입력과 나열된 classifier로 간단한 모델을 시각화해서 볼 수 있습니다:
+
+![Netron visual](../images/netron.png)
+
+Netron은 모델을 보게 도와주는 도구입니다.
+
+지금부터 웹 앱에서 neat 모델을 사용할 준비가 되었습니다. 냉장고를 볼 때 편리한 앱을 만들고 모델이 결정해서 건내준 요리를 조리할 수 있게 남은 재료 조합을 찾아봅니다.
+
+## recommender 웹 애플리케이션 만들기
+
+웹 앱에서 바로 모델을 사용할 수 있습니다. 이 구조를 사용한다면 로컬에서 실행할 수 있고 필요하면 오프라인으로 가능합니다. `model.onnx` 파일을 저장한 동일 폴더에서 `index.html` 파일을 만들기 시작합니다.
+
+1. _index.html_ 파일에서, 다음 마크업을 추가합니다:
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+        <header>
+            <title>Cuisine Matcher</title>
+        </header>
+        <body>
+            ...
+        </body>
+    </html>
+    ```
+
+1. 지금부터, `body` 테그에서 작업하며, 일부 요소를 반영하는 체크박스의 리스트로 보여줄 약간의 마크업을 추가합니다:
+
+    ```html
+    <h1>Check your refrigerator. What can you create?</h1>
+            <div id="wrapper">
+                <div class="boxCont">
+                    <input type="checkbox" value="4" class="checkbox">
+                    <label>apple</label>
+                </div>
+            
+                <div class="boxCont">
+                    <input type="checkbox" value="247" class="checkbox">
+                    <label>pear</label>
+                </div>
+            
+                <div class="boxCont">
+                    <input type="checkbox" value="77" class="checkbox">
+                    <label>cherry</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="126" class="checkbox">
+                    <label>fenugreek</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="302" class="checkbox">
+                    <label>sake</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="327" class="checkbox">
+                    <label>soy sauce</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="112" class="checkbox">
+                    <label>cumin</label>
+                </div>
+            </div>
+            <div style="padding-top:10px">
+                <button onClick="startInference()">What kind of cuisine can you make?</button>
+            </div> 
+    ```
+
+    각 체크박스에 값이 주어졌습니다. 데이터셋에 따라서 식재료가 발견된 인덱스를 반영합니다. Apple을 예시로 들면, 알파벳 리스트에서, 5번째 열을 차지하므로, 0부터 세기 시작해서 값은 '4'가 됩니다. 주어진 식재료의 색인을 찾기 위해서 [ingredients spreadsheet](../../data/ingredient_indexes.csv)를 참고할 수 있습니다
+
+    index.html 파일에 작업을 계속 이어서, 마지막 닫는 `</div>` 뒤에 모델을 부를 script 블록을 추가합니다.
+
+1. 먼저, [Onnx Runtime](https://www.onnxruntime.ai/)을 가져옵니다:
+
+    ```html
+    <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script> 
+    ```
+
+    > Onnx 런타임은 최적화와 사용할 API를 포함해서, 넓은 범위의 하드웨어 플랫폼으로 Onnx 모델을 실행할 때 쓰입니다.
+
+1. 런타임이 자리에 있다면, 이렇게 부를 수 있습니다:
+
+    ```javascript
+    <script>
+                const ingredients = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+                
+                const checks = [].slice.call(document.querySelectorAll('.checkbox'));
+    
+                // use an async context to call onnxruntime functions.
+                function init() {
+                    
+                    checks.forEach(function (checkbox, index) {
+                        checkbox.onchange = function () {
+                            if (this.checked) {
+                                var index = checkbox.value;
+    
+                                if (index !== -1) {
+                                    ingredients[index] = 1;
+                                }
+                                console.log(ingredients)
+                            }
+                            else {
+                                var index = checkbox.value;
+    
+                                if (index !== -1) {
+                                    ingredients[index] = 0;
+                                }
+                                console.log(ingredients)
+                            }
+                        }
+                    })
+                }
+    
+                function testCheckboxes() {
+                        for (var i = 0; i < checks.length; i++)
+                            if (checks[i].type == "checkbox")
+                                if (checks[i].checked)
+                                    return true;
+                        return false;
+                }
+    
+                async function startInference() {
+    
+                    let checked = testCheckboxes()
+    
+                    if (checked) {
+    
+                    try {
+                        // create a new session and load the model.
+                        
+                        const session = await ort.InferenceSession.create('./model.onnx');
+    
+                        const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
+                        const feeds = { float_input: input };
+    
+                        // feed inputs and run
+    
+                        const results = await session.run(feeds);
+    
+                        // read from results
+                        alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
+    
+                    } catch (e) {
+                        console.log(`failed to inference ONNX model: ${e}.`);
+                    }
+                }
+                else alert("Please check an ingredient")
+                    
+                }
+        init();
+               
+            </script>
+    ```
+
+이 코드에서, 몇가지 해프닝이 생깁니다:
+
+1. 체크박스 요소가 체크되었는 지에 따라, 추론해서 모델로 보낼 380개 가능한 값(1 또는 0)의 배열을 만듭니다.
+2. 체크박스의 배열과 애플리케이션을 시작하며 불렀던 `init` 함수에서 체크되었는 지 확인할 방식을 만들었습니다. 체크박스를 체크하면, 고른 재료를 반영할 수 있게 `ingredients` 배열이 변경됩니다.
+3. 모든 체크박스를 체크했는지 확인하는 `testCheckboxes` 함수를 만들었습니다.
+4. 버튼을 누르면 이 함수를 사용하고, 만약 모든 체크박스가 체크되어 있다면, 추론하기 시작합니다.
+5. 추론 루틴에 포함됩니다:
+   1. 모델의 비동기 로드 세팅하기
+   2. 모델로 보낼 Tensor 구조 만들기
+   3. 모델을 훈련할 때 만들었던 입력 `float_input`을 반영하는 'feeds' 만들기 (Netron으로 이름을 확인할 수 있습니다)
+   4. 모델로 'feeds'를 보내고 응답 기다리기
+
+## 애플리케이션 테스트하기
+
+index.html 파일의 폴더에서 Visual Studio Code로 터미널 세션을 엽니다. 전역적으로 [http-server](https://www.npmjs.com/package/http-server)를 설치했는지 확인하고, 프롬프트에 `http-server`를 타이핑합니다. 로컬 호스트로 열고 웹 앱을 볼 수 있습니다. 여러 재료를 기반으로 추천된 요리를 확인합니다:
+
+![ingredient web app](../images/web-app.png)
+
+축하드립니다, 약간의 필드로 'recommendation' 웹 앱을 만들었습니다. 시간을 조금 내어 이 시스템을 만들어봅니다!
+
+## 🚀 도전
+
+이 웹 앱은 매우 작아서, [ingredient_indexes](../../data/ingredient_indexes.csv) 데이터에서 성분과 인덱스로 계속 만듭니다. 주어진 국민 요리를 만드려면 어떤 풍미 조합으로 작업해야 되나요?
+
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
+
+## 검토 & 자기주도 학습
+
+이 강의에서 식품 재료에 대한 recommendation 시스템 구축의 유용함을 다뤘지만, ML 애플리케이션 영역은 예제가 매우 많습니다. 이 시스템이 어떻게 만들어졌는지 읽어봅니다:
+
+- https://www.sciencedirect.com/topics/computer-science/recommendation-engine
+- https://www.technologyreview.com/2014/08/25/171547/the-ultimate-challenge-for-recommendation-engines/
+- https://www.technologyreview.com/2015/03/23/168831/everything-is-a-recommendation/
+
+## 과제 
+
+[Build a new recommender](../assignment.md)
diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
new file mode 100644
index 000000000..d3418e9ab
--- /dev/null
+++ b/4-Classification/4-Applied/translations/README.tr.md
@@ -0,0 +1,336 @@
+# Mutfak Önerici Bir Web Uygulaması Oluşturun
+
+Bu derste, önceki derslerde öğrendiğiniz bazı yöntemleri kullanarak, bu seri boyunca kullanılan leziz mutfak veri setiyle bir sınıflandırma modeli oluşturacaksınız. Ayrıca, kaydettiğiniz modeli kullanmak üzere, Onnx'un web çalışma zamanından yararlanan küçük bir web uygulaması oluşturacaksınız.
+
+Makine öğreniminin en faydalı pratik kullanımlarından biri, önerici/tavsiyeci sistemler oluşturmaktır ve bu yöndeki ilk adımınızı bugün atabilirsiniz!
+
+[![Önerici Sistemler Tanıtımı](https://img.youtube.com/vi/giIXNoiqO_U/0.jpg)](https://youtu.be/giIXNoiqO_U "Recommendation Systems Introduction")
+
+> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın: Andrew Ng introduces recommendation system design (Andrew Ng önerici sistem tasarımını tanıtıyor)
+
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=tr)
+
+Bu derste şunları öğreneceksiniz:
+
+- Bir model nasıl oluşturulur ve Onnx modeli olarak kaydedilir
+- Modeli denetlemek için Netron nasıl kullanılır
+- Modeliniz çıkarım için bir web uygulamasında nasıl kullanılabilir
+
+## Modelinizi oluşturun
+
+Uygulamalı Makine Öğrenimi sistemleri oluşturmak, bu teknolojilerden kendi iş sistemleriniz için yararlanmanızın önemli bir parçasıdır. Onnx kullanarak modelleri kendi web uygulamalarınız içerisinde kullanabilirsiniz (Böylece gerektiğinde çevrim dışı bir içerikte kullanabilirsiniz.).
+
+[Önceki bir derste](../../../3-Web-App/1-Web-App/README.md) UFO gözlemleriyle ilgili bir Regresyon modeli oluşturmuş, "pickle" kullanmış ve bir Flask uygulamasında kullanmıştınız. Bu mimariyi bilmek çok faydalıdır, ancak bu tam yığın Python uygulamasıdır ve bir JavaScript uygulaması kullanımı gerekebilir.
+
+Bu derste, çıkarım için temel JavaScript tabanlı bir sistem oluşturabilirsiniz. Ancak öncelikle, bir model eğitmeniz ve Onnx ile kullanım için dönüştürmeniz gerekmektedir.
+
+## Alıştırma - sınıflandırma modelini eğitin
+
+Öncelikle, kullandığımız temiz mutfak veri setini kullanarak bir sınıflandırma modeli eğitin.
+
+1. Faydalı kütüphaneler almakla başlayın:
+
+    ```python
+    !pip install skl2onnx
+    import pandas as pd 
+    ```
+
+    Scikit-learn modelinizi Onnx biçimine dönüştürmeyi sağlamak için '[skl2onnx](https://onnx.ai/sklearn-onnx/)'a ihtiyacınız var.
+
+1. Sonra, önceki derslerde yaptığınız şekilde, `read_csv()` kullanarak bir CSV dosyasını okuyarak veriniz üzerinde çalışın:
+
+    ```python
+    data = pd.read_csv('../data/cleaned_cuisines.csv')
+    data.head()
+    ```
+
+1. İlk iki gereksiz sütunu kaldırın ve geriye kalan veriyi 'X' olarak kaydedin:
+
+    ```python
+    X = data.iloc[:,2:]
+    X.head()
+    ```
+
+1. Etiketleri 'y' olarak kaydedin:
+
+    ```python
+    y = data[['cuisine']]
+    y.head()
+    
+    ```
+
+### Eğitme rutinine başlayın
+
+İyi doğruluğu olan 'SVC' kütüphanesini kullanacağız.
+
+1. Scikit-learn'den uygun kütüphaneleri alın:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.svm import SVC
+    from sklearn.model_selection import cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
+    ```
+
+1. Eğitme ve sınama kümelerini ayırın:
+
+    ```python
+    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
+    ```
+
+1. Önceki derste yaptığınız gibi bir SVC Sınıflandırma modeli oluşturun:
+
+    ```python
+    model = SVC(kernel='linear', C=10, probability=True,random_state=0)
+    model.fit(X_train,y_train.values.ravel())
+    ```
+
+1. Şimdi, `predict()` fonksiyonunu çağırarak modelinizi sınayın:
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+1. Modelin kalitesini kontrol etmek için bir sınıflandırma raporu bastırın:
+
+    ```python
+    print(classification_report(y_test,y_pred))
+    ```
+
+    Daha önce de gördüğümüz gibi, doğruluk iyi:
+
+    ```output
+                    precision    recall  f1-score   support
+    
+         chinese       0.72      0.69      0.70       257
+          indian       0.91      0.87      0.89       243
+        japanese       0.79      0.77      0.78       239
+          korean       0.83      0.79      0.81       236
+            thai       0.72      0.84      0.78       224
+    
+        accuracy                           0.79      1199
+       macro avg       0.79      0.79      0.79      1199
+    weighted avg       0.79      0.79      0.79      1199
+    ```
+
+### Modelinizi Onnx'a dönüştürün
+
+Dönüştürmeyi uygun Tensor sayısıyla yaptığınıza emin olun. Bu veri seti listelenmiş 380 malzeme içeriyor, dolayısıyla bu sayıyı `FloatTensorType` içinde belirtmeniz gerekiyor:
+
+1. 380 tensor sayısını kullanarak dönüştürün.
+
+    ```python
+    from skl2onnx import convert_sklearn
+    from skl2onnx.common.data_types import FloatTensorType
+    
+    initial_type = [('float_input', FloatTensorType([None, 380]))]
+    options = {id(model): {'nocl': True, 'zipmap': False}}
+    ```
+
+1. onx'u oluşturun ve **model.onnx** diye bir dosya olarak kaydedin:
+
+    ```python
+    onx = convert_sklearn(model, initial_types=initial_type, options=options)
+    with open("./model.onnx", "wb") as f:
+        f.write(onx.SerializeToString())
+    ```
+
+    > Not olarak, dönüştürme senaryonuzda [seçenekler](https://onnx.ai/sklearn-onnx/parameterized.html) geçirebilirsiniz. Biz bu durumda, 'nocl' parametresini True ve 'zipmap' parametresini 'False' olarak geçirdik. Bu bir sınıflandırma modeli olduğundan, bir sözlük listesi üreten (gerekli değil) ZipMap'i kaldırma seçeneğiniz var. `nocl`, modelde sınıf bilgisinin barındırılmasını ifade eder. `nocl` parametresini 'True' olarak ayarlayarak modelinizin boyutunu küçültün.
+
+Tüm not defterini çalıştırmak şimdi bir Onnx modeli oluşturacak ve bu klasöre kaydedecek.
+
+## Modelinizi inceleyin
+
+Onnx modelleri Visual Studio code'da pek görünür değiller ama birçok araştırmacının modelin doğru oluştuğundan emin olmak üzere modeli görselleştirmek için kullandığı çok iyi bir yazılım var. [Netron](https://github.com/lutzroeder/Netron)'u indirin ve model.onnx dosyanızı açın. 380 girdisi ve sınıflandırıcısıyla basit modelinizin görselleştirildiğini görebilirsiniz:
+
+![Netron görseli](../images/netron.png)
+
+Netron, modellerinizi incelemek için faydalı bir araçtır.
+
+Şimdi, bu düzenli modeli web uygulamanızda kullanmak için hazırsınız. Buzdolabınıza baktığınızda ve verilen bir mutfak için artık malzemelerin hangi birleşimini kullanabileceğinizi bulmayı denediğinizde kullanışlı olacak bir uygulama oluşturalım. Bu birleşim modeliniz tarafından belirlenecek.
+
+## Önerici bir web uygulaması oluşturun
+
+Modelinizi doğrudan bir web uygulamasında kullanabilirsiniz. Bu mimari, modelinizi yerelde ve hatta gerektiğinde çevrim dışı çalıştırabilmenizi de sağlar. `model.onnx` dosyanızı kaydettiğiniz klasörde `index.html` dosyasını oluşturarak başlayın.
+
+1. Bu _index.html_ dosyasında aşağıdaki işaretlemeyi ekleyin:
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+        <header>
+            <title>Cuisine Matcher</title>
+        </header>
+        <body>
+            ...
+        </body>
+    </html>
+    ```
+
+1. Şimdi, `body` etiketleri içinde çalışarak, bazı malzemeleri ifade eden bir onay kutusu listesi göstermek için küçük bir işaretleme ekleyin:
+
+    ```html
+    <h1>Check your refrigerator. What can you create?</h1>
+            <div id="wrapper">
+                <div class="boxCont">
+                    <input type="checkbox" value="4" class="checkbox">
+                    <label>apple</label>
+                </div>
+            
+                <div class="boxCont">
+                    <input type="checkbox" value="247" class="checkbox">
+                    <label>pear</label>
+                </div>
+            
+                <div class="boxCont">
+                    <input type="checkbox" value="77" class="checkbox">
+                    <label>cherry</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="126" class="checkbox">
+                    <label>fenugreek</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="302" class="checkbox">
+                    <label>sake</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="327" class="checkbox">
+                    <label>soy sauce</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="112" class="checkbox">
+                    <label>cumin</label>
+                </div>
+            </div>
+            <div style="padding-top:10px">
+                <button onClick="startInference()">What kind of cuisine can you make?</button>
+            </div> 
+    ```
+
+    Her bir onay kutusuna bir değer verildiğine dikkat edin. Bu, veri setine göre malzemenin bulunduğu indexi ifade eder. Örneğin bu alfabetik listede elma beşinci sütundadır, dolayısıyla onun değeri '4'tür çünkü saymaya 0'dan başlıyoruz. Verilen malzemenin indexini görmek için [malzemeler tablosuna](../../data/ingredient_indexes.csv) başvurabilirsiniz.
+
+    index.html dosyasındaki işinize devam ederek, son `</div>` kapamasından sonra modelinizin çağrılacağı bir script bloğu ekleyin.
+
+1. Öncelikle, [Onnx Runtime](https://www.onnxruntime.ai/) alın:
+
+    ```html
+    <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script> 
+    ```
+
+    > Onnx Runtime, Onnx modelinizin, eniyileştirmeler ve kullanmak için bir API da dahil olmak üzere, geniş bir donanım platform yelpazesinde çalışmasını sağlamak için kullanılır.
+
+1. Runtime uygun hale geldiğinde, onu çağırabilirsiniz:
+
+    ```javascript
+    <script>
+                const ingredients = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+                
+                const checks = [].slice.call(document.querySelectorAll('.checkbox'));
+    
+                // use an async context to call onnxruntime functions.
+                function init() {
+                    
+                    checks.forEach(function (checkbox, index) {
+                        checkbox.onchange = function () {
+                            if (this.checked) {
+                                var index = checkbox.value;
+    
+                                if (index !== -1) {
+                                    ingredients[index] = 1;
+                                }
+                                console.log(ingredients)
+                            }
+                            else {
+                                var index = checkbox.value;
+    
+                                if (index !== -1) {
+                                    ingredients[index] = 0;
+                                }
+                                console.log(ingredients)
+                            }
+                        }
+                    })
+                }
+    
+                function testCheckboxes() {
+                        for (var i = 0; i < checks.length; i++)
+                            if (checks[i].type == "checkbox")
+                                if (checks[i].checked)
+                                    return true;
+                        return false;
+                }
+    
+                async function startInference() {
+    
+                    let checked = testCheckboxes()
+    
+                    if (checked) {
+    
+                    try {
+                        // create a new session and load the model.
+                        
+                        const session = await ort.InferenceSession.create('./model.onnx');
+    
+                        const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
+                        const feeds = { float_input: input };
+    
+                        // feed inputs and run
+    
+                        const results = await session.run(feeds);
+    
+                        // read from results
+                        alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
+    
+                    } catch (e) {
+                        console.log(`failed to inference ONNX model: ${e}.`);
+                    }
+                }
+                else alert("Please check an ingredient")
+                    
+                }
+        init();
+               
+            </script>
+    ```
+
+Bu kodda birçok şey gerçekleşiyor:
+
+1. Ayarlanması ve çıkarım için modele gönderilmesi için, bir malzeme onay kutusunun işaretli olup olmadığına bağlı 380 muhtemel değerden (ya 1 ya da 0) oluşan bir dizi oluşturdunuz.
+2. Onay kutularından oluşan bir dizi ve uygulama başladığında çağrılan bir `init` fonksiyonunda işaretli olup olmadıklarını belirleme yolu oluşturdunuz. Eğer onay kutusu işaretliyse, `ingredients` dizisi, seçilen malzemeyi ifade etmek üzere değiştirilir.
+3. Herhangi bir onay kutusunun işaretli olup olmadığını kontrol eden bir `testCheckboxes` fonksiyonu oluşturdunuz.
+4. Düğmeye basıldığında o fonksiyonu kullanıyor ve eğer herhangi bir onay kutusu işaretlenmişse çıkarıma başlıyorsunuz.
+5. Çıkarım rutini şunları içerir:
+   1. Makinenin eşzamansız bir yüklemesini ayarlama
+   2. Modele göndermek için bir Tensor yapısı oluşturma
+   3. Modelinizi eğitirken oluşturduğunuz `float_input` (Bu adı doğrulamak için Netron kullanabilirsiniz.) girdisini ifade eden 'feeds' oluşturma
+   4. Bu 'feeds'i modele gönderme ve yanıt için bekleme
+
+## Uygulamanızı test edin
+
+index.html dosyanızın olduğu klasördeyken Visual Studio Code'da bir terminal açın. Global kapsamda [http-server](https://www.npmjs.com/package/http-server) indirilmiş olduğundan emin olun ve istemde `http-server` yazın. Bir yerel ana makine açılmalı ve web uygulamanızı görebilirsiniz. Çeşitli malzemeleri baz alarak hangi mutfağın önerildiğine bakın:
+
+![malzeme web uygulaması](../images/web-app.png)
+
+Tebrikler, birkaç değişkenle bir 'önerici' web uygulaması oluşturdunuz! Bu sistemi oluşturmak için biraz zaman ayırın!
+## :rocket: Meydan okuma
+
+Web uygulamanız çok minimal, bu yüzden [ingredient_indexes](../../data/ingredient_indexes.csv) verisinden malzemeleri ve indexlerini kullanarak web uygulamanızı oluşturmaya devam edin. Verilen bir ulusal yemeği yapmak için hangi tat birleşimleri işe yarıyor?
+
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=tr)
+
+## Gözden Geçirme & Kendi Kendine Çalışma
+
+Bu dersin sadece yemek malzemeleri için bir öneri sistemi oluşturmanın olanaklarına değinmesiyle beraber, makine öğrenimi uygulamalarının bu alanı örnekler açısından çok zengin. Bu sistemlerin nasıl oluşturulduğu hakkında biraz daha okuyun:
+
+- https://www.sciencedirect.com/topics/computer-science/recommendation-engine
+- https://www.technologyreview.com/2014/08/25/171547/the-ultimate-challenge-for-recommendation-engines/
+- https://www.technologyreview.com/2015/03/23/168831/everything-is-a-recommendation/
+
+## Ödev 
+
+[Yeni bir önerici oluşturun](assignment.tr.md)
diff --git a/4-Classification/4-Applied/translations/assignment.it.md b/4-Classification/4-Applied/translations/assignment.it.md
new file mode 100644
index 000000000..cc926c727
--- /dev/null
+++ b/4-Classification/4-Applied/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Creare un sistema di raccomandazione
+
+## Istruzioni
+
+Dati gli esercizi di questa lezione, ora si conosce come creare un'app Web basata su JavaScript utilizzando Onnx Runtime e un modello Onnx convertito. Sperimentare con la creazione di un nuovo sistema di raccomandazione utilizzando i dati di queste lezioni o provenienti da altre parti (citare le fonti, per favore). Si potrebbe creare un sistema di raccomandazione di animali domestici in base a vari attributi della personalità o un sistema di raccomandazione di genere musicale basato sull'umore di una persona. Dare sfogo alla creatività!
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ---------------------------------------------------------------------- | ------------------------------------- | --------------------------------- |
+|          | Vengono presentati un'app Web e un notebook, entrambi ben documentati e funzionanti | Uno di quei due è mancante o difettoso | Entrambi sono mancanti o difettosi |
diff --git a/4-Classification/4-Applied/translations/assignment.tr.md b/4-Classification/4-Applied/translations/assignment.tr.md
new file mode 100644
index 000000000..f561bf48a
--- /dev/null
+++ b/4-Classification/4-Applied/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Bir önerici oluşturun
+
+## Yönergeler
+
+Bu dersteki alıştırmalar göz önünde bulundurulursa, Onnx Runtime ve dönüştürülmüş bir Onnx modeli kullanarak JavaScript tabanlı web uygulamasının nasıl oluşturulacağını artık biliyorsunuz. Bu derslerdeki verileri veya başka bir yerden kaynaklandırılmış verileri (Lütfen kaynakça verin.) kullanarak yeni bir önerici oluşturma deneyimi kazanın. Verilen çeşitli kişilik özellikleriyle bir evcil hayvan önericisi veya kişinin ruh haline göre bir müzik türü önericisi oluşturabilirsiniz. Yaratıcı olun!
+
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                               | Yeterli                               | Geliştirme Gerekli                |
+| -------- | ---------------------------------------------------------------------- | ------------------------------------- | --------------------------------- |
+|          | İyi belgelenen ve çalışan bir web uygulaması ve not defteri sunulmuş   | İkisinden biri eksik veya kusurlu     | İkisi ya eksik ya da kusurlu      |
\ No newline at end of file
diff --git a/4-Classification/README.md b/4-Classification/README.md
index f6133aa13..14560a561 100644
--- a/4-Classification/README.md
+++ b/4-Classification/README.md
@@ -1,4 +1,5 @@
 # Getting started with classification
+
 ## Regional topic: Delicious Asian and Indian Cuisines 🍜
 
 In Asia and India, food traditions are extremely diverse, and very delicious! Let's look at data about regional cuisines to try to understand their ingredients.
@@ -8,7 +9,7 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le
   
 ## What you will learn
 
-In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data.
+In this section, you will build on the skills you learned in the first part of this curriculum all about regression to learn about other classifiers you can use that will help you learn about your data.
 
 > There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 
@@ -18,8 +19,9 @@ In this section, you will build on the skills you learned in the first part of t
 2. [More classifiers](2-Classifiers-1/README.md)
 3. [Yet other classifiers](3-Classifiers-2/README.md)
 4. [Applied ML: build a web app](4-Applied/README.md)
+
 ## Credits
 
 "Getting started with classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper)
 
-The delicious cuisines dataset was sourced from [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
+The delicious cuisines dataset was sourced from [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines).
diff --git a/4-Classification/data/cleaned_cuisine.csv b/4-Classification/data/cleaned_cuisines.csv
similarity index 100%
rename from 4-Classification/data/cleaned_cuisine.csv
rename to 4-Classification/data/cleaned_cuisines.csv
diff --git a/4-Classification/data/cleaned_cuisines_R.csv b/4-Classification/data/cleaned_cuisines_R.csv
new file mode 100644
index 000000000..797fd8fed
--- /dev/null
+++ b/4-Classification/data/cleaned_cuisines_R.csv
@@ -0,0 +1,3996 @@
+cuisine,almond,angelica,anise,anise_seed,apple,apple_brandy,apricot,armagnac,artemisia,artichoke,asparagus,avocado,bacon,baked_potato,balm,banana,barley,bartlett_pear,basil,bay,bean,beech,beef,beef_broth,beef_liver,beer,beet,bell_pepper,bergamot,berry,bitter_orange,black_bean,black_currant,black_mustard_seed_oil,black_pepper,black_raspberry,black_sesame_seed,black_tea,blackberry,blackberry_brandy,blue_cheese,blueberry,bone_oil,bourbon_whiskey,brandy,brassica,bread,broccoli,brown_rice,brussels_sprout,buckwheat,butter,buttermilk,cabbage,cabernet_sauvignon_wine,cacao,camembert_cheese,cane_molasses,caraway,cardamom,carnation,carob,carrot,cashew,cassava,catfish,cauliflower,caviar,cayenne,celery,celery_oil,cereal,chamomile,champagne_wine,chayote,cheddar_cheese,cheese,cherry,cherry_brandy,chervil,chicken,chicken_broth,chicken_liver,chickpea,chicory,chinese_cabbage,chive,cider,cilantro,cinnamon,citrus,citrus_peel,clam,clove,cocoa,coconut,coconut_oil,cod,coffee,cognac,concord_grape,condiment,coriander,corn,corn_flake,corn_grit,cottage_cheese,crab,cranberry,cream,cream_cheese,cucumber,cumin,cured_pork,currant,date,dill,durian,eel,egg,egg_noodle,elderberry,emmental_cheese,endive,enokidake,fennel,fenugreek,feta_cheese,fig,fish,flower,frankfurter,fruit,galanga,gardenia,gelatin,geranium,gin,goat_cheese,grape,grape_brandy,grape_juice,grapefruit,green_bell_pepper,green_tea,gruyere_cheese,guava,haddock,ham,hazelnut,herring,holy_basil,honey,hop,horseradish,huckleberry,jamaican_rum,japanese_plum,jasmine,jasmine_tea,juniper_berry,kaffir_lime,kale,katsuobushi,kelp,kidney_bean,kiwi,kohlrabi,kumquat,lamb,lard,laurel,lavender,leaf,leek,lemon,lemon_juice,lemon_peel,lemongrass,lentil,lettuce,licorice,lilac_flower_oil,lima_bean,lime,lime_juice,lime_peel_oil,lingonberry,litchi,liver,lobster,long_pepper,lovage,macadamia_nut,macaroni,mace,mackerel,malt,mandarin,mandarin_peel,mango,maple_syrup,marjoram,mate,matsutake,meat,melon,milk,milk_fat,mint,mozzarella_cheese,mung_bean,munster_cheese,muscat_grape,mushroom,mussel,mustard,mutton,nectarine,nira,nut,nutmeg,oat,oatmeal,octopus,okra,olive,olive_oil,onion,orange,orange_flower,orange_juice,orange_peel,oregano,ouzo,oyster,palm,papaya,parmesan_cheese,parsley,parsnip,passion_fruit,pea,peach,peanut,peanut_butter,peanut_oil,pear,pear_brandy,pecan,pelargonium,pepper,peppermint,peppermint_oil,pimenta,pimento,pineapple,pistachio,plum,popcorn,porcini,pork,pork_liver,pork_sausage,port_wine,potato,potato_chip,prawn,prickly_pear,provolone_cheese,pumpkin,quince,radish,raisin,rapeseed,raspberry,raw_beef,red_algae,red_bean,red_kidney_bean,red_wine,rhubarb,roasted_almond,roasted_beef,roasted_hazelnut,roasted_meat,roasted_nut,roasted_peanut,roasted_pecan,roasted_pork,roasted_sesame_seed,romano_cheese,root,roquefort_cheese,rose,rosemary,rum,rutabaga,rye_bread,rye_flour,saffron,sage,sake,salmon,salmon_roe,sassafras,sauerkraut,savory,scallion,scallop,sea_algae,seaweed,seed,sesame_oil,sesame_seed,shallot,sheep_cheese,shellfish,sherry,shiitake,shrimp,smoke,smoked_fish,smoked_salmon,smoked_sausage,sour_cherry,sour_milk,soy_sauce,soybean,soybean_oil,spearmint,squash,squid,star_anise,starch,strawberry,strawberry_jam,strawberry_juice,sturgeon_caviar,sumac,sunflower_oil,sweet_potato,swiss_cheese,tabasco_pepper,tamarind,tangerine,tarragon,tea,tequila,thai_pepper,thyme,tomato,tomato_juice,truffle,tuna,turkey,turmeric,turnip,vanilla,veal,vegetable,vegetable_oil,vinegar,violet,walnut,wasabi,watercress,watermelon,wheat,wheat_bread,whiskey,white_bread,white_wine,whole_grain_wheat_flour,wine,wood,yam,yeast,yogurt,zucchini
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+thai,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8143811237532645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18561887624673545,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8143811237532645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18561887624673545,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8143811237532645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8143811237532645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
diff --git a/4-Classification/translations/README.it.md b/4-Classification/translations/README.it.md
new file mode 100644
index 000000000..1a8e6d4fe
--- /dev/null
+++ b/4-Classification/translations/README.it.md
@@ -0,0 +1,26 @@
+# Iniziare con la classificazione
+
+## Argomento regionale: Deliziose Cucine Asiatiche e Indiane 🍜
+
+In Asia e in India, le tradizioni alimentari sono estremamente diverse e molto deliziose! Si darà un'occhiata ai dati sulle cucine regionali per cercare di capirne gli ingredienti.
+
+![Venditore di cibo tailandese](../images/thai-food.jpg)
+> Foto di <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> su <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Cosa si imparerà
+
+In questa sezione si approfondiranno le abilità sulla regressione apprese nella prima parte di questo programma di studi  per conoscere altri classificatori da poter utilizzare e che aiuteranno a conoscere i propri dati.
+
+> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## Lezioni
+
+1. [Introduzione alla classificazione](../1-Introduction/translations/README.it.md)
+2. [Più classificatori](../2-Classifiers-1/translations/README.it.md)
+3. [Ancora altri classificatori](../3-Classifiers-2/translations/README.it.md)
+4. [Machine Learning applicato: sviluppare un'app web](../4-Applied/translations/README.it.md)
+## Crediti
+
+"Iniziare con la classificazione" è stato scritto con ♥️ da [Cassie Breviu](https://www.twitter.com/cassieview) e [Jen Looper](https://www.twitter.com/jenlooper).
+
+L'insieme di dati sulle deliziose cucine proviene da [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines).
diff --git a/4-Classification/translations/README.ko.md b/4-Classification/translations/README.ko.md
new file mode 100644
index 000000000..9a77657fd
--- /dev/null
+++ b/4-Classification/translations/README.ko.md
@@ -0,0 +1,27 @@
+# classification 시작하기
+
+## 지역 토픽: 맛있는 아시아 및 인도 요리 🍜
+
+아시아와 인도의, 전통 음식은 다양하고, 매우 맛있습니다! 재료를 이해하기 위해서 지역 요리에 대한 데이터를 찾아봅니다.
+
+![Thai food seller](../images/thai-food.jpg)
+> Photo by <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> on <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## 무엇을 배우나요
+
+이 섹션에서, 데이터를 배우며 도음이 될 다른 classifiers을 배우기 위해서 이 커리큘럼 첫 파트에서 배운 regression의 모든 내용을 바탕으로 진행합니다.
+
+> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## 강의
+
+1. [classification 소개하기](../1-Introduction/translations/README.ko.md)
+2. [더 많은 classifiers](../2-Classifiers-1/translations/README.ko.md)
+3. [또 다른 classifiers](../3-Classifiers-2/translations/README.ko.md)
+4. [응용: web app 만들기](../4-Applied/translations/README.ko.md)
+
+## 크레딧
+
+"Getting started with classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper).
+
+맛있는 요리 데이터셋은 [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)에서 가져왔습니다.
diff --git a/4-Classification/translations/README.ru.md b/4-Classification/translations/README.ru.md
index e9359d59a..48583b3cf 100644
--- a/4-Classification/translations/README.ru.md
+++ b/4-Classification/translations/README.ru.md
@@ -3,7 +3,7 @@
 
 В Азии и Индии традиции кухни чрезвычайно разнообразны и очень вкусны! Давайте посмотрим на данные о региональных кухнях, чтобы попытаться понять их состав.
 
-! [Продавец тайской еды](./images/thai-food.jpg)
+![Продавец тайской еды](../images/thai-food.jpg)
 > Фото <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Лишенг Чанг </a> на <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText "> Unsplash </a>
   
 ## Что вы узнаете
@@ -14,12 +14,13 @@
 
 ## Уроки
 
-1. [Введение в классификацию](1-Introduction/README.md)
-2. [Другие классификаторы](2-Classifiers-1/README.md)
-3. [Еще классификаторы](3-Classifiers-2/README.md)
-4. [Прикладное машинное обучение: создание веб-приложения](4-Applied/README.md)
+1. [Введение в классификацию](../1-Introduction/README.md)
+2. [Другие классификаторы](../2-Classifiers-1/README.md)
+3. [Еще классификаторы](../3-Classifiers-2/README.md)
+4. [Прикладное машинное обучение: создание веб-приложения](../4-Applied/README.md)
+
 ## Благодарности
 
 «Начало работы с классификацией» было написано с ♥ ️[Кэсси Бревиу](https://www.twitter.com/cassieview) и [Джен Лупер](https://www.twitter.com/jenlooper)
 
-Набор данных о вкусных блюдах взят из [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
\ No newline at end of file
+Набор данных о вкусных блюдах взят из [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
diff --git a/4-Classification/translations/README.tr.md b/4-Classification/translations/README.tr.md
new file mode 100644
index 000000000..9514dd0ad
--- /dev/null
+++ b/4-Classification/translations/README.tr.md
@@ -0,0 +1,25 @@
+# Sınıflandırmaya başlarken
+## Bölgesel konu: Leziz Asya ve Hint Mutfağı :ramen:
+
+Asya ve Hindistan'da yemek gelenekleri fazlaca çeşitlilik gösterir ve çok lezzetlidir! Malzemelerini anlamaya çalışmak için bölgesel mutfaklar hakkındaki veriye bakalım.
+
+![Taylandlı yemek satıcısı](../images/thai-food.jpg)
+> Fotoğraf <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> tarafından çekilmiştir ve <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>'tadır.
+
+## Öğrenecekleriniz
+
+Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk bölümünde öğrendiğiniz becerilere dayanıp onların üstüne beceriler ekleyeceksiniz ve veriniz hakkında bilgi sahibi olmanızı sağlayacak diğer sınıflandırıcıları öğreneceksiniz.
+
+> Sınıflandırma modelleriyle çalışmayı öğrenmenizi sağlayacak faydalı düşük kodlu araçlar vardır. [Bu görev için Azure ML](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)'i deneyin.
+
+## Dersler
+
+1. [Sınıflandırmaya giriş](../1-Introduction/translations/README.tr.md)
+2. [Daha fazla sınıflandırıcı](../2-Classifiers-1/translations/README.tr.md)
+3. [Hatta daha fazla sınıflandırıcı](../3-Classifiers-2/translations/README.tr.md)
+4. [Uygulamalı Makine Öğrenimi: bir web uygulaması oluşturun](../4-Applied/translations/README.tr.md)
+## Katkıda bulunanlar
+
+"Sınıflandırmaya başlarken" [Cassie Breviu](https://www.twitter.com/cassieview) ve [Jen Looper](https://www.twitter.com/jenlooper) tarafından :hearts: ile yazılmıştır.
+
+Leziz mutfak veri seti [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)'dan alınmıştır.
diff --git a/4-Classification/translations/README.zh-cn.md b/4-Classification/translations/README.zh-cn.md
new file mode 100644
index 000000000..a24a411b1
--- /dev/null
+++ b/4-Classification/translations/README.zh-cn.md
@@ -0,0 +1,27 @@
+# 开始学习分类方法
+
+## 地方性的话题：美味的亚洲与印度菜肴 🍜
+
+无论是在亚洲亦或是在印度，饮食风俗在美味无比的同时，又在不同的地区各具特色。让我们来看一些地方美食的数据，并尝试理解一下他们的原料。
+
+![Thai food seller](../images/thai-food.jpg)
+> 图片由 <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> 提供，来自 <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## 你会学到什么
+
+建立在我们之前关于回归问题的讨论基础上，在本小节中，你将继续学习能够帮助你更好地理解数据的各种分类器。
+
+> 这里有一些不太涉及代码，且能帮助你了解如何使用分类模型的小工具。可以试试用 Azure 来完成[这个小任务](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)。
+
+## 课程
+
+1. [介绍分类方法](../1-Introduction/translations/README.zh-cn.md)
+2. [其他分类器](../2-Classifiers-1/translations/README.zh-cn.md)
+3. [更多其他的分类器](../3-Classifiers-2/README.md)
+4. [应用机器学习：做一个网页 APP](../4-Applied/README.md)
+
+## 致谢
+
+“开始学习分类方法”部分由 [Cassie Breviu](https://www.twitter.com/cassieview) 和 [Jen Looper](https://www.twitter.com/jenlooper) 用 ♥️ 写作。
+
+这些美食的数据集来源于 [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)。
diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 8453c4512..c5ef453f5 100644
--- a/5-Clustering/1-Visualize/README.md
+++ b/5-Clustering/1-Visualize/README.md
@@ -5,7 +5,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
 [![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
 
 > 🎥 Click the image above for a video. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
 ### Introduction
 
 [Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
@@ -317,7 +317,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
 
 In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
 
 ## Review & Self Study
 
diff --git a/5-Clustering/1-Visualize/images/correlation.png b/5-Clustering/1-Visualize/images/correlation.png
index ef3affdf0..b61f876c1 100644
Binary files a/5-Clustering/1-Visualize/images/correlation.png and b/5-Clustering/1-Visualize/images/correlation.png differ
diff --git a/5-Clustering/1-Visualize/solution/Julia/README.md b/5-Clustering/1-Visualize/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/5-Clustering/1-Visualize/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
new file mode 100644
index 000000000..d05e800fe
--- /dev/null
+++ b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -0,0 +1,489 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "source": [
+                "## **Nigerian Music scraped from Spotify - an analysis**\r\n",
+                "\r\n",
+                "Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.\r\n",
+                "\r\n",
+                "[**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)\r\n",
+                "\r\n",
+                "### **Introduction**\r\n",
+                "\r\n",
+                "[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.\r\n",
+                "\r\n",
+                "> ✅ Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes 🧦👕👖🩲. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos, like a sock drawer.\r\n",
+                "\r\n",
+                "In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans.\r\n",
+                "\r\n",
+                "✅ Think a minute about how you might have encountered clustering 'in the wild', in a banking, e-commerce, or business setting.\r\n",
+                "\r\n",
+                "> 🎓 Interestingly, cluster analysis originated in the fields of Anthropology and Psychology in the 1930s. Can you imagine how it might have been used?\r\n",
+                "\r\n",
+                "Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.\r\n",
+                "\r\n",
+                "✅ Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it?\r\n",
+                "\r\n",
+                "### Getting started with clustering\r\n",
+                "\r\n",
+                "> 🎓 How we create clusters has a lot to do with how we gather up the data points into groups. Let's unpack some vocabulary:\r\n",
+                ">\r\n",
+                "> 🎓 ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning))\r\n",
+                ">\r\n",
+                "> Transductive inference is derived from observed training cases that map to specific test cases. Inductive inference is derived from training cases that map to general rules which are only then applied to test cases.\r\n",
+                ">\r\n",
+                "> An example: Imagine you have a dataset that is only partially labelled. Some things are 'records', some 'cds', and some are blank. Your job is to provide labels for the blanks. If you choose an inductive approach, you'd train a model looking for 'records' and 'cds', and apply those labels to your unlabeled data. This approach will have trouble classifying things that are actually 'cassettes'. A transductive approach, on the other hand, handles this unknown data more effectively as it works to group similar items together and then applies a label to a group. In this case, clusters might reflect 'round musical things' and 'square musical things'.\r\n",
+                ">\r\n",
+                "> 🎓 ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\r\n",
+                ">\r\n",
+                "> Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either 'flat' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) or 'non-flat' (non-Euclidean) geometrical methods.\r\n",
+                ">\r\n",
+                "> 'Flat' in this context refers to Euclidean geometry (parts of which are taught as 'plane' geometry), and non-flat refers to non-Euclidean geometry. What does geometry have to do with machine learning? Well, as two fields that are rooted in mathematics, there must be a common way to measure distances between points in clusters, and that can be done in a 'flat' or 'non-flat' way, depending on the nature of the data. [Euclidean distances](https://wikipedia.org/wiki/Euclidean_distance) are measured as the length of a line segment between two points. [Non-Euclidean distances](https://wikipedia.org/wiki/Non-Euclidean_geometry) are measured along a curve. If your data, visualized, seems to not exist on a plane, you might need to use a specialized algorithm to handle it.\r\n",
+                "\r\n",
+                "<p >\r\n",
+                "   <img src=\"../../images/flat-nonflat.png\"\r\n",
+                "   width=\"600\"/>\r\n",
+                "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+                "\r\n",
+                "\r\n",
+                "\r\n",
+                "> 🎓 ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\r\n",
+                ">\r\n",
+                "> Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways.\r\n",
+                ">\r\n",
+                "> 🎓 ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering)\r\n",
+                ">\r\n",
+                "> [Constrained Clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) introduces 'semi-supervised' learning into this unsupervised method. The relationships between points are flagged as 'cannot link' or 'must-link' so some rules are forced on the dataset.\r\n",
+                ">\r\n",
+                "> An example: If an algorithm is set free on a batch of unlabelled or semi-labelled data, the clusters it produces may be of poor quality. In the example above, the clusters might group 'round music things' and 'square music things' and 'triangular things' and 'cookies'. If given some constraints, or rules to follow (\"the item must be made of plastic\", \"the item needs to be able to produce music\") this can help 'constrain' the algorithm to make better choices.\r\n",
+                ">\r\n",
+                "> 🎓 'Density'\r\n",
+                ">\r\n",
+                "> Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.\r\n",
+                "\r\n",
+                "Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)\r\n",
+                "\r\n",
+                "### **Clustering algorithms**\r\n",
+                "\r\n",
+                "There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let's discuss some of the major ones:\r\n",
+                "\r\n",
+                "-   **Hierarchical clustering**. If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members' distance to and from other objects. Hierarchical clustering is characterized by repeatedly combining two clusters.\r\n",
+                "\r\n",
+                "\r\n",
+                "<p >\r\n",
+                "   <img src=\"../../images/hierarchical.png\"\r\n",
+                "   width=\"600\"/>\r\n",
+                "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+                "\r\n",
+                "\r\n",
+                "\r\n",
+                "-   **Centroid clustering**. This popular algorithm requires the choice of 'k', or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) is a popular version of centroid clustering which separates a data set into pre-defined K groups. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.\r\n",
+                "\r\n",
+                "<p >\r\n",
+                "   <img src=\"../../images/centroid.png\"\r\n",
+                "   width=\"600\"/>\r\n",
+                "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+                "\r\n",
+                "\r\n",
+                "\r\n",
+                "-   **Distribution-based clustering**. Based in statistical modeling, distribution-based clustering centers on determining the probability that a data point belongs to a cluster, and assigning it accordingly. Gaussian mixture methods belong to this type.\r\n",
+                "\r\n",
+                "-   **Density-based clustering**. Data points are assigned to clusters based on their density, or their grouping around each other. Data points far from the group are considered outliers or noise. DBSCAN, Mean-shift and OPTICS belong to this type of clustering.\r\n",
+                "\r\n",
+                "-   **Grid-based clustering**. For multi-dimensional datasets, a grid is created and the data is divided amongst the grid's cells, thereby creating clusters.\r\n",
+                "\r\n",
+                "The best way to learn about clustering is to try it for yourself, so that's what you'll do in this exercise.\r\n",
+                "\r\n",
+                "We'll require some packages to knock-off this module. You can have them installed as: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\r\n",
+                "\r\n",
+                "Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case some are missing.\r\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+                "\r\n",
+                "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "## Exercise - cluster your data\n",
+                "\n",
+                "Clustering as a technique is greatly aided by proper visualization, so let's get started by visualizing our music data. This exercise will help us decide which of the methods of clustering we should most effectively use for the nature of this data.\n",
+                "\n",
+                "Let's hit the ground running by importing the data.\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Load the core tidyverse and make it available in your current R session\r\n",
+                "library(tidyverse)\r\n",
+                "\r\n",
+                "# Import the data into a tibble\r\n",
+                "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+                "\r\n",
+                "# View the first 5 rows of the data set\r\n",
+                "df %>% \r\n",
+                "  slice_head(n = 5)\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "Sometimes, we may want some little more information on our data. We can have a look at the `data` and `its structure` by using the [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) function:\n",
+                "\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Glimpse into the data set\r\n",
+                "df %>% \r\n",
+                "  glimpse()\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "Good job!💪\n",
+                "\n",
+                "We can observe that `glimpse()` will give you the total number of rows (observations) and columns (variables), then, the first few entries of each variable in a row after the variable name. In addition, the *data type* of the variable is given immediately after each variable's name inside `< >`.\n",
+                "\n",
+                "`DataExplorer::introduce()` can summarize this information neatly:\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Describe basic information for our data\r\n",
+                "df %>% \r\n",
+                "  introduce()\r\n",
+                "\r\n",
+                "# A visual display of the same\r\n",
+                "df %>% \r\n",
+                "  plot_intro()\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "Awesome! We have just learnt that our data has no missing values.\n",
+                "\n",
+                "While we are at it, we can explore common central tendency statistics (e.g [mean](https://en.wikipedia.org/wiki/Arithmetic_mean) and [median](https://en.wikipedia.org/wiki/Median)) and measures of dispersion (e.g [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation)) using `summarytools::descr()`\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Describe common statistics\r\n",
+                "df %>% \r\n",
+                "  descr(stats = \"common\")\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "Let's look at the general values of the data. Note that popularity can be `0`, which show songs that have no ranking. We'll remove those shortly.\n",
+                "\n",
+                "> 🤔 If we are working with clustering, an unsupervised method that does not require labeled data, why are we showing this data with labels? In the data exploration phase, they come in handy, but they are not necessary for the clustering algorithms to work.\n",
+                "\n",
+                "### 1. Explore popular genres\n",
+                "\n",
+                "Let's go ahead and find out the most popular genres 🎶 by making a count of the instances it appears.\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Popular genres\r\n",
+                "top_genres <- df %>% \r\n",
+                "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+                "# Encode to categorical and reorder the according to count\r\n",
+                "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+                "\r\n",
+                "# Print the top genres\r\n",
+                "top_genres\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "That went well! They say a picture is worth a thousand rows of a data frame (actually nobody ever says that 😅). But you get the gist of it, right?\n",
+                "\n",
+                "One way to visualize categorical data (character or factor variables) is using barplots. Let's make a barplot of the top 10 genres:\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Change the default gray theme\r\n",
+                "theme_set(theme_light())\r\n",
+                "\r\n",
+                "# Visualize popular genres\r\n",
+                "top_genres %>%\r\n",
+                "  slice(1:10) %>% \r\n",
+                "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+                "                       fill = artist_top_genre)) +\r\n",
+                "  geom_col(alpha = 0.8) +\r\n",
+                "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+                "  ggtitle(\"Top genres\") +\r\n",
+                "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+                "        # Rotates the X markers (so we can read them)\r\n",
+                "    axis.text.x = element_text(angle = 90))\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "Now it's way easier to identify that we have `missing` genres 🧐!\n",
+                "\n",
+                "> A good visualisation will show you things that you did not expect, or raise new questions about the data - Hadley Wickham and Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+                "\n",
+                "Note, when the top genre is described as `Missing`, that means that Spotify did not classify it, so let's get rid of it.\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Visualize popular genres\r\n",
+                "top_genres %>%\r\n",
+                "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+                "  slice(1:10) %>% \r\n",
+                "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+                "                       fill = artist_top_genre)) +\r\n",
+                "  geom_col(alpha = 0.8) +\r\n",
+                "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+                "  ggtitle(\"Top genres\") +\r\n",
+                "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+                "        # Rotates the X markers (so we can read them)\r\n",
+                "    axis.text.x = element_text(angle = 90))\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "From the little data exploration, we learn that the top three genres dominate this dataset. Let's concentrate on `afro dancehall`, `afropop`, and `nigerian pop`, additionally filter the dataset to remove anything with a 0 popularity value (meaning it was not classified with a popularity in the dataset and can be considered noise for our purposes):\n",
+                "\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "nigerian_songs <- df %>% \r\n",
+                "  # Concentrate on top 3 genres\r\n",
+                "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+                "  # Remove unclassified observations\r\n",
+                "  filter(popularity != 0)\r\n",
+                "\r\n",
+                "\r\n",
+                "\r\n",
+                "# Visualize popular genres\r\n",
+                "nigerian_songs %>%\r\n",
+                "  count(artist_top_genre) %>%\r\n",
+                "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+                "                       fill = artist_top_genre)) +\r\n",
+                "  geom_col(alpha = 0.8) +\r\n",
+                "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+                "  ggtitle(\"Top genres\") +\r\n",
+                "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "Let's see whether there is any apparent linear relationship among the numerical variables in our data set. This relationship is quantified mathematically by the [correlation statistic](https://en.wikipedia.org/wiki/Correlation).\n",
+                "\n",
+                "The correlation statistic is a value between -1 and 1 that indicates the strength of a relationship. Values above 0 indicate a *positive* correlation (high values of one variable tend to coincide with high values of the other), while values below 0 indicate a *negative* correlation (high values of one variable tend to coincide with low values of the other).\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Narrow down to numeric variables and fid correlation\r\n",
+                "corr_mat <- nigerian_songs %>% \r\n",
+                "  select(where(is.numeric)) %>% \r\n",
+                "  cor()\r\n",
+                "\r\n",
+                "# Visualize correlation matrix\r\n",
+                "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "The data is not strongly correlated except between `energy` and `loudness`, which makes sense, given that loud music is usually pretty energetic. `Popularity` has a correspondence to `release date`, which also makes sense, as more recent songs are probably more popular. Length and energy seem to have a correlation too.\n",
+                "\n",
+                "It will be interesting to see what a clustering algorithm can make of this data!\n",
+                "\n",
+                "> 🎓 Note that correlation does not imply causation! We have proof of correlation but no proof of causation. An [amusing web site](https://tylervigen.com/spurious-correlations) has some visuals that emphasize this point.\n",
+                "\n",
+                "### 2. Explore data distribution\n",
+                "\n",
+                "Let's ask some more subtle questions. Are the genres significantly different in the perception of their danceability, based on their popularity? Let's examine our top three genres data distribution for popularity and danceability along a given x and y axis using [density plots](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# Perform 2D kernel density estimation\r\n",
+                "density_estimate_2d <- nigerian_songs %>% \r\n",
+                "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+                "  geom_density_2d(bins = 5, size = 1) +\r\n",
+                "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+                "  xlim(-20, 80) +\r\n",
+                "  ylim(0, 1.2)\r\n",
+                "\r\n",
+                "# Density plot based on the popularity\r\n",
+                "density_estimate_pop <- nigerian_songs %>% \r\n",
+                "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+                "  geom_density(size = 1, alpha = 0.5) +\r\n",
+                "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+                "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+                "  theme(legend.position = \"none\")\r\n",
+                "\r\n",
+                "# Density plot based on the danceability\r\n",
+                "density_estimate_dance <- nigerian_songs %>% \r\n",
+                "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+                "  geom_density(size = 1, alpha = 0.5) +\r\n",
+                "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+                "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+                "\r\n",
+                "\r\n",
+                "# Patch everything together\r\n",
+                "library(patchwork)\r\n",
+                "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "We see that there are concentric circles that line up, regardless of genre. Could it be that Nigerian tastes converge at a certain level of danceability for this genre?\n",
+                "\n",
+                "In general, the three genres align in terms of their popularity and danceability. Determining clusters in this loosely-aligned data will be a challenge. Let's see whether a scatter plot can support this.\n"
+            ],
+            "metadata": {}
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "source": [
+                "# A scatter plot of popularity and danceability\r\n",
+                "scatter_plot <- nigerian_songs %>% \r\n",
+                "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+                "  geom_point(size = 2, alpha = 0.8) +\r\n",
+                "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+                "\r\n",
+                "# Add a touch of interactivity\r\n",
+                "ggplotly(scatter_plot)\r\n"
+            ],
+            "outputs": [],
+            "metadata": {}
+        },
+        {
+            "cell_type": "markdown",
+            "source": [
+                "A scatterplot of the same axes shows a similar pattern of convergence.\n",
+                "\n",
+                "In general, for clustering, you can use scatterplots to show clusters of data, so mastering this type of visualization is very useful. In the next lesson, we will take this filtered data and use k-means clustering to discover groups in this data that see to overlap in interesting ways.\n",
+                "\n",
+                "##  **🚀 Challenge**\n",
+                "\n",
+                "In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?\n",
+                "\n",
+                "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)\n",
+                "\n",
+                "## **Review & Self Study**\n",
+                "\n",
+                "Before you apply clustering algorithms, as we have learned, it's a good idea to understand the nature of your dataset. Read more on this topic [here](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)\n",
+                "\n",
+                "Deepen your understanding of clustering techniques:\n",
+                "\n",
+                "-   [Train and Evaluate Clustering Models using Tidymodels and friends](https://rpubs.com/eR_ic/clustering)\n",
+                "\n",
+                "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+                "\n",
+                "## **Assignment**\n",
+                "\n",
+                "[Research other visualizations for clustering](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+                "\n",
+                "## THANK YOU TO:\n",
+                "\n",
+                "[Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\n",
+                "\n",
+                "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) for creating the amazing illustrations that make machine learning concepts more interpretable and easier to understand.\n",
+                "\n",
+                "Happy Learning,\n",
+                "\n",
+                "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n"
+            ],
+            "metadata": {}
+        }
+    ],
+    "metadata": {
+        "anaconda-cloud": "",
+        "kernelspec": {
+            "display_name": "R",
+            "langauge": "R",
+            "name": "ir"
+        },
+        "language_info": {
+            "codemirror_mode": "r",
+            "file_extension": ".r",
+            "mimetype": "text/x-r-source",
+            "name": "R",
+            "pygments_lexer": "r",
+            "version": "3.4.1"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
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+++ b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
@@ -0,0 +1,342 @@
+---
+title: 'Introduction to clustering: Clean, prep and visualize your data'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## **Nigerian Music scraped from Spotify - an analysis**
+
+Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.
+
+[**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+
+### **Introduction**
+
+[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
+
+> ✅ Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes 🧦👕👖🩲. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos, like a sock drawer.
+
+In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans.
+
+✅ Think a minute about how you might have encountered clustering 'in the wild', in a banking, e-commerce, or business setting.
+
+> 🎓 Interestingly, cluster analysis originated in the fields of Anthropology and Psychology in the 1930s. Can you imagine how it might have been used?
+
+Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.
+
+✅ Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it?
+
+### Getting started with clustering
+
+> 🎓 How we create clusters has a lot to do with how we gather up the data points into groups. Let's unpack some vocabulary:
+>
+> 🎓 ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning))
+>
+> Transductive inference is derived from observed training cases that map to specific test cases. Inductive inference is derived from training cases that map to general rules which are only then applied to test cases.
+>
+> An example: Imagine you have a dataset that is only partially labelled. Some things are 'records', some 'cds', and some are blank. Your job is to provide labels for the blanks. If you choose an inductive approach, you'd train a model looking for 'records' and 'cds', and apply those labels to your unlabeled data. This approach will have trouble classifying things that are actually 'cassettes'. A transductive approach, on the other hand, handles this unknown data more effectively as it works to group similar items together and then applies a label to a group. In this case, clusters might reflect 'round musical things' and 'square musical things'.
+>
+> 🎓 ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)
+>
+> Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either 'flat' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) or 'non-flat' (non-Euclidean) geometrical methods.
+>
+> 'Flat' in this context refers to Euclidean geometry (parts of which are taught as 'plane' geometry), and non-flat refers to non-Euclidean geometry. What does geometry have to do with machine learning? Well, as two fields that are rooted in mathematics, there must be a common way to measure distances between points in clusters, and that can be done in a 'flat' or 'non-flat' way, depending on the nature of the data. [Euclidean distances](https://wikipedia.org/wiki/Euclidean_distance) are measured as the length of a line segment between two points. [Non-Euclidean distances](https://wikipedia.org/wiki/Non-Euclidean_geometry) are measured along a curve. If your data, visualized, seems to not exist on a plane, you might need to use a specialized algorithm to handle it.
+
+![Infographic by Dasani Madipalli](../../images/flat-nonflat.png){width="500"}
+
+> 🎓 ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
+>
+> Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways.
+>
+> 🎓 ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering)
+>
+> [Constrained Clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) introduces 'semi-supervised' learning into this unsupervised method. The relationships between points are flagged as 'cannot link' or 'must-link' so some rules are forced on the dataset.
+>
+> An example: If an algorithm is set free on a batch of unlabelled or semi-labelled data, the clusters it produces may be of poor quality. In the example above, the clusters might group 'round music things' and 'square music things' and 'triangular things' and 'cookies'. If given some constraints, or rules to follow ("the item must be made of plastic", "the item needs to be able to produce music") this can help 'constrain' the algorithm to make better choices.
+>
+> 🎓 'Density'
+>
+> Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.
+
+Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)
+
+### **Clustering algorithms**
+
+There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let's discuss some of the major ones:
+
+-   **Hierarchical clustering**. If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members' distance to and from other objects. Hierarchical clustering is characterized by repeatedly combining two clusters.
+
+![Infographic by Dasani Madipalli](../../images/hierarchical.png){width="500"}
+
+-   **Centroid clustering**. This popular algorithm requires the choice of 'k', or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) is a popular version of centroid clustering which separates a data set into pre-defined K groups. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.![Infographic by Dasani Madipalli](../../images/centroid.png){width="500"}
+
+-   **Distribution-based clustering**. Based in statistical modeling, distribution-based clustering centers on determining the probability that a data point belongs to a cluster, and assigning it accordingly. Gaussian mixture methods belong to this type.
+
+-   **Density-based clustering**. Data points are assigned to clusters based on their density, or their grouping around each other. Data points far from the group are considered outliers or noise. DBSCAN, Mean-shift and OPTICS belong to this type of clustering.
+
+-   **Grid-based clustering**. For multi-dimensional datasets, a grid is created and the data is divided amongst the grid's cells, thereby creating clusters.
+
+The best way to learn about clustering is to try it for yourself, so that's what you'll do in this exercise.
+
+We'll require some packages to knock-off this module. You can have them installed as: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`
+
+Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case some are missing.
+
+```{r}
+suppressWarnings(if(!require("pacman")) install.packages("pacman"))
+
+pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')
+```
+
+```{r setup}
+knitr::opts_chunk$set(warning = F, message = F)
+
+```
+
+## Exercise - cluster your data
+
+Clustering as a technique is greatly aided by proper visualization, so let's get started by visualizing our music data. This exercise will help us decide which of the methods of clustering we should most effectively use for the nature of this data.
+
+Let's hit the ground running by importing the data.
+
+```{r}
+# Load the core tidyverse and make it available in your current R session
+library(tidyverse)
+
+# Import the data into a tibble
+df <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv")
+
+# View the first 5 rows of the data set
+df %>% 
+  slice_head(n = 5)
+
+```
+
+Sometimes, we may want some little more information on our data. We can have a look at the `data` and `its structure` by using the [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) function:
+
+```{r}
+# Glimpse into the data set
+df %>% 
+  glimpse()
+```
+
+Good job!💪
+
+We can observe that `glimpse()` will give you the total number of rows (observations) and columns (variables), then, the first few entries of each variable in a row after the variable name. In addition, the *data type* of the variable is given immediately after each variable's name inside `< >`.
+
+`DataExplorer::introduce()` can summarize this information neatly:
+
+```{r DataExplorer}
+# Describe basic information for our data
+df %>% 
+  introduce()
+
+# A visual display of the same
+df %>% 
+  plot_intro()
+
+```
+
+Awesome! We have just learnt that our data has no missing values.
+
+While we are at it, we can explore common central tendency statistics (e.g [mean](https://en.wikipedia.org/wiki/Arithmetic_mean) and [median](https://en.wikipedia.org/wiki/Median)) and measures of dispersion (e.g [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation)) using `summarytools::descr()`
+
+```{r summarytools}
+# Describe common statistics
+df %>% 
+  descr(stats = "common")
+
+```
+
+Let's look at the general values of the data. Note that popularity can be `0`, which show songs that have no ranking. We'll remove those shortly.
+
+> 🤔 If we are working with clustering, an unsupervised method that does not require labeled data, why are we showing this data with labels? In the data exploration phase, they come in handy, but they are not necessary for the clustering algorithms to work.
+
+### 1. Explore popular genres
+
+Let's go ahead and find out the most popular genres 🎶 by making a count of the instances it appears.
+
+```{r count_genres}
+# Popular genres
+top_genres <- df %>% 
+  count(artist_top_genre, sort = TRUE) %>% 
+# Encode to categorical and reorder the according to count
+  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())
+
+# Print the top genres
+top_genres
+
+```
+
+That went well! They say a picture is worth a thousand rows of a data frame (actually nobody ever says that 😅). But you get the gist of it, right?
+
+One way to visualize categorical data (character or factor variables) is using barplots. Let's make a barplot of the top 10 genres:
+
+```{r bar_plot_genre}
+# Change the default gray theme
+theme_set(theme_light())
+
+# Visualize popular genres
+top_genres %>%
+  slice(1:10) %>% 
+  ggplot(mapping = aes(x = artist_top_genre, y = n,
+                       fill = artist_top_genre)) +
+  geom_col(alpha = 0.8) +
+  paletteer::scale_fill_paletteer_d("rcartocolor::Vivid") +
+  ggtitle("Top genres") +
+  theme(plot.title = element_text(hjust = 0.5),
+        # Rotates the X markers (so we can read them)
+    axis.text.x = element_text(angle = 90))
+```
+
+Now it's way easier to identify that we have `missing` genres 🧐!
+
+> A good visualisation will show you things that you did not expect, or raise new questions about the data - Hadley Wickham and Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)
+
+Note, when the top genre is described as `Missing`, that means that Spotify did not classify it, so let's get rid of it.
+
+```{r remove_missing}
+# Visualize popular genres
+top_genres %>%
+  filter(artist_top_genre != "Missing") %>% 
+  slice(1:10) %>% 
+  ggplot(mapping = aes(x = artist_top_genre, y = n,
+                       fill = artist_top_genre)) +
+  geom_col(alpha = 0.8) +
+  paletteer::scale_fill_paletteer_d("rcartocolor::Vivid") +
+  ggtitle("Top genres") +
+  theme(plot.title = element_text(hjust = 0.5),
+        # Rotates the X markers (so we can read them)
+    axis.text.x = element_text(angle = 90))
+```
+
+From the little data exploration, we learn that the top three genres dominate this dataset. Let's concentrate on `afro dancehall`, `afropop`, and `nigerian pop`, additionally filter the dataset to remove anything with a 0 popularity value (meaning it was not classified with a popularity in the dataset and can be considered noise for our purposes):
+
+```{r new_dataset}
+nigerian_songs <- df %>% 
+  # Concentrate on top 3 genres
+  filter(artist_top_genre %in% c("afro dancehall", "afropop","nigerian pop")) %>% 
+  # Remove unclassified observations
+  filter(popularity != 0)
+
+
+
+# Visualize popular genres
+nigerian_songs %>%
+  count(artist_top_genre) %>%
+  ggplot(mapping = aes(x = artist_top_genre, y = n,
+                       fill = artist_top_genre)) +
+  geom_col(alpha = 0.8) +
+  paletteer::scale_fill_paletteer_d("ggsci::category10_d3") +
+  ggtitle("Top genres") +
+  theme(plot.title = element_text(hjust = 0.5))
+```
+
+Let's see whether there is any apparent linear relationship among the numerical variables in our data set. This relationship is quantified mathematically by the [correlation statistic](https://en.wikipedia.org/wiki/Correlation).
+
+The correlation statistic is a value between -1 and 1 that indicates the strength of a relationship. Values above 0 indicate a *positive* correlation (high values of one variable tend to coincide with high values of the other), while values below 0 indicate a *negative* correlation (high values of one variable tend to coincide with low values of the other).
+
+```{r correlation}
+# Narrow down to numeric variables and fid correlation
+corr_mat <- nigerian_songs %>% 
+  select(where(is.numeric)) %>% 
+  cor()
+
+# Visualize correlation matrix
+corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  
+```
+
+The data is not strongly correlated except between `energy` and `loudness`, which makes sense, given that loud music is usually pretty energetic. `Popularity` has a correspondence to `release date`, which also makes sense, as more recent songs are probably more popular. Length and energy seem to have a correlation too.
+
+It will be interesting to see what a clustering algorithm can make of this data!
+
+> 🎓 Note that correlation does not imply causation! We have proof of correlation but no proof of causation. An [amusing web site](https://tylervigen.com/spurious-correlations) has some visuals that emphasize this point.
+
+### 2. Explore data distribution
+
+Let's ask some more subtle questions. Are the genres significantly different in the perception of their danceability, based on their popularity? Let's examine our top three genres data distribution for popularity and danceability along a given x and y axis using [density plots](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).
+
+```{r}
+# Perform 2D kernel density estimation
+density_estimate_2d <- nigerian_songs %>% 
+  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +
+  geom_density_2d(bins = 5, size = 1) +
+  paletteer::scale_color_paletteer_d("RSkittleBrewer::wildberry") +
+  xlim(-20, 80) +
+  ylim(0, 1.2)
+
+# Density plot based on the popularity
+density_estimate_pop <- nigerian_songs %>% 
+  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +
+  geom_density(size = 1, alpha = 0.5) +
+  paletteer::scale_fill_paletteer_d("RSkittleBrewer::wildberry") +
+  paletteer::scale_color_paletteer_d("RSkittleBrewer::wildberry") +
+  theme(legend.position = "none")
+
+# Density plot based on the danceability
+density_estimate_dance <- nigerian_songs %>% 
+  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +
+  geom_density(size = 1, alpha = 0.5) +
+  paletteer::scale_fill_paletteer_d("RSkittleBrewer::wildberry") +
+  paletteer::scale_color_paletteer_d("RSkittleBrewer::wildberry")
+
+
+# Patch everything together
+library(patchwork)
+density_estimate_2d / (density_estimate_pop + density_estimate_dance)
+```
+
+We see that there are concentric circles that line up, regardless of genre. Could it be that Nigerian tastes converge at a certain level of danceability for this genre?
+
+In general, the three genres align in terms of their popularity and danceability. Determining clusters in this loosely-aligned data will be a challenge. Let's see whether a scatter plot can support this.
+
+```{r scatter_plot}
+# A scatter plot of popularity and danceability
+scatter_plot <- nigerian_songs %>% 
+  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +
+  geom_point(size = 2, alpha = 0.8) +
+  paletteer::scale_color_paletteer_d("futurevisions::mars")
+
+# Add a touch of interactivity
+ggplotly(scatter_plot)
+```
+
+A scatterplot of the same axes shows a similar pattern of convergence.
+
+In general, for clustering, you can use scatterplots to show clusters of data, so mastering this type of visualization is very useful. In the next lesson, we will take this filtered data and use k-means clustering to discover groups in this data that see to overlap in interesting ways.
+
+## **🚀 Challenge**
+
+In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
+
+## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+
+## **Review & Self Study**
+
+Before you apply clustering algorithms, as we have learned, it's a good idea to understand the nature of your dataset. Read more on this topic [here](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
+
+Deepen your understanding of clustering techniques:
+
+-   [Train and Evaluate Clustering Models using Tidymodels and friends](https://rpubs.com/eR_ic/clustering)
+
+-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*
+
+## **Assignment**
+
+[Research other visualizations for clustering](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)
+
+## THANK YOU TO:
+
+[Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️
+
+[`Dasani Madipalli`](https://twitter.com/dasani_decoded) for creating the amazing illustrations that make machine learning concepts more interpretable and easier to understand.
+
+Happy Learning,
+
+[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.
diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md
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@@ -0,0 +1,332 @@
+# Introduzione al clustering
+
+Il clustering è un tipo di [apprendimento non supervisionato](https://wikipedia.org/wiki/Unsupervised_learning) che presuppone che un insieme di dati non sia etichettato o che i suoi input non siano abbinati a output predefiniti. Utilizza vari algoritmi per ordinare i dati non etichettati e fornire raggruppamenti in base ai modelli che individua nei dati.
+
+[![No One Like You di PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You di PSquare")
+
+> 🎥 Fare clic sull'immagine sopra per un video. Mentre si studia machine learning con il clustering, si potranno gradire brani della Nigerian Dance Hall: questa è una canzone molto apprezzata del 2014 di PSquare.
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/?loc=it)
+
+### Introduzione
+
+[Il clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) è molto utile per l'esplorazione dei dati. Si vedrà se può aiutare a scoprire tendenze e modelli nel modo in cui il pubblico nigeriano consuma la musica.
+
+✅ Ci si prenda un minuto per pensare agli usi del clustering. Nella vita reale, il clustering si verifica ogni volta che si ha una pila di biancheria e si devono sistemare i vestiti dei propri familiari 🧦👕👖🩲. Nella scienza dei dati, il clustering si verifica quando si tenta di analizzare le preferenze di un utente o di determinare le caratteristiche di qualsiasi insieme di dati senza etichetta. Il clustering, in un certo senso, aiuta a dare un senso al caos, come un cassetto dei calzini.
+
+[![Introduzione a ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduzione al Clustering")
+
+> 🎥 Fare clic sull'immagine sopra per un video: John Guttag del MIT introduce il clustering
+
+In un ambiente professionale, il clustering può essere utilizzato per determinare cose come la segmentazione del mercato, determinare quali fasce d'età acquistano quali articoli, ad esempio. Un altro uso sarebbe il rilevamento di anomalie, forse per rilevare le frodi da un insieme di dati delle transazioni con carta di credito. Oppure si potrebbe usare il clustering per determinare i tumori in una serie di scansioni mediche.
+
+✅ Si pensi un minuto a come si potrebbe aver incontrato il clustering 'nel mondo reale', in un ambiente bancario, e-commerce o aziendale.
+
+> 🎓 È interessante notare che l'analisi dei cluster ha avuto origine nei campi dell'antropologia e della psicologia negli anni '30. Si riusce a immaginare come potrebbe essere stato utilizzato?
+
+In alternativa, lo si può utilizzare per raggruppare i risultati di ricerca, ad esempio tramite link per acquisti, immagini o recensioni. Il clustering è utile quando si dispone di un insieme di dati di grandi dimensioni che si desidera ridurre e sul quale si desidera eseguire un'analisi più granulare, quindi la tecnica può essere utilizzata per conoscere i dati prima che vengano costruiti altri modelli.
+
+✅ Una volta che i dati sono organizzati in cluster, viene assegnato un ID cluster e questa tecnica può essere utile quando si preserva la privacy di un insieme di dati; si può invece fare riferimento a un punto dati tramite il suo ID cluster, piuttosto che dati identificabili più rivelatori. Si riesce a pensare ad altri motivi per cui fare riferimento a un ID cluster piuttosto che ad altri elementi del cluster per identificarlo?
+
+In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) si approfondirà la propria comprensione delle tecniche di clustering
+
+## Iniziare con il clustering
+
+[Scikit-learn offre una vasta gamma](https://scikit-learn.org/stable/modules/clustering.html) di metodi per eseguire il clustering. Il tipo scelto dipenderà dal caso d'uso. Secondo la documentazione, ogni metodo ha diversi vantaggi. Ecco una tabella semplificata dei metodi supportati da Scikit-learn e dei loro casi d'uso appropriati:
+
+| Nome del metodo                                         | Caso d'uso                                                                  |
+| :------------------------------------------------------ | :-------------------------------------------------------------------------- |
+| K-MEANS                                                 | uso generale, induttivo                                                     |
+| Affinity propagation (Propagazione dell'affinità)       | molti, cluster irregolari, induttivo                                        |
+| Mean-shift (Spostamento medio)                          | molti, cluster irregolari, induttivo                                        |
+| Spectral clustering (Raggruppamento spettrale)          | pochi, anche grappoli, trasduttivi                                          |
+| Ward hierarchical clustering (Cluster gerarchico)       | molti, cluster vincolati, trasduttivi                                       |
+| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive                        |
+| DBSCAN                                                  | geometria non piatta, cluster irregolari, trasduttivo                       |
+| OPTICS                                                  | geometria non piatta, cluster irregolari con densità variabile, trasduttivo |
+| Gaussian mixtures (miscele gaussiane)                   | geometria piana, induttiva                                                  |
+| BIRCH                                                   | insiemi di dati di grandi dimensioni con valori anomali, induttivo          |
+
+> 🎓 Il modo in cui si creno i cluster ha molto a che fare con il modo in cui si raccolgono punti dati in gruppi. Si esamina un po' di vocabolario:
+>
+> 🎓 ['trasduttivo' vs. 'induttivo'](https://wikipedia.org/wiki/Transduction_(machine_learning))
+>
+> L'inferenza trasduttiva è derivata da casi di addestramento osservati che mappano casi di test specifici. L'inferenza induttiva è derivata da casi di addestramento che mappano regole generali che vengono poi applicate ai casi di test.
+>
+> Un esempio: si immagini di avere un insieme di dati che è solo parzialmente etichettato. Alcune cose sono "dischi", alcune "cd" e altre sono vuote. Il compito è fornire etichette per gli spazi vuoti. Se si scegliesse un approccio induttivo, si addestrerebbe un modello alla ricerca di "dischi" e "cd" e si applicherebbero quelle etichette ai dati non etichettati. Questo approccio avrà problemi a classificare cose che sono in realtà "cassette". Un approccio trasduttivo, d'altra parte, gestisce questi dati sconosciuti in modo più efficace poiché funziona raggruppando elementi simili e quindi applica un'etichetta a un gruppo. In questo caso, i cluster potrebbero riflettere "cose musicali rotonde" e "cose musicali quadrate".
+>
+> 🎓 [Geometria 'non piatta' (non-flat) vs. 'piatta' (flat)](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)
+>
+> Derivato dalla terminologia matematica, la geometria non piatta rispetto a quella piatta si riferisce alla misura delle distanze tra i punti mediante metodi geometrici "piatti" ([euclidei](https://wikipedia.org/wiki/Euclidean_geometry)) o "non piatti" (non euclidei).
+>
+> "Piatto" in questo contesto si riferisce alla geometria euclidea (parti della quale vengono insegnate come geometria "piana") e non piatto si riferisce alla geometria non euclidea. Cosa ha a che fare la geometria con machine learning? Bene, come due campi che sono radicati nella matematica, ci deve essere un modo comune per misurare le distanze tra i punti nei cluster, e questo può essere fatto in modo "piatto" o "non piatto", a seconda della natura dei dati . [Le distanze euclidee](https://wikipedia.org/wiki/Euclidean_distance) sono misurate come la lunghezza di un segmento di linea tra due punti. [Le distanze non euclidee](https://wikipedia.org/wiki/Non-Euclidean_geometry) sono misurate lungo una curva. Se i dati, visualizzati, sembrano non esistere su un piano, si potrebbe dover utilizzare un algoritmo specializzato per gestirli.
+>
+![Infografica con geometria piatta e non piatta](../images/flat-nonflat.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+>
+> [' Distanze'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
+>
+> I cluster sono definiti dalla loro matrice di distanza, ad esempio le distanze tra i punti. Questa distanza può essere misurata in alcuni modi. I cluster euclidei sono definiti dalla media dei valori dei punti e contengono un 'centroide' o baricentro. Le distanze sono quindi misurate dalla distanza da quel baricentro. Le distanze non euclidee si riferiscono a "clustroidi", il punto più vicino ad altri punti. I clustroidi a loro volta possono essere definiti in vari modi.
+>
+> 🎓 ['Vincolato'](https://wikipedia.org/wiki/Constrained_clustering)
+>
+> [Constrained Clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) introduce l'apprendimento 'semi-supervisionato' in questo metodo non supervisionato. Le relazioni tra i punti sono contrassegnate come "non è possibile collegare" o "è necessario collegare", quindi alcune regole sono imposte sull'insieme di dati.
+>
+> Un esempio: se un algoritmo viene applicato su un batch di dati non etichettati o semi-etichettati, i cluster che produce potrebbero essere di scarsa qualità. Nell'esempio sopra, i cluster potrebbero raggruppare "cose musicali rotonde" e "cose musicali quadrate" e "cose triangolari" e "biscotti". Se vengono dati dei vincoli, o delle regole da seguire ("l'oggetto deve essere di plastica", "l'oggetto deve essere in grado di produrre musica"), questo può aiutare a "vincolare" l'algoritmo a fare scelte migliori.
+>
+> 'Densità'
+>
+> I dati "rumorosi" sono considerati "densi". Le distanze tra i punti in ciascuno dei suoi cluster possono rivelarsi, all'esame, più o meno dense, o "affollate" e quindi questi dati devono essere analizzati con il metodo di clustering appropriato. [Questo articolo](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) dimostra la differenza tra l'utilizzo del clustering K-Means rispetto agli algoritmi HDBSCAN per esplorare un insieme di dati rumoroso con densità di cluster non uniforme.
+
+## Algoritmi di clustering
+
+Esistono oltre 100 algoritmi di clustering e il loro utilizzo dipende dalla natura dei dati a portata di mano. Si discutono alcuni dei principali:
+
+- **Raggruppamento gerarchico**. Se un oggetto viene classificato in base alla sua vicinanza a un oggetto vicino, piuttosto che a uno più lontano, i cluster vengono formati in base alla distanza dei loro membri da e verso altri oggetti. Il clustering agglomerativo di Scikit-learn è gerarchico.
+
+   ![Infografica sul clustering gerarchico](../images/hierarchical.png)
+   > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+- **Raggruppamento centroide**. Questo popolare algoritmo richiede la scelta di 'k', o il numero di cluster da formare, dopodiché l'algoritmo determina il punto centrale di un cluster e raccoglie i dati attorno a quel punto. [Il clustering K-means](https://wikipedia.org/wiki/K-means_clustering) è una versione popolare del clustering centroide. Il centro è determinato dalla media più vicina, da qui il nome. La distanza al quadrato dal cluster è ridotta al minimo.
+
+   ![Infografica sul clustering del centroide](../images/centroid.png)
+   > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+- **Clustering basato sulla distribuzione**. Basato sulla modellazione statistica, il clustering basato sulla distribuzione è incentrato sulla determinazione della probabilità che un punto dati appartenga a un cluster e sull'assegnazione di conseguenza. I metodi di miscelazione gaussiana appartengono a questo tipo.
+
+- **Clustering basato sulla densità**. I punti dati vengono assegnati ai cluster in base alla loro densità o al loro raggruppamento l'uno intorno all'altro. I punti dati lontani dal gruppo sono considerati valori anomali o rumore. DBSCAN, Mean-shift e OPTICS appartengono a questo tipo di clustering.
+
+- **Clustering basato su griglia**. Per gli insiemi di dati multidimensionali, viene creata una griglia e i dati vengono divisi tra le celle della griglia, creando così dei cluster.
+
+## Esercizio: raggruppare i dati
+
+Il clustering come tecnica è notevolmente aiutato da una corretta visualizzazione, quindi si inizia visualizzando i  dati musicali. Questo esercizio aiuterà a decidere quale dei metodi di clustering si dovranno utilizzare in modo più efficace per la natura di questi dati.
+
+1. Aprire il file _notebook.ipynb_ in questa cartella.
+
+1. Importare il pacchetto `Seaborn` per una buona visualizzazione dei dati.
+
+   ```python
+   !pip install seaborn
+   ```
+
+1. Aggiungere i dati dei brani da _nigerian-songs.csv_. Caricare un dataframe con alcuni dati sulle canzoni. Prepararsi a esplorare questi dati importando le librerie e scaricando i dati:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import pandas as pd
+
+   df = pd.read_csv("../data/nigerian-songs.csv")
+   df.head()
+   ```
+
+   Controllare le prime righe di dati:
+
+    |     | name                     | album                        | artist              | artist_top_genre | release_date | length | popularity | danceability | acousticness | energy | instrumentalness | liveness | loudness | speechiness | tempo   | time_signature |
+    | --- | ------------------------ | ---------------------------- | ------------------- | ---------------- | ------------ | ------ | ---------- | ------------ | ------------ | ------ | ---------------- | -------- | -------- | ----------- | ------- | -------------- |
+    | 0   | Sparky                   | Mandy & The Jungle           | Cruel Santino       | alternative r&b  | 2019         | 144000 | 48         | 0.666        | 0.851        | 0.42   | 0.534            | 0.11     | -6.699   | 0.0829      | 133.015 | 5              |
+    | 1   | shuga rush               | EVERYTHING YOU HEARD IS TRUE | Odunsi (The Engine) | afropop          | 2020         | 89488  | 30         | 0.71         | 0.0822       | 0.683  | 0.000169         | 0.101    | -5.64    | 0.36        | 129.993 | 3              |
+    | 2   | LITT!                    | LITT!                        | AYLØ                | indie r&b        | 2018         | 207758 | 40         | 0.836        | 0.272        | 0.564  | 0.000537         | 0.11     | -7.127   | 0.0424      | 130.005 | 4              |
+    | 3   | Confident / Feeling Cool | Enjoy Your Life              | Lady Donli          | nigerian pop     | 2019         | 175135 | 14         | 0.894        | 0.798        | 0.611  | 0.000187         | 0.0964   | -4.961   | 0.113       | 111.087 | 4              |
+    | 4   | wanted you               | rare.                        | Odunsi (The Engine) | afropop          | 2018         | 152049 | 25         | 0.702        | 0.116        | 0.833  | 0.91             | 0.348    | -6.044   | 0.0447      | 105.115 | 4              |
+
+1. Ottenere alcune informazioni sul dataframe, chiamando `info()`:
+
+   ```python
+   df.info()
+   ```
+
+   Il risultato appare così:
+
+   ```output
+   <class 'pandas.core.frame.DataFrame'>
+   RangeIndex: 530 entries, 0 to 529
+   Data columns (total 16 columns):
+    #   Column            Non-Null Count  Dtype  
+   ---  ------            --------------  -----  
+    0   name              530 non-null    object
+    1   album             530 non-null    object
+    2   artist            530 non-null    object
+    3   artist_top_genre  530 non-null    object
+    4   release_date      530 non-null    int64  
+    5   length            530 non-null    int64  
+    6   popularity        530 non-null    int64  
+    7   danceability      530 non-null    float64
+    8   acousticness      530 non-null    float64
+    9   energy            530 non-null    float64
+    10  instrumentalness  530 non-null    float64
+    11  liveness          530 non-null    float64
+    12  loudness          530 non-null    float64
+    13  speechiness       530 non-null    float64
+    14  tempo             530 non-null    float64
+    15  time_signature    530 non-null    int64  
+   dtypes: float64(8), int64(4), object(4)
+   memory usage: 66.4+ KB
+   ```
+
+1. Ricontrollare i valori null, chiamando `isnull()` e verificando che la somma sia 0:
+
+   ```python
+   df.isnull().sum()
+   ```
+
+   Si presenta bene!
+
+   ```output
+   name                0
+   album               0
+   artist              0
+   artist_top_genre    0
+   release_date        0
+   length              0
+   popularity          0
+   danceability        0
+   acousticness        0
+   energy              0
+   instrumentalness    0
+   liveness            0
+   loudness            0
+   speechiness         0
+   tempo               0
+   time_signature      0
+   dtype: int64
+   ```
+
+1. Descrivere i dati:
+
+   ```python
+   df.describe()
+   ```
+
+   |         | release_date | lenght      | popularity | danceability | acousticness | Energia  | strumentale | vitalità | livello di percezione sonora | parlato  | tempo      | #ora_firma |
+   | ------- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ----------- | -------- | ---------------------------- | -------- | ---------- | ---------- |
+   | estero) | 530          | 530         | 530        | 530          | 530          | 530      | 530         | 530      | 530                          | 530      | 530        | 530        |
+   | mezzo   | 2015.390566  | 222298.1698 | 17.507547  | 0.741619     | 0.265412     | 0.760623 | 0,016305    | 0,147308 | -4.953011                    | 0,130748 | 116.487864 | 3.986792   |
+   | std     | 3.131688     | 39696.82226 | 18.992212  | 0,117522     | 0.208342     | 0.148533 | 0.090321    | 0,123588 | 2.464186                     | 0,092939 | 23.518601  | 0.333701   |
+   | min     | 1998         | 89488       | 0          | 0,255        | 0,000665     | 0,111    | 0           | 0,0283   | -19,362                      | 0,0278   | 61.695     | 3          |
+   | 25%     | 2014         | 199305      | 0          | 0,681        | 0,089525     | 0,669    | 0           | 0,07565  | -6.29875                     | 0,0591   | 102.96125  | 4          |
+   | 50%     | 2016         | 218509      | 13         | 0,761        | 0.2205       | 0.7845   | 0.000004    | 0,1035   | -4.5585                      | 0,09795  | 112.7145   | 4          |
+   | 75%     | 2017         | 242098.5    | 31         | 0,8295       | 0.403        | 0.87575  | 0.000234    | 0,164    | -3.331                       | 0,177    | 125.03925  | 4          |
+   | max     | 2020         | 511738      | 73         | 0.966        | 0,954        | 0,995    | 0,91        | 0,811    | 0,582                        | 0.514    | 206.007    | 5          |
+
+> 🤔 Se si sta lavorando con il clustering, un metodo non supervisionato che non richiede dati etichettati, perché si stanno mostrando questi dati con etichette? Nella fase di esplorazione dei dati, sono utili, ma non sono necessari per il funzionamento degli algoritmi di clustering. Si potrebbero anche rimuovere le intestazioni delle colonne e fare riferimento ai dati per numero di colonna.
+
+Dare un'occhiata ai valori generali dei dati. Si nota che la popolarità può essere "0", che mostra i brani che non hanno una classifica. Quelli verranno rimossi a breve.
+
+1. Usare un grafico a barre per scoprire i generi più popolari:
+
+   ```python
+   import seaborn as sns
+
+   top = df['artist_top_genre'].value_counts()
+   plt.figure(figsize=(10,7))
+   sns.barplot(x=top[:5].index,y=top[:5].values)
+   plt.xticks(rotation=45)
+   plt.title('Top genres',color = 'blue')
+   ```
+
+   ![I più popolari](../images/popular.png)
+
+✅ Se si desidera vedere più valori superiori, modificare il valore di top `[:5]` con un valore più grande o rimuoverlo per vederli tutti.
+
+Nota, quando un valore di top è descritto come "Missing", ciò significa che Spotify non lo ha classificato, quindi va rimosso.
+
+1. Eliminare i dati mancanti escludendoli via filtro
+
+   ```python
+   df = df[df['artist_top_genre'] != 'Missing']
+   top = df['artist_top_genre'].value_counts()
+   plt.figure(figsize=(10,7))
+   sns.barplot(x=top.index,y=top.values)
+   plt.xticks(rotation=45)
+   plt.title('Top genres',color = 'blue')
+   ```
+
+   Ora ricontrollare i generi:
+
+   ![I più popolari](../images/all-genres.png)
+
+1. Di gran lunga, i primi tre generi dominano questo insieme di dati. Si pone l'attenzione su `afrodancehall,` `afropop` e `nigerian pop`, filtrando inoltre l'insieme di dati per rimuovere qualsiasi cosa con un valore di popolarità 0 (il che significa che non è stato classificato con una popolarità nell'insieme di dati e può essere considerato rumore per gli scopi attuali):
+
+   ```python
+   df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]
+   df = df[(df['popularity'] > 0)]
+   top = df['artist_top_genre'].value_counts()
+   plt.figure(figsize=(10,7))
+   sns.barplot(x=top.index,y=top.values)
+   plt.xticks(rotation=45)
+   plt.title('Top genres',color = 'blue')
+   ```
+
+1. Fare un test rapido per vedere se i dati sono correlati in modo particolarmente forte:
+
+   ```python
+   corrmat = df.corr()
+   f, ax = plt.subplots(figsize=(12, 9))
+   sns.heatmap(corrmat, vmax=.8, square=True)
+   ```
+
+   ![correlazioni](../images/correlation.png)
+
+   L'unica forte correlazione è tra `energy` e `loudness` (volume), il che non è troppo sorprendente, dato che la musica ad alto volume di solito è piuttosto energica. Altrimenti, le correlazioni sono relativamente deboli. Sarà interessante vedere cosa può fare un algoritmo di clustering di questi dati.
+
+   > 🎓 Notare che la correlazione non implica la causalità! Ci sono prove di correlazione ma nessuna prova di causalità. Un [sito web divertente](https://tylervigen.com/spurious-correlations) ha alcune immagini che enfatizzano questo punto.
+
+C'è qualche convergenza in questo insieme di dati intorno alla popolarità e alla ballabilità percepite di una canzone? Una FacetGrid mostra che ci sono cerchi concentrici che si allineano, indipendentemente dal genere. Potrebbe essere che i gusti nigeriani convergano ad un certo livello di ballabilità per questo genere?
+
+✅ Provare diversi punti dati (energy, loudness, speachiness) e più o diversi generi musicali. Cosa si può scoprire? Dare un'occhiata alla tabella con `df.describe()` per vedere la diffusione generale dei punti dati.
+
+### Esercizio - distribuzione dei dati
+
+Questi tre generi sono significativamente differenti nella percezione della loro ballabilità, in base alla loro popolarità?
+
+1. Esaminare la distribuzione dei dati sui tre principali generi per la popolarità e la ballabilità lungo un dato asse x e y.
+
+   ```python
+   sns.set_theme(style="ticks")
+
+   g = sns.jointplot(
+       data=df,
+       x="popularity", y="danceability", hue="artist_top_genre",
+       kind="kde",
+   )
+   ```
+
+   Si possono scoprire cerchi concentrici attorno a un punto di convergenza generale, che mostra la distribuzione dei punti.
+
+   > 🎓 Si noti che questo esempio utilizza un grafico KDE (Kernel Density Estimate) che rappresenta i dati utilizzando una curva di densità di probabilità continua. Questo consente di interpretare i dati quando si lavora con più distribuzioni.
+
+   In generale, i tre generi si allineano liberamente in termini di popolarità e ballabilità. Determinare i cluster in questi dati vagamente allineati sarà una sfida:
+
+   ![distribuzione](../images/distribution.png)
+
+1. Crea un grafico a dispersione:
+
+   ```python
+   sns.FacetGrid(df, hue="artist_top_genre", size=5) \
+      .map(plt.scatter, "popularity", "danceability") \
+      .add_legend()
+   ```
+
+   Un grafico a dispersione degli stessi assi mostra un modello di convergenza simile
+
+   ![Facetgrid](../images/facetgrid.png)
+
+In generale, per il clustering è possibile utilizzare i grafici a dispersione per mostrare i cluster di dati, quindi è molto utile padroneggiare questo tipo di visualizzazione. Nella prossima lezione, si prenderanno questi dati filtrati e si utilizzerà il clustering k-means per scoprire gruppi in questi dati che si sovrappongono in modi interessanti.
+
+---
+
+## 🚀 Sfida
+
+In preparazione per la lezione successiva, creare un grafico sui vari algoritmi di clustering che si potrebbero scoprire e utilizzare in un ambiente di produzione. Che tipo di problemi sta cercando di affrontare il clustering?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Prima di applicare gli algoritmi di clustering, come si è appreso, è una buona idea comprendere la natura del proprio insieme di dati. Leggere di più su questo argomento [qui](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
+
+[Questo utile articolo](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) illustra i diversi modi in cui si comportano i vari algoritmi di clustering, date diverse forme di dati.
+
+## Compito
+
+[Ricercare altre visualizzazioni per il clustering](assignment.it.md)
diff --git a/5-Clustering/1-Visualize/translations/README.ko.md b/5-Clustering/1-Visualize/translations/README.ko.md
new file mode 100644
index 000000000..c561bd621
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/README.ko.md
@@ -0,0 +1,335 @@
+# Clustering 소개하기
+
+Clustering이 데이터셋에 라벨을 붙이지 않거나 입력이 미리 정의한 출력과 맞지 않는다고 가정한다면 [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) 타입입니다. 다양한 알고리즘으로 라벨링되지 않은 데이터를 정렬하고 데이터에서 식별할 패턴에 따라 묶을 수 있게 제공됩니다.
+
+[![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
+
+> 🎥 영상을 보려면 이미지 클릭. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+
+### 소개
+
+[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)은 데이터를 탐색할 때 매우 유용합니다. 나이지리아 사람들이 음악을 듣는 방식에서 트렌드와 패턴을 찾아 도움을 받을 수 있는지 봅니다.
+
+✅ 시간을 내서 clustering 사용법에 대해 생각해봅니다. 실생활에서, clustering은 빨래 바구니를 가지고 가족 구성원의 옷 🧦👕👖🩲을 정리하는 순간에 발생합니다. 데이터 사이언스에서, clustering은 사용자의 선호를 분석하거나, 라벨을 붙이지 않은 데이터셋 특성을 정하는 순간에 발생합니다. Clustering은, 어떤 식으로든, 양말 서랍처럼, 혼란스러움을 이해하는 순간에 도움을 받을 수 있습니다.
+
+[![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
+
+> 🎥 영상을 보려면 이미지 클릭: MIT's John Guttag introduces clustering
+
+전문적인 설정에서, clustering은 시장 세분화처럼 결정하면서 사용할 수 있습니다, 예시로, 특정 나이대가 어떤 아이템을 구매하는지 결정할 수 있습니다. 또 다른 용도는 anomaly detection이며, 아마도 신용 카드 거래 데이터셋에서 사기를 적발하기 위함입니다. 또는 clustering으로 의학촬영의 배치에서 종양을 판단할 수 있습니다.
+
+✅ 시간을 내서, 은행, 이커머스, 비지니스 설정에서, 'in the wild' 어떻게 clustering을 접했는지 생각합니다.
+
+> 🎓 흥미로운 사실은, cluster analysis는 1930년에 인류학과 심리학의 필드에서 유래되었습니다. 어떻게 사용했는지 상상 되나요?
+
+또한, 그룹화된 검색 결과를 위해서 사용합니다. - 예를 들면, 쇼핑 링크, 이미지, 또는 리뷰. Clustering은 줄이려는 대규모 데이터셋이 있고 세분화된 분석을 하고 싶을 때 유용하므로, 다른 모델이 설계되기 전까지 데이터를 학습하며 이 기술을 사용할 수 있습니다.
+
+✅ 데이터가 클러스터에서 구성되면, 클러스터 ID를 할당하며, 이 기술로 데이터셋의 프라이버시를 보호할 때 유용합니다; 식별할 수 있는 데이터를 더 노출하는 대신, 클러스터 ID로 데이터 포인트를 참조할 수 있습니다. 클러스터의 다른 요소가 아닌 클러스터 ID를 참조해서 식별하는 이유를 생각할 수 있나요?
+
+이 [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)에서 clustering 기술을 깊게 이해합니다.
+
+## Clustering 시작하기
+
+[Scikit-learn](https://scikit-learn.org/stable/modules/clustering.html)은 clustering을 수행하는 방식의 큰 배열을 제공합니다. 선택한 타입은 사용 케이스에 따라서 달라질 예정입니다. 문서에 따르면, 각 방식에 다양한 이점이 있습니다. Scikit-learn에서 지원하는 방식과 적절한 사용 케이스에 대한 단순화된 테이블입니다:
+
+| Method name                  | Use case                                                               |
+| :--------------------------- | :--------------------------------------------------------------------- |
+| K-Means                      | general purpose, inductive                                             |
+| Affinity propagation         | many, uneven clusters, inductive                                       |
+| Mean-shift                   | many, uneven clusters, inductive                                       |
+| Spectral clustering          | few, even clusters, transductive                                       |
+| Ward hierarchical clustering | many, constrained clusters, transductive                               |
+| Agglomerative clustering     | many, constrained, non Euclidean distances, transductive               |
+| DBSCAN                       | non-flat geometry, uneven clusters, transductive                       |
+| OPTICS                       | non-flat geometry, uneven clusters with variable density, transductive |
+| Gaussian mixtures            | flat geometry, inductive                                               |
+| BIRCH                        | large dataset with outliers, inductive                                 |
+
+> 🎓 클러스터를 만드는 방식에서 데이터 포인트를 그룹으로 수집하는 것과 많이 비슷합니다. 몇 단어를 풀어봅니다:
+>
+> 🎓 ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning))
+> 
+> Transductive 추론은 특정한 테스트 케이스로 맵핑되어 관찰된 훈련 케이스에서 유래됩니다. Inductive 추론은 오직 테스트 케이스에서만 적용되는 일반적인 규칙으로 맵핑된 훈련 케이스에서 유래됩니다.
+> 
+> 예시: 오직 일부만 라벨링된 데이터를 가지고 있다고 생각합니다. 일부 'records', 'cds', 공백으로 이루어져 있습니다. 공백에 라벨을 제공하는 일입니다. 만약 inductive 접근법을 선택했다면, 'records'와 'cds'를 찾는 모델로 훈련하고, 라벨링되지 않은 데이터에 라벨을 적용합니다. 이 접근법은 실제 'cassettes'를 분류할 때 골치아픕니다. transductive 접근법은, 반면에, 비슷한 아이템과 함께 그룹으로 묶어서 라벨을 적용하므로 알려지지 않은 데이터보다 효과적으로 핸들링합니다. 이 케이스에서, 클러스터는 'round musical things'와 'square musical things'를 반영할 수 있습니다.
+> 
+> 🎓 ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)
+> 
+> 수학 용어에서 유래된, non-flat vs. flat 기하학은 'flat' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) 또는 'non-flat' (non-Euclidean) 기하학 방식으로 포인트 사이 거리를 특정하는 것을 의미합니다.
+>
+> 이 컨텍스트에서 'Flat'은 Euclidean 기하학 (일부는 'plane' 기하학으로 가르침)을, non-flat은 non-Euclidean을 나타냅니다. 기하학은 머신러닝과 어떤 연관성이 있나요? 음, 수학과 기반이 같은 두 필드라서, 클러스터에서 포인트 사이의 거리를 측정할 수 있는 공통 방식이 있으며, 데이터의 특성에 기반해서, 'flat' 또는 'non-flat'으로 마무리지을 수 있습니다. [Euclidean distances](https://wikipedia.org/wiki/Euclidean_distance)는 두 포인트 사이 선분의 길이로 측정합니다. [Non-Euclidean distances](https://wikipedia.org/wiki/Non-Euclidean_geometry)는 곡선에 따라서 측정됩니다. 만약 데이터가, 시각화되어서, 평면에 존재하지 않은 것으로 보인다면, 특별 알고리즘을 사용해서 핸들링할 수 있습니다.
+>
+![Flat vs Nonflat Geometry Infographic](.././images/flat-nonflat.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+> 
+> 🎓 ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
+> 
+> 클러스터는 distance matrix로 정의됩니다, 예시로. 포인트 사이 거리입니다. 거리는 몇 방식으로 측정될 수 있습니다. Euclidean 클러스터는 포인트 값의 평균으로 정의되고, 'centroid' 또는 중심 포인트를 포함합니다. 거리는 이 중심까지 거리로 측정됩니다. Non-Euclidean 거리는 다른 포인트에서 가까운 포인트, 'clustroids'로 나타냅니다. Clustroid는 다음과 같이 다양한 방식으로 정의할 수 있습니다. 
+> 
+> 🎓 ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering)
+> 
+> [Constrained Clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf)은 unsupervised 방식에서 'semi-supervised' 학습을 접목합니다. 포인트 사이 관계는 'cannot link' 또는 'must-link'로 플래그되어 데이터 셋에 일부 룰을 강제합니다.
+
+>
+> 예시: 만약 알고리즘이 라벨링하지 못했거나 세미-라벨링된 데이터의 배치에서 풀리면, 만들어지는 클러스터의 품질이 내려갈 수 있습니다. 위 예시에서, 클러스터는 'round music things'와 'square music things'와 'triangular things'와 'cookies'를 그룹으로 묶을 수 있습니다. 만약 제한사항이나, 따라야할 룰이 주어진다면 ("the item must be made of plastic", "the item needs to be able to produce music") 알고리즘이 더 좋은 선택을 하도록 '제한'해서 도와줄 수 있습니다.
+> 
+> 🎓 'Density'
+> 
+> 'noisy' 데이터는 'dense'로 칩니다. 각 클러스터의 포인트 사이 거리에서 조금 밀집해있거나, 'crowded'한 것으로 증명할 수 있으므로, 데이터는 적당한 clustering 방식으로 분석되어질 필요가 있습니다. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html)에서 K-Means clustering vs. HDBSCAN 알고리즘을 사용해서 고르지않는 클러스터 밀집도로 노이즈 데이터셋을 찾아보고 서로 다른 차이점을 시연합니다.
+
+## Clustering 알고리즘
+
+100개 이상 clustering 알고리즘이 있고, 현재 데이터의 특성에 기반해서 사용하는 게 다릅니다. 몇 주요 사항에 대해 이야기해봅니다:
+
+- **Hierarchical clustering**. 만약 오브젝트가 멀리 떨어져있지 않고, 가까운 오브젝트와 근접성으로 분류된다면, 클러스터는 다른 오브젝트의 거리에 따라서 형태가 만들어집니다. Scikit-learn의 agglomerative clustering은 계층적입니다.
+
+   ![Hierarchical clustering Infographic](.././images/hierarchical.png)
+   > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+- **Centroid clustering**. 이 인기있는 알고리즘은 'k', 또는 형성할 클러스터의 수를 선택해야 될 필요가 있으며, 이후 알고리즘은 클러스터의 중심 포인트를 결정하고 포인트 주변 데이터를 수집합니다. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering)은 인기있는 centroid clustering 버전입니다. 중심이 가까운 평균에 따라서 이름이 정해집니다. 클러스터에서 제곱 거리가 최소화됩니다.
+
+   ![Centroid clustering Infographic](.././images/centroid.png)
+   > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+- **Distribution-based clustering**. 통계 모델링에서, distribution-based clustering은 데이터 포인트가 클러스터에 있는 확률을 기반으로, 할당에 중점을 둡니다. Gaussian mixture 방식이 이 타입에 속합니다.
+
+- **Density-based clustering**. 데이터 포인트는 밀집도나 서로 그룹으로 묶어진 기반으로 클러스터에 할당합니다. 그룹에서 멀리 떨어진 데이터 포인트를 아웃라이어나 노이즈로 간주합니다. DBSCAN, Mean-shift와 OPTICS는 이 clustering 타입에 해당됩니다.
+
+- **Grid-based clustering**. multi-dimensional 데이터셋이면, 그리드가 만들어지고 데이터가 그리드의 셀에 나눈 뒤에, 클러스터를 만듭니다.
+
+## 연습 - 데이터 cluster
+
+기술에서 Clustering은 적절한 시각화로 크게 도움받으므로, 음악 데이터로 시각화해서 시작해봅니다. 이 연습은 데이터의 특성에 가장 효과적으로 사용할 clustering 방식을 정할 때 도움받을 수 있습니다.
+
+1. 이 폴더에서 _notebook.ipynb_ 파일을 엽니다.
+
+1. 좋은 데이터 시각화를 위해서 `Seaborn` 패키지를 가져옵니다.
+
+    ```python
+    !pip install seaborn
+    ```
+
+1. _nigerian-songs.csv_ 의 노래 데이터를 추가합니다. 일부 노래 데이터가 있는 데이터 프레임을 불러옵니다. 라이브러리를 가져오고 데이터를 덤프해서 찾아봅니다:
+
+    ```python
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    
+    df = pd.read_csv("../data/nigerian-songs.csv")
+    df.head()
+    ```
+
+    데이터의 첫 몇 줄을 확인합니다:
+
+    |     | name                     | album                        | artist              | artist_top_genre | release_date | length | popularity | danceability | acousticness | energy | instrumentalness | liveness | loudness | speechiness | tempo   | time_signature |
+    | --- | ------------------------ | ---------------------------- | ------------------- | ---------------- | ------------ | ------ | ---------- | ------------ | ------------ | ------ | ---------------- | -------- | -------- | ----------- | ------- | -------------- |
+    | 0   | Sparky                   | Mandy & The Jungle           | Cruel Santino       | alternative r&b  | 2019         | 144000 | 48         | 0.666        | 0.851        | 0.42   | 0.534            | 0.11     | -6.699   | 0.0829      | 133.015 | 5              |
+    | 1   | shuga rush               | EVERYTHING YOU HEARD IS TRUE | Odunsi (The Engine) | afropop          | 2020         | 89488  | 30         | 0.71         | 0.0822       | 0.683  | 0.000169         | 0.101    | -5.64    | 0.36        | 129.993 | 3              |
+    | 2   | LITT!                    | LITT!                        | AYLØ                | indie r&b        | 2018         | 207758 | 40         | 0.836        | 0.272        | 0.564  | 0.000537         | 0.11     | -7.127   | 0.0424      | 130.005 | 4              |
+    | 3   | Confident / Feeling Cool | Enjoy Your Life              | Lady Donli          | nigerian pop     | 2019         | 175135 | 14         | 0.894        | 0.798        | 0.611  | 0.000187         | 0.0964   | -4.961   | 0.113       | 111.087 | 4              |
+    | 4   | wanted you               | rare.                        | Odunsi (The Engine) | afropop          | 2018         | 152049 | 25         | 0.702        | 0.116        | 0.833  | 0.91             | 0.348    | -6.044   | 0.0447      | 105.115 | 4              |
+
+1. `info()`를 불러서, 데이터 프레임에 대한 약간의 정보를 얻습니다:
+
+    ```python
+    df.info()
+    ```
+
+   출력은 이렇게 보입니다:
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 530 entries, 0 to 529
+    Data columns (total 16 columns):
+     #   Column            Non-Null Count  Dtype  
+    ---  ------            --------------  -----  
+     0   name              530 non-null    object 
+     1   album             530 non-null    object 
+     2   artist            530 non-null    object 
+     3   artist_top_genre  530 non-null    object 
+     4   release_date      530 non-null    int64  
+     5   length            530 non-null    int64  
+     6   popularity        530 non-null    int64  
+     7   danceability      530 non-null    float64
+     8   acousticness      530 non-null    float64
+     9   energy            530 non-null    float64
+     10  instrumentalness  530 non-null    float64
+     11  liveness          530 non-null    float64
+     12  loudness          530 non-null    float64
+     13  speechiness       530 non-null    float64
+     14  tempo             530 non-null    float64
+     15  time_signature    530 non-null    int64  
+    dtypes: float64(8), int64(4), object(4)
+    memory usage: 66.4+ KB
+    ```
+
+1. `isnull()`을 부르고 합산이 0인지 확인해서, Null 값을 다시 검토합니다:
+
+    ```python
+    df.isnull().sum()
+    ```
+
+    좋게 보입니다:
+
+    ```output
+    name                0
+    album               0
+    artist              0
+    artist_top_genre    0
+    release_date        0
+    length              0
+    popularity          0
+    danceability        0
+    acousticness        0
+    energy              0
+    instrumentalness    0
+    liveness            0
+    loudness            0
+    speechiness         0
+    tempo               0
+    time_signature      0
+    dtype: int64
+    ```
+
+1. 데이터를 서술합니다:
+
+    ```python
+    df.describe()
+    ```
+
+    |       | release_date | length      | popularity | danceability | acousticness | energy   | instrumentalness | liveness | loudness  | speechiness | tempo      | time_signature |
+    | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- |
+    | count | 530          | 530         | 530        | 530          | 530          | 530      | 530              | 530      | 530       | 530         | 530        | 530            |
+    | mean  | 2015.390566  | 222298.1698 | 17.507547  | 0.741619     | 0.265412     | 0.760623 | 0.016305         | 0.147308 | -4.953011 | 0.130748    | 116.487864 | 3.986792       |
+    | std   | 3.131688     | 39696.82226 | 18.992212  | 0.117522     | 0.208342     | 0.148533 | 0.090321         | 0.123588 | 2.464186  | 0.092939    | 23.518601  | 0.333701       |
+    | min   | 1998         | 89488       | 0          | 0.255        | 0.000665     | 0.111    | 0                | 0.0283   | -19.362   | 0.0278      | 61.695     | 3              |
+    | 25%   | 2014         | 199305      | 0          | 0.681        | 0.089525     | 0.669    | 0                | 0.07565  | -6.29875  | 0.0591      | 102.96125  | 4              |
+    | 50%   | 2016         | 218509      | 13         | 0.761        | 0.2205       | 0.7845   | 0.000004         | 0.1035   | -4.5585   | 0.09795     | 112.7145   | 4              |
+    | 75%   | 2017         | 242098.5    | 31         | 0.8295       | 0.403        | 0.87575  | 0.000234         | 0.164    | -3.331    | 0.177       | 125.03925  | 4              |
+    | max   | 2020         | 511738      | 73         | 0.966        | 0.954        | 0.995    | 0.91             | 0.811    | 0.582     | 0.514       | 206.007    | 5              |
+
+> 🤔 만약 라벨링 안 한 데이터가 필요하지 않은 unsupervised 방식으로, clustering을 작업하게되면, 왜 데이터로 라벨을 보여주나요? 데이터 탐색 단계에서 편리하겠지만, clustering 알고리즘이 동작할 때는 필요 없습니다. 열 헤더를 제거하고 열 넘버로 데이터를 참조할 수 있습니다.
+
+데이터의 일반적 값을 봅니다. 랭킹에 못 들은 음악을 보여주는 건, '0'일 수 있습니다. 바로 제거하겠습니다.
+
+1. 가장 인기있는 장르를 찾기 위해서 barplot을 사용합니다:
+
+    ```python
+    import seaborn as sns
+    
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top[:5].index,y=top[:5].values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+    ![most popular](.././images/popular.png)
+
+✅ 만약 상위 값을 많이 보려면,  top `[:5]`을 더 큰 값으로 변경하거나, 제거해서 다 봅니다.
+
+노트, 상위 장르가 'Missing'으로 서술되어 있으면, Spotify에서 분류하지 않았으므로, 제거합니다.
+
+1. 필터링해서 missing 데이터를 제거합니다
+
+    ```python
+    df = df[df['artist_top_genre'] != 'Missing']
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top.index,y=top.values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+    이제 장르를 다시 확인합니다:
+
+    ![most popular](../images/all-genres.png)
+
+1. 지금까지, 상위 3개 장르가 데이터셋을 장악했습니다. `afro dancehall`, `afropop`, 그리고 `nigerian pop`에 집중하고 인기도 값이 0인 모든 것을 지우기 위해서 추가로 필터링합니다 (데이터셋에서 인기도로 분류하지 않은 것은 이 목적에서 노이즈로 간주될 수 있다는 점을 의미합니다):
+
+    ```python
+    df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]
+    df = df[(df['popularity'] > 0)]
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top.index,y=top.values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+1. 특별히 강력한 방식으로 데이터에 상관 관계가 있는지 보기 위해서 빠르게 테스트합니다:
+
+    ```python
+    corrmat = df.corr()
+    f, ax = plt.subplots(figsize=(12, 9))
+    sns.heatmap(corrmat, vmax=.8, square=True)
+    ```
+
+    ![correlations](../images/correlation.png)
+
+    유일하게 강한 상관 관계는 `energy`와 `loudness` 사이에 있으며, 일반적으로 화려한 음악이 에너지 넘치는다는 사실은 놀랍지 않습니다. 아니라면, 상관 관계는 상대적으로 약합니다. clustering 알고리즘이 데이터를 만드는 과정을 보는 것은 흥미로울 예정입니다.
+
+    > 🎓 상관 관계가 인과 관계를 의미하지 않는다는 것을 참고합니다! 상관 관계의 증거는 있지만 인과 관계의 증거가 없습니다. [amusing web site](https://tylervigen.com/spurious-correlations)에 이 점을 강조할 몇 자료가 있습니다.
+
+데이터셋에 노래의 perceived popularity와 danceability가 수렴되나요? FacetGrid는 장르와 관계없이, 일렬로 늘어선 동심원을 보여줍니다. 나이지리아 사람들의 취향이 이 장르에서 특정 danceability 레벨에 수렴할 수 있지 않을까요?
+
+✅ 다른 데이터 포인트 (energy, loudness, speechiness)와 더 많거나 다른 뮤지컬 장르로 시도합니다. 무엇을 찾을 수 있나요? 일반적으로 데이터 포인트가 확산하는 것을 보려면 `df.describe()` 테이블을 찾아봅니다.
+
+### 연습 - 데이터 분산
+
+이 3개 장르는 인기도에 기반해서, danceability의 인지도와 상당히 다르나요?
+
+1. 주어진 x와 y 축에 따라서 인기도와 danceability에 대한 상위 3개 장르 데이터 분포를 찾아봅니다.
+
+    ```python
+    sns.set_theme(style="ticks")
+    
+    g = sns.jointplot(
+        data=df,
+        x="popularity", y="danceability", hue="artist_top_genre",
+        kind="kde",
+    )
+    ```
+
+    일반적인 수렴 점을 중심으로 동심원을 발견해서, 점의 분포를 확인할 수 있습니다.
+
+    > 🎓 이 예시에서 continuous probability density curve로 데이터를 나타내는 KDE (Kernel Density Estimate) 그래프를 사용합니다. 여러 분포로 작업할 때 데이터를 해석할 수 있습니다.
+
+    보통은, 3가지 장르가 인기도와 danceability로 루즈하게 정렬됩니다. 루즈하게-정렬된 데이터에서 클러스터를 결정하는 것은 힘듭니다:
+
+    ![distribution](../images/distribution.png)
+
+1. scatter plot을 만듭니다:
+
+
+    ```python
+    sns.FacetGrid(df, hue="artist_top_genre", size=5) \
+       .map(plt.scatter, "popularity", "danceability") \
+       .add_legend()
+    ```
+
+    동일 축의 scatterplot은 비슷한 수렴 패턴을 보입니다
+
+    ![Facetgrid](../images/facetgrid.png)
+
+보통, clustering은, scatterplots으로 데이터 클러스터를 표시할 수 있으므로, 이 시각화 타입을 숙지하는 것은 매우 유용합니다. 다음 강의에서, 필터링된 데이터를 가져와서 k-means clustering으로 흥미로운 방식이 겹쳐보일 이 데이터의 그룹을 찾아보겠습니다.
+
+---
+
+## 🚀 도전
+
+다음 강의를 준비하기 위해서, 프로덕션 환경에서 찾아서 사용할 수 있는 다양한 clustering 알고리즘을 차트로 만듭니다. clustering은 어떤 문제를 해결하려고 시도하나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+
+## 검토 & 자기주도 학습
+
+clustering 알고리즘을 적용하기 전에, 배운대로, 데이터셋의 특성을 이해하는 게 좋습니다. 이 토픽 [here](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)을 더 읽어봅니다.
+
+[This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/)에서 다양한 데이터 형태가 주어지면, 다양한 clustering 알고리즘이 동작하는 다른 방식을 알려줍니다.
+
+## 과제
+
+[Research other visualizations for clustering](../assignment.md)
diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
new file mode 100644
index 000000000..c3aae1dcb
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -0,0 +1,339 @@
+# 介绍聚类
+
+聚类是一种无监督学习，它假定数据集未标记或其输入与预定义的输出不匹配。它使用各种算法对未标记的数据进行排序，并根据它在数据中识别的模式提供分组。
+
+[![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
+
+> 🎥 点击上面的图片观看视频。当您通过聚类学习机器学习时，请欣赏一些尼日利亚舞厅曲目 - 这是 2014 年 PSquare 上高度评价的歌曲。
+
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+
+### 介绍
+
+[聚类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数据探索非常有用。让我们看看它是否有助于发现尼日利亚观众消费音乐的趋势和模式。
+
+✅花一点时间思考聚类的用途。在现实生活中，每当你有一堆衣服需要整理家人的衣服时，就会发生聚类🧦👕👖🩲. 在数据科学中，聚类用于在尝试分析用户的偏好或确定任何未标记数据集的特征。在某种程度上，聚类有助于理解杂乱的状态，就像是一个袜子抽屉。
+
+[![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
+
+> 🎥单击上图观看视频：麻省理工学院的 John Guttag 介绍聚类
+
+在专业环境中，聚类可用于确定诸如市场细分之类的事情，例如确定哪些年龄组购买哪些商品。另一个用途是异常检测，可能是从信用卡交易数据集中检测欺诈。或者您可以使用聚类来确定一批医学扫描中的肿瘤。
+
+✅ 想一想您是如何在银行、电子商务或商业环境中“意外”遇到聚类的。
+
+> 🎓有趣的是，聚类分析起源于 1930 年代的人类学和心理学领域。你能想象它是如何被使用的吗？
+
+或者，您可以使用它对搜索结果进行分组 - 例如，通过购物链接、图片或评论。当您有一个大型数据集想要减少并且想要对其执行更细粒度的分析时，聚类非常有用，因此该技术可用于在构建其他模型之前了解数据。
+
+✅一旦你的数据被组织成聚类，你就为它分配一个聚类 ID，这个技术在保护数据集的隐私时很有用；您可以改为通过其聚类 ID 来引用数据点，而不是通过更多的可明显区分的数据。您能想到为什么要引用聚类 ID 而不是聚类的其他元素来识别它的其他原因吗？
+
+在此[学习模块中](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)加深您对聚类技术的理解
+
+## 聚类入门
+
+[Scikit-learn](https://scikit-learn.org/stable/modules/clustering.html) 提供了大量的方法来执行聚类。您选择的类型将取决于您的用例。根据文档，每种方法都有不同的好处。以下是 Scikit-learn 支持的方法及其适当用例的简化表：
+
+| 方法名称                     | 用例                                               |
+| ---------------------------- | -------------------------------------------------- |
+| K-Means                      | 通用目的，归纳的                                   |
+| Affinity propagation         | 许多，不均匀的聚类，归纳的                         |
+| Mean-shift                   | 许多，不均匀的聚类，归纳的                         |
+| Spectral clustering          | 少数，甚至聚类，转导的                             |
+| Ward hierarchical clustering | 许多，受约束的聚类，转导的                         |
+| Agglomerative clustering     | 许多，受约束的，非欧几里得距离，转导的             |
+| DBSCAN                       | 非平面几何，不均匀聚类，转导的                     |
+| OPTICS                       | 不平坦的几何形状，具有可变密度的不均匀聚类，转导的 |
+| Gaussian mixtures            | 平面几何，归纳的                                   |
+| BIRCH                        | 具有异常值的大型数据集，归纳的                     |
+
+> 🎓我们如何创建聚类与我们如何将数据点收集到组中有很大关系。让我们分析一些词汇：
+>
+> 🎓 [“转导”与“归纳”](https://wikipedia.org/wiki/Transduction_(machine_learning))
+>
+> 转导推理源自观察到的映射到特定测试用例的训练用例。归纳推理源自映射到一般规则的训练案例，然后才应用于测试案例。
+>
+> 示例：假设您有一个仅部分标记的数据集。有些东西是“记录”，有些是“CD”，有些是空白的。您的工作是为空白提供标签。如果您选择归纳方法，您将训练一个寻找“记录”和“CD”的模型，并将这些标签应用于未标记的数据。这种方法将难以对实际上是“盒式磁带”的东西进行分类。另一方面，转导方法可以更有效地处理这些未知数据，因为它可以将相似的项目组合在一起，然后将标签应用于一个组。在这种情况下，聚类可能反映“圆形音乐事物”和“方形音乐事物”。
+>
+> 🎓 [“非平面”与“平面”几何](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)
+>
+> 源自数学术语，非平面与平面几何是指通过“平面”（[欧几里德](https://wikipedia.org/wiki/Euclidean_geometry)）或“非平面”（非欧几里得）几何方法测量点之间的距离。
+>
+> 在此上下文中，“平面”是指欧几里得几何（其中一部分被教导为“平面”几何），而非平面是指非欧几里得几何。几何与机器学习有什么关系？好吧，作为植根于数学的两个领域，必须有一种通用的方法来测量聚类中点之间的距离，并且可以以“平坦”(flat)或“非平坦”(non-flat)的方式完成，具体取决于数据的性质. [欧几里得距离](https://wikipedia.org/wiki/Euclidean_distance)测量为两点之间线段的长度。[非欧距离](https://wikipedia.org/wiki/Non-Euclidean_geometry)是沿曲线测量的。如果您的可视化数据似乎不存在于平面上，您可能需要使用专门的算法来处理它。
+>
+> ![Flat vs Nonflat Geometry Infographic](../images/flat-nonflat.png)
+> [Dasani Madipalli ](https://twitter.com/dasani_decoded)作图
+>
+> 🎓 ['距离'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
+>
+> 聚类由它们的距离矩阵定义，例如点之间的距离。这个距离可以通过几种方式来测量。欧几里得聚类由点值的平均值定义，并包含“质心”或中心点。因此，距离是通过到该质心的距离来测量的。非欧式距离指的是“聚类中心”，即离其他点最近的点。聚类中心又可以用各种方式定义。
+>
+> 🎓 ['约束'](https://wikipedia.org/wiki/Constrained_clustering)
+>
+> [约束聚类](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf)将“半监督”学习引入到这种无监督方法中。点之间的关系被标记为“无法链接”或“必须链接”，因此对数据集强加了一些规则。
+>
+> 一个例子：如果一个算法在一批未标记或半标记的数据上不受约束，它产生的聚类质量可能很差。在上面的示例中，聚类可能将“圆形音乐事物”和“方形音乐事物”以及“三角形事物”和“饼干”分组。如果给出一些约束或要遵循的规则（“物品必须由塑料制成”、“物品需要能够产生音乐”），这可以帮助“约束”算法做出更好的选择。
+>
+> 🎓 '密度'
+>
+> “嘈杂”的数据被认为是“密集的”。在检查时，每个聚类中的点之间的距离可能或多或少地密集或“拥挤”，因此需要使用适当的聚类方法分析这些数据。[本文](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html)演示了使用 K-Means 聚类与 HDBSCAN 算法探索具有不均匀聚类密度的嘈杂数据集之间的区别。
+
+## 聚类算法
+
+有超过 100 种聚类算法，它们的使用取决于手头数据的性质。让我们讨论一些主要的：
+
+- **层次聚类**。如果一个对象是根据其与附近对象的接近程度而不是较远对象来分类的，则聚类是根据其成员与其他对象之间的距离来形成的。Scikit-learn 的凝聚聚类是分层的。
+
+   ![Hierarchical clustering Infographic](../images/hierarchical.png)
+
+   > [Dasani Madipalli](https://twitter.com/dasani_decoded) 作图
+
+- **质心聚类**。这种流行的算法需要选择“k”或要形成的聚类数量，然后算法确定聚类的中心点并围绕该点收集数据。[K-means 聚类](https://wikipedia.org/wiki/K-means_clustering)是质心聚类的流行版本。中心由最近的平均值确定，因此叫做质心。与聚类的平方距离被最小化。
+
+   ![Centroid clustering Infographic](../images/centroid.png)
+
+   > [Dasani Madipalli](https://twitter.com/dasani_decoded) 作图
+
+- **基于分布的聚类**。基于统计建模，基于分布的聚类中心确定一个数据点属于一个聚类的概率，并相应地分配它。高斯混合方法属于这种类型。
+
+- **基于密度的聚类**。数据点根据它们的密度或它们彼此的分组分配给聚类。远离该组的数据点被视为异常值或噪声。DBSCAN、Mean-shift 和 OPTICS 属于此类聚类。
+
+- **基于网格的聚类**。对于多维数据集，创建一个网格并将数据划分到网格的单元格中，从而创建聚类。
+
+
+
+## 练习 - 对你的数据进行聚类
+
+适当的可视化对聚类作为一种技术有很大帮助，所以让我们从可视化我们的音乐数据开始。这个练习将帮助我们决定我们应该最有效地使用哪种聚类方法来处理这些数据的性质。
+
+1. 打开此文件夹中的 *notebook.ipynb* 文件。
+
+1. 导入 `Seaborn` 包以获得良好的数据可视化。
+
+    ```python
+    !pip install seaborn
+    ```
+
+1. 附加来自 *nigerian-songs.csv* 的歌曲数据。加载包含有关歌曲的一些数据的数据帧。准备好通过导入库和转储数据来探索这些数据：
+
+    ```python
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    
+    df = pd.read_csv("../data/nigerian-songs.csv")
+    df.head()
+    ```
+
+    检查前几行数据：
+
+    |     | name                     | album                        | artist              | artist_top_genre | release_date | length | popularity | danceability | acousticness | energy | instrumentalness | liveness | loudness | speechiness | tempo   | time_signature |
+    | --- | ------------------------ | ---------------------------- | ------------------- | ---------------- | ------------ | ------ | ---------- | ------------ | ------------ | ------ | ---------------- | -------- | -------- | ----------- | ------- | -------------- |
+    | 0   | Sparky                   | Mandy & The Jungle           | Cruel Santino       | alternative r&b  | 2019         | 144000 | 48         | 0.666        | 0.851        | 0.42   | 0.534            | 0.11     | -6.699   | 0.0829      | 133.015 | 5              |
+    | 1   | shuga rush               | EVERYTHING YOU HEARD IS TRUE | Odunsi (The Engine) | afropop          | 2020         | 89488  | 30         | 0.71         | 0.0822       | 0.683  | 0.000169         | 0.101    | -5.64    | 0.36        | 129.993 | 3              |
+    | 2   | LITT!                    | LITT!                        | AYLØ                | indie r&b        | 2018         | 207758 | 40         | 0.836        | 0.272        | 0.564  | 0.000537         | 0.11     | -7.127   | 0.0424      | 130.005 | 4              |
+    | 3   | Confident / Feeling Cool | Enjoy Your Life              | Lady Donli          | nigerian pop     | 2019         | 175135 | 14         | 0.894        | 0.798        | 0.611  | 0.000187         | 0.0964   | -4.961   | 0.113       | 111.087 | 4              |
+    | 4   | wanted you               | rare.                        | Odunsi (The Engine) | afropop          | 2018         | 152049 | 25         | 0.702        | 0.116        | 0.833  | 0.91             | 0.348    | -6.044   | 0.0447      | 105.115 | 4              |
+
+1. 获取有关数据帧的一些信息，调用 `info()`：
+
+    ```python
+    df.info()
+    ```
+
+   输出看起来像这样：
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 530 entries, 0 to 529
+    Data columns (total 16 columns):
+     #   Column            Non-Null Count  Dtype  
+    ---  ------            --------------  -----  
+     0   name              530 non-null    object 
+     1   album             530 non-null    object 
+     2   artist            530 non-null    object 
+     3   artist_top_genre  530 non-null    object 
+     4   release_date      530 non-null    int64  
+     5   length            530 non-null    int64  
+     6   popularity        530 non-null    int64  
+     7   danceability      530 non-null    float64
+     8   acousticness      530 non-null    float64
+     9   energy            530 non-null    float64
+     10  instrumentalness  530 non-null    float64
+     11  liveness          530 non-null    float64
+     12  loudness          530 non-null    float64
+     13  speechiness       530 non-null    float64
+     14  tempo             530 non-null    float64
+     15  time_signature    530 non-null    int64  
+    dtypes: float64(8), int64(4), object(4)
+    memory usage: 66.4+ KB
+    ```
+
+1. 通过调用 `isnull()` 和验证总和为 0 来仔细检查空值：
+
+    ```python
+    df.isnull().sum()
+    ```
+
+    看起来不错：
+
+    ```output
+    name                0
+    album               0
+    artist              0
+    artist_top_genre    0
+    release_date        0
+    length              0
+    popularity          0
+    danceability        0
+    acousticness        0
+    energy              0
+    instrumentalness    0
+    liveness            0
+    loudness            0
+    speechiness         0
+    tempo               0
+    time_signature      0
+    dtype: int64
+    ```
+
+1. 描述数据：
+
+    ```python
+    df.describe()
+    ```
+
+    |       | release_date | length      | popularity | danceability | acousticness | energy   | instrumentalness | liveness | loudness  | speechiness | tempo      | time_signature |
+    | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- |
+    | count | 530          | 530         | 530        | 530          | 530          | 530      | 530              | 530      | 530       | 530         | 530        | 530            |
+    | mean  | 2015.390566  | 222298.1698 | 17.507547  | 0.741619     | 0.265412     | 0.760623 | 0.016305         | 0.147308 | -4.953011 | 0.130748    | 116.487864 | 3.986792       |
+    | std   | 3.131688     | 39696.82226 | 18.992212  | 0.117522     | 0.208342     | 0.148533 | 0.090321         | 0.123588 | 2.464186  | 0.092939    | 23.518601  | 0.333701       |
+    | min   | 1998         | 89488       | 0          | 0.255        | 0.000665     | 0.111    | 0                | 0.0283   | -19.362   | 0.0278      | 61.695     | 3              |
+    | 25%   | 2014         | 199305      | 0          | 0.681        | 0.089525     | 0.669    | 0                | 0.07565  | -6.29875  | 0.0591      | 102.96125  | 4              |
+    | 50%   | 2016         | 218509      | 13         | 0.761        | 0.2205       | 0.7845   | 0.000004         | 0.1035   | -4.5585   | 0.09795     | 112.7145   | 4              |
+    | 75%   | 2017         | 242098.5    | 31         | 0.8295       | 0.403        | 0.87575  | 0.000234         | 0.164    | -3.331    | 0.177       | 125.03925  | 4              |
+    | max   | 2020         | 511738      | 73         | 0.966        | 0.954        | 0.995    | 0.91             | 0.811    | 0.582     | 0.514       | 206.007    | 5              |
+
+> 🤔如果我们正在使用聚类，一种不需要标记数据的无监督方法，为什么我们用标签显示这些数据？在数据探索阶段，它们派上用场，但它们不是聚类算法工作所必需的。您也可以删除列标题并按列号引用数据。
+
+查看数据的普遍值。请注意，流行度可以是“0”，表示没有排名的歌曲。让我们尽快删除它们。
+
+1. 使用条形图找出最受欢迎的类型：
+
+    ```python
+    import seaborn as sns
+    
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top[:5].index,y=top[:5].values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+    ![most popular](../images/popular.png)
+
+✅如果您想查看更多顶部值，请将顶部更改`[:5]`为更大的值，或将其删除以查看全部。
+
+请注意，当顶级流派被描述为“缺失”时，这意味着 Spotify 没有对其进行分类，所以让我们避免它。
+
+1. 通过过滤掉丢失的数据来避免
+
+    ```python
+    df = df[df['artist_top_genre'] != 'Missing']
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top.index,y=top.values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+    现在重新检查 genres：
+
+    ![most popular](../images/all-genres.png)
+
+1. 到目前为止，前三大流派主导了这个数据集。让我们专注于 `afro dancehall`，`afropop` 和 `nigerian pop`，另外过滤数据集以删除任何具有 0 流行度值的内容（这意味着它在数据集中没有被归类为流行度并且可以被视为我们的目的的噪音）：
+
+    ```python
+    df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]
+    df = df[(df['popularity'] > 0)]
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top.index,y=top.values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+1. 做一个快速测试，看看数据是否以任何特别强的方式相关：
+
+    ```python
+    corrmat = df.corr()
+    f, ax = plt.subplots(figsize=(12, 9))
+    sns.heatmap(corrmat, vmax=.8, square=True)
+    ```
+
+    ![correlations](../images/correlation.png)
+
+    > 唯一强相关性是 `energy` 和之间 `loudness`，这并不奇怪，因为嘈杂的音乐通常非常有活力。否则，相关性相对较弱。看看聚类算法可以如何处理这些数据会很有趣。
+    >
+    > > 🎓请注意，相关性并不意味着因果关系！我们有相关性的证据，但没有因果关系的证据。一个[有趣的网站](https://tylervigen.com/spurious-correlations)有一些强调这一点的视觉效果。
+
+这个数据集是否围绕歌曲的流行度和可舞性有任何收敛？FacetGrid 显示无论流派如何，都有同心圆排列。对于这种类型，尼日利亚人的口味是否会在某种程度的可舞性上趋于一致？
+
+✅ 尝试不同的数据点（能量、响度、语音）和更多或不同的音乐类型。你能发现什么？查看 `df.describe()` 表格以了解数据点的一般分布。
+
+### 练习 - 数据分布
+
+这三种流派是否因其受欢迎程度而对其可舞性的看法有显着差异？
+
+1. 检查我们沿给定 x 和 y 轴的流行度和可舞性的前三种类型数据分布。
+
+    ```python
+    sns.set_theme(style="ticks")
+    
+    g = sns.jointplot(
+        data=df,
+        x="popularity", y="danceability", hue="artist_top_genre",
+        kind="kde",
+    )
+    ```
+
+    您可以发现围绕一般收敛点的同心圆，显示点的分布。
+
+    > 🎓请注意，此示例使用 KDE（核密度估计）图，该图使用连续概率密度曲线表示数据。这允许我们在处理多个分布时解释数据。
+
+    总的来说，这三种流派在流行度和可舞性方面松散地对齐。在这种松散对齐的数据中确定聚类将是一个挑战：
+
+    ![distribution](../images/distribution.png)
+
+1. 创建散点图：
+
+    ```python
+    sns.FacetGrid(df, hue="artist_top_genre", size=5) \
+       .map(plt.scatter, "popularity", "danceability") \
+       .add_legend()
+    ```
+
+    相同轴的散点图显示了类似的收敛模式
+
+    ![Facetgrid](../images/facetgrid.png)
+
+一般来说，对于聚类，你可以使用散点图来展示数据的聚类，所以掌握这种类型的可视化是非常有用的。在下一课中，我们将使用过滤后的数据并使用 k-means 聚类来发现这些数据中以有趣方式重叠的组。
+
+---
+
+## 🚀挑战
+
+为下一课做准备，制作一张图表，说明您可能会在生产环境中发现和使用的各种聚类算法。
+
+聚类试图解决什么样的问题？
+
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+
+## 复习与自学
+
+在应用聚类算法之前，正如我们所了解的，了解数据集的性质是一个好主意。[在此处](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)阅读有关此主题的更多[信息](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
+
+[这篇有用的文章将](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/)引导您了解各种聚类算法在给定不同数据形状的情况下的不同行为方式。
+
+## 作业
+
+[研究用于聚类的其他可视化](./assignment.zh-cn.md)
diff --git a/5-Clustering/1-Visualize/translations/assignment.it.md b/5-Clustering/1-Visualize/translations/assignment.it.md
new file mode 100644
index 000000000..dad3d7081
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Ricercare altre visualizzazioni per il clustering
+
+## Istruzioni
+
+In questa lezione, si è lavorato con alcune tecniche di visualizzazione per capire come tracciare i propri dati in preparazione per il clustering. I grafici a dispersione, in particolare, sono utili per trovare gruppi di oggetti. Ricercare modi diversi e librerie diverse per creare grafici a dispersione e documentare il proprio lavoro in un notebook. Si possono utilizzare i dati di questa lezione, di altre lezioni o dei dati che si sono procurati in autonomia (per favore citare la fonte, comunque, nel proprio notebook). Tracciare alcuni dati usando i grafici a dispersione e spiegare cosa si scopre.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | -------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | ----------------------------------- |
+|          | Viene presentato un notebook con cinque grafici a dispersione ben documentati | Un notebook viene presentato con meno di cinque grafici a dispersione ed è meno ben documentato | Viene presentato un notebook incompleto |
diff --git a/5-Clustering/1-Visualize/translations/assignment.zh-cn.md b/5-Clustering/1-Visualize/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..512c880cf
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/assignment.zh-cn.md
@@ -0,0 +1,13 @@
+# 研究用于聚类的其他可视化
+
+## 说明
+
+在本节课中，您使用了一些可视化技术来掌握绘制数据图，为聚类数据做准备。散点图在寻找一组对象时尤其有用。研究不同的方法和不同的库来创建散点图，并在 notebook 上记录你的工作。你可以使用这节课的数据，其他课的数据，或者你自己的数据（但是，请把它的来源记在你的 notebook 上）。用散点图绘制一些数据，并解释你的发现。
+
+## 评判规则
+
+
+| 评判标准 | 优秀                             | 中规中矩                                        | 仍需努力             |
+| -------- | -------------------------------- | ----------------------------------------------- | -------------------- |
+|          | notebook 上有五个详细文档的散点图 | notebook 上的散点图少于 5 个，而且文档写得不太详细 | 一个不完整的 notebook |
+
diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index 153932e63..9c3c1127a 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -4,7 +4,7 @@
 
 > 🎥 Click the image above for a video: Andrew Ng explains clustering
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
 
 In this lesson, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
 
@@ -224,7 +224,7 @@ Previously, you surmised that, because you have targeted 3 song genres, you shou
 
 ## Variance
 
-Variance is defined as "the average of the squared differences from the Mean."[source](https://www.mathsisfun.com/data/standard-deviation.html) In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean. 
+Variance is defined as "the average of the squared differences from the Mean" [source](https://www.mathsisfun.com/data/standard-deviation.html). In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean. 
 
 ✅ This is a great moment to think about all the ways you could correct this issue. Tweak the data a bit more? Use different columns? Use a different algorithm? Hint: Try [scaling your data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) to normalize it and test other columns.
 
@@ -238,13 +238,13 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
 
 Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
 
 ## Review & Self Study
 
-Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+Take a look at a K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
 
-Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
+Also, take a look at [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
 
 ## Assignment
 
diff --git a/5-Clustering/2-K-Means/images/kmeans.gif b/5-Clustering/2-K-Means/images/kmeans.gif
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index 000000000..ece19fa7e
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diff --git a/5-Clustering/2-K-Means/images/r_learners_sm.jpeg b/5-Clustering/2-K-Means/images/r_learners_sm.jpeg
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diff --git a/5-Clustering/2-K-Means/solution/Julia/README.md b/5-Clustering/2-K-Means/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/5-Clustering/2-K-Means/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
new file mode 100644
index 000000000..f0dea8ce1
--- /dev/null
+++ b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -0,0 +1,635 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "anaconda-cloud": "",
+    "kernelspec": {
+      "display_name": "R",
+      "langauge": "R",
+      "name": "ir"
+    },
+    "language_info": {
+      "codemirror_mode": "r",
+      "file_extension": ".r",
+      "mimetype": "text/x-r-source",
+      "name": "R",
+      "pygments_lexer": "r",
+      "version": "3.4.1"
+    },
+    "colab": {
+      "name": "lesson_14.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GULATlQXLXyR"
+      },
+      "source": [
+        "## Explore K-Means clustering using R and Tidy data principles.\n",
+        "\n",
+        "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)\n",
+        "\n",
+        "In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 🧑‍🤝‍🧑), and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!\n",
+        "\n",
+        "Terms you will learn about:\n",
+        "\n",
+        "-   Silhouette scoring\n",
+        "\n",
+        "-   Elbow method\n",
+        "\n",
+        "-   Inertia\n",
+        "\n",
+        "-   Variance\n",
+        "\n",
+        "### **Introduction**\n",
+        "\n",
+        "[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) is a method derived from the domain of signal processing. It is used to divide and partition groups of data into `k clusters` based on similarities in their features.\n",
+        "\n",
+        "The clusters can be visualized as [Voronoi diagrams](https://wikipedia.org/wiki/Voronoi_diagram), which include a point (or 'seed') and its corresponding region.\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/voronoi.png\"\n",
+        "   width=\"500\"/>\n",
+        "   <figcaption>Infographic by Jen Looper</figcaption>\n",
+        "\n",
+        "\n",
+        "K-Means clustering has the following steps:\n",
+        "\n",
+        "1.  The data scientist starts by specifying the desired number of clusters to be created.\n",
+        "\n",
+        "2.  Next, the algorithm randomly selects K observations from the data set to serve as the initial centers for the clusters (i.e., centroids).\n",
+        "\n",
+        "3.  Next, each of the remaining observations is assigned to its closest centroid.\n",
+        "\n",
+        "4.  Next, the new means of each cluster is computed and the centroid is moved to the mean.\n",
+        "\n",
+        "5.  Now that the centers have been recalculated, every observation is checked again to see if it might be closer to a different cluster. All the objects are reassigned again using the updated cluster means. The cluster assignment and centroid update steps are iteratively repeated until the cluster assignments stop changing (i.e., when convergence is achieved). Typically, the algorithm terminates when each new iteration results in negligible movement of centroids and the clusters become static.\n",
+        "\n",
+        "<div>\n",
+        "\n",
+        "> Note that due to randomization of the initial k observations used as the starting centroids, we can get slightly different results each time we apply the procedure. For this reason, most algorithms use several *random starts* and choose the iteration with the lowest WCSS. As such, it is strongly recommended to always run K-Means with several values of *nstart* to avoid an *undesirable local optimum.*\n",
+        "\n",
+        "</div>\n",
+        "\n",
+        "This short animation using the [artwork](https://github.com/allisonhorst/stats-illustrations) of Allison Horst explains the clustering process:\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/kmeans.gif\"\n",
+        "   width=\"550\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "\n",
+        "\n",
+        "A fundamental question that arises in clustering is this: how do you know how many clusters to separate your data into? One drawback of using K-Means includes the fact that you will need to establish `k`, that is the number of `centroids`. Fortunately the `elbow method` helps to estimate a good starting value for `k`. You'll try it in a minute.\n",
+        "\n",
+        "### \n",
+        "\n",
+        "**Prerequisite**\n",
+        "\n",
+        "We'll pick off right from where we stopped in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), where we analysed the data set, made lots of visualizations and filtered the data set to observations of interest. Be sure to check it out!\n",
+        "\n",
+        "We'll require some packages to knock-off this module. You can have them installed as: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+        "\n",
+        "Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case some are missing.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ah_tBi58LXyi"
+      },
+      "source": [
+        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+        "\n",
+        "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7e--UCUTLXym"
+      },
+      "source": [
+        "Let's hit the ground running!\n",
+        "\n",
+        "## 1. A dance with data: Narrow down to the 3 most popular music genres\n",
+        "\n",
+        "This is a recap of what we did in the previous lesson. Let's slice and dice some data!\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ycamx7GGLXyn"
+      },
+      "source": [
+        "# Load the core tidyverse and make it available in your current R session\n",
+        "library(tidyverse)\n",
+        "\n",
+        "# Import the data into a tibble\n",
+        "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+        "\n",
+        "# Narrow down to top 3 popular genres\n",
+        "nigerian_songs <- df %>% \n",
+        "  # Concentrate on top 3 genres\n",
+        "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+        "  # Remove unclassified observations\n",
+        "  filter(popularity != 0)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Visualize popular genres using bar plots\n",
+        "theme_set(theme_light())\n",
+        "nigerian_songs %>%\n",
+        "  count(artist_top_genre) %>%\n",
+        "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+        "                       fill = artist_top_genre)) +\n",
+        "  geom_col(alpha = 0.8) +\n",
+        "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+        "  ggtitle(\"Top genres\") +\n",
+        "  theme(plot.title = element_text(hjust = 0.5))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b5h5zmkPLXyp"
+      },
+      "source": [
+        "🤩 That went well!\n",
+        "\n",
+        "## 2. More data exploration.\n",
+        "\n",
+        "How clean is this data? Let's check for outliers using box plots. We will concentrate on numeric columns with fewer outliers (although you could clean out the outliers). Boxplots can show the range of the data and will help choose which columns to use. Note, Boxplots do not show variance, an important element of good clusterable data. Please see [this discussion](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) for further reading.\n",
+        "\n",
+        "[Boxplots](https://en.wikipedia.org/wiki/Box_plot) are used to graphically depict the distribution of `numeric` data, so let's start by *selecting* all numeric columns alongside the popular music genres.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HhNreJKLLXyq"
+      },
+      "source": [
+        "# Select top genre column and all other numeric columns\n",
+        "df_numeric <- nigerian_songs %>% \n",
+        "  select(artist_top_genre, where(is.numeric)) \n",
+        "\n",
+        "# Display the data\n",
+        "df_numeric %>% \n",
+        "  slice_head(n = 5)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uYXrwJRaLXyq"
+      },
+      "source": [
+        "See how the selection helper `where` makes this easy 💁? Explore such other functions [here](https://tidyselect.r-lib.org/).\n",
+        "\n",
+        "Since we'll be making a boxplot for each numeric features and we want to avoid using loops, let's reformat our data into a *longer* format that will allow us to take advantage of `facets` - subplots that each display one subset of the data.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gd5bR3f8LXys"
+      },
+      "source": [
+        "# Pivot data from wide to long\n",
+        "df_numeric_long <- df_numeric %>% \n",
+        "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+        "\n",
+        "# Print out data\n",
+        "df_numeric_long %>% \n",
+        "  slice_head(n = 15)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-7tE1swnLXyv"
+      },
+      "source": [
+        "Much longer! Now time for some `ggplots`! So what `geom` will we use?\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "r88bIsyuLXyy"
+      },
+      "source": [
+        "# Make a box plot\n",
+        "df_numeric_long %>% \n",
+        "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+        "  geom_boxplot() +\n",
+        "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+        "  theme(legend.position = \"none\")\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EYVyKIUELXyz"
+      },
+      "source": [
+        "Easy-gg!\n",
+        "\n",
+        "Now we can see this data is a little noisy: by observing each column as a boxplot, you can see outliers. You could go through the dataset and remove these outliers, but that would make the data pretty minimal.\n",
+        "\n",
+        "For now, let's choose which columns we will use for our clustering exercise. Let's pick the numeric columns with similar ranges. We could encode the `artist_top_genre` as numeric but we'll drop it for now.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-wkpINyZLXy0"
+      },
+      "source": [
+        "# Select variables with similar ranges\n",
+        "df_numeric_select <- df_numeric %>% \n",
+        "  select(popularity, danceability, acousticness, loudness, energy) \n",
+        "\n",
+        "# Normalize data\n",
+        "# df_numeric_select <- scale(df_numeric_select)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D7dLzgpqLXy1"
+      },
+      "source": [
+        "## 3. Computing k-means clustering in R\n",
+        "\n",
+        "We can compute k-means in R with the built-in `kmeans` function, see `help(\"kmeans()\")`. `kmeans()` function accepts a data frame with all numeric columns as it's primary argument.\n",
+        "\n",
+        "The first step when using k-means clustering is to specify the number of clusters (k) that will be generated in the final solution. We know there are 3 song genres that we carved out of the dataset, so let's try 3:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uC4EQ5w7LXy5"
+      },
+      "source": [
+        "set.seed(2056)\n",
+        "# Kmeans clustering for 3 clusters\n",
+        "kclust <- kmeans(\n",
+        "  df_numeric_select,\n",
+        "  # Specify the number of clusters\n",
+        "  centers = 3,\n",
+        "  # How many random initial configurations\n",
+        "  nstart = 25\n",
+        ")\n",
+        "\n",
+        "# Display clustering object\n",
+        "kclust\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hzfhscWrLXy-"
+      },
+      "source": [
+        "The kmeans object contains several bits of information which is well explained in `help(\"kmeans()\")`. For now, let's focus on a few. We see that the data has been grouped into 3 clusters of sizes 65, 110, 111. The output also contains the cluster centers (means) for the 3 groups across the 5 variables.\n",
+        "\n",
+        "The clustering vector is the cluster assignment for each observation. Let's use the `augment` function to add the cluster assignment the original data set.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0XwwpFGQLXy_"
+      },
+      "source": [
+        "# Add predicted cluster assignment to data set\n",
+        "augment(kclust, df_numeric_select) %>% \n",
+        "  relocate(.cluster) %>% \n",
+        "  slice_head(n = 10)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NXIVXXACLXzA"
+      },
+      "source": [
+        "Perfect, we have just partitioned our data set into a set of 3 groups. So, how good is our clustering 🤷? Let's take a look at the `Silhouette score`\n",
+        "\n",
+        "### **Silhouette score**\n",
+        "\n",
+        "[Silhouette analysis](https://en.wikipedia.org/wiki/Silhouette_(clustering)) can be used to study the separation distance between the resulting clusters. This score varies from -1 to 1, and if the score is near 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters.[source](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+        "\n",
+        "The average silhouette method computes the average silhouette of observations for different values of *k*. A high average silhouette score indicates a good clustering.\n",
+        "\n",
+        "The `silhouette` function in the cluster package to compuate the average silhouette width.\n",
+        "\n",
+        "> The silhouette can be calculated with any [distance](https://en.wikipedia.org/wiki/Distance \"Distance\") metric, such as the [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") or the [Manhattan distance](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") which we discussed in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Jn0McL28LXzB"
+      },
+      "source": [
+        "# Load cluster package\n",
+        "library(cluster)\n",
+        "\n",
+        "# Compute average silhouette score\n",
+        "ss <- silhouette(kclust$cluster,\n",
+        "                 # Compute euclidean distance\n",
+        "                 dist = dist(df_numeric_select))\n",
+        "mean(ss[, 3])\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QyQRn97nLXzC"
+      },
+      "source": [
+        "Our score is **.549**, so right in the middle. This indicates that our data is not particularly well-suited to this type of clustering. Let's see whether we can confirm this hunch visually. The [factoextra package](https://rpkgs.datanovia.com/factoextra/index.html) provides functions (`fviz_cluster()`) to visualize clustering.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7a6Km1_FLXzD"
+      },
+      "source": [
+        "library(factoextra)\n",
+        "\n",
+        "# Visualize clustering results\n",
+        "fviz_cluster(kclust, df_numeric_select)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IBwCWt-0LXzD"
+      },
+      "source": [
+        "The overlap in clusters indicates that our data is not particularly well-suited to this type of clustering but let's continue.\n",
+        "\n",
+        "## 4. Determining optimal clusters\n",
+        "\n",
+        "A fundamental question that often arises in K-Means clustering is this - without known class labels, how do you know how many clusters to separate your data into?\n",
+        "\n",
+        "One way we can try to find out is to use a data sample to `create a series of clustering models` with an incrementing number of clusters (e.g from 1-10), and evaluate clustering metrics such as the **Silhouette score.**\n",
+        "\n",
+        "Let's determine the optimal number of clusters by computing the clustering algorithm for different values of *k* and evaluating the **Within Cluster Sum of Squares** (WCSS). The total within-cluster sum of square (WCSS) measures the compactness of the clustering and we want it to be as small as possible, with lower values meaning that the data points are closer.\n",
+        "\n",
+        "Let's explore the effect of different choices of `k`, from 1 to 10, on this clustering.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hSeIiylDLXzE"
+      },
+      "source": [
+        "# Create a series of clustering models\n",
+        "kclusts <- tibble(k = 1:10) %>% \n",
+        "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+        "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+        "  # Farm out clustering metrics eg WCSS\n",
+        "         glanced = map(model, ~ glance(.x))) %>% \n",
+        "  unnest(cols = glanced)\n",
+        "  \n",
+        "\n",
+        "# View clustering rsulsts\n",
+        "kclusts\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m7rS2U1eLXzE"
+      },
+      "source": [
+        "Now that we have the total within-cluster sum-of-squares (tot.withinss) for each clustering algorithm with center *k*, we use the [elbow method](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) to find the optimal number of clusters. The method consists of plotting the WCSS as a function of the number of clusters, and picking the [elbow of the curve](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") as the number of clusters to use.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "o_DjHGItLXzF"
+      },
+      "source": [
+        "set.seed(2056)\n",
+        "# Use elbow method to determine optimum number of clusters\n",
+        "kclusts %>% \n",
+        "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+        "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+        "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pLYyt5XSLXzG"
+      },
+      "source": [
+        "The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an `elbow` 💪in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points.\n",
+        "\n",
+        "We can now go ahead and extract the clustering model where `k = 3`:\n",
+        "\n",
+        "> `pull()`: used to extract a single column\n",
+        ">\n",
+        "> `pluck()`: used to index data structures such as lists\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JP_JPKBILXzG"
+      },
+      "source": [
+        "# Extract k = 3 clustering\n",
+        "final_kmeans <- kclusts %>% \n",
+        "  filter(k == 3) %>% \n",
+        "  pull(model) %>% \n",
+        "  pluck(1)\n",
+        "\n",
+        "\n",
+        "final_kmeans\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l_PDTu8tLXzI"
+      },
+      "source": [
+        "Great! Let's go ahead and visualize the clusters obtained. Care for some interactivity using `plotly`?\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dNcleFe-LXzJ"
+      },
+      "source": [
+        "# Add predicted cluster assignment to data set\n",
+        "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+        "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+        "\n",
+        "# Plot cluster assignments\n",
+        "clust_plt <- results %>% \n",
+        "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+        "  geom_point(size = 2, alpha = 0.8) +\n",
+        "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+        "\n",
+        "ggplotly(clust_plt)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6JUM_51VLXzK"
+      },
+      "source": [
+        "Perhaps we would have expected that each cluster (represented by different colors) would have distinct genres (represented by different shapes).\n",
+        "\n",
+        "Let's take a look at the model's accuracy.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HdIMUGq7LXzL"
+      },
+      "source": [
+        "# Assign genres to predefined integers\n",
+        "label_count <- results %>% \n",
+        "  group_by(artist_top_genre) %>% \n",
+        "  mutate(id = cur_group_id()) %>% \n",
+        "  ungroup() %>% \n",
+        "  summarise(correct_labels = sum(.cluster == id))\n",
+        "\n",
+        "\n",
+        "# Print results  \n",
+        "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+        "\n",
+        "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C50wvaAOLXzM"
+      },
+      "source": [
+        "This model's accuracy is not bad, but not great. It may be that the data may not lend itself well to K-Means Clustering. This data is too imbalanced, too little correlated and there is too much variance between the column values to cluster well. In fact, the clusters that form are probably heavily influenced or skewed by the three genre categories we defined above.\n",
+        "\n",
+        "Nevertheless, that was quite a learning process!\n",
+        "\n",
+        "In Scikit-learn's documentation, you can see that a model like this one, with clusters not very well demarcated, has a 'variance' problem:\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/problems.png\"\n",
+        "   width=\"500\"/>\n",
+        "   <figcaption>Infographic from Scikit-learn</figcaption>\n",
+        "\n",
+        "\n",
+        "\n",
+        "## **Variance**\n",
+        "\n",
+        "Variance is defined as \"the average of the squared differences from the Mean\" [source](https://www.mathsisfun.com/data/standard-deviation.html). In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.\n",
+        "\n",
+        "✅ This is a great moment to think about all the ways you could correct this issue. Tweak the data a bit more? Use different columns? Use a different algorithm? Hint: Try [scaling your data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) to normalize it and test other columns.\n",
+        "\n",
+        "> Try this '[variance calculator](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' to understand the concept a bit more.\n",
+        "\n",
+        "------------------------------------------------------------------------\n",
+        "\n",
+        "## **🚀Challenge**\n",
+        "\n",
+        "Spend some time with this notebook, tweaking parameters. Can you improve the accuracy of the model by cleaning the data more (removing outliers, for example)? You can use weights to give more weight to given data samples. What else can you do to create better clusters?\n",
+        "\n",
+        "Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+        "\n",
+        "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)\n",
+        "\n",
+        "## **Review & Self Study**\n",
+        "\n",
+        "-   Take a look at a K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?\n",
+        "\n",
+        "-   Also, take a look at [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.\n",
+        "\n",
+        "Want to try out your newly acquired clustering skills to data sets that lend well to K-Means clustering? Please see:\n",
+        "\n",
+        "-   [Train and Evaluate Clustering Models](https://rpubs.com/eR_ic/clustering) using Tidymodels and friends\n",
+        "\n",
+        "-   [K-means Cluster Analysis](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide\n",
+        "\n",
+        "- [K-means clustering with tidy data principles](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+        "\n",
+        "## **Assignment**\n",
+        "\n",
+        "[Try different clustering methods](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+        "\n",
+        "## THANK YOU TO:\n",
+        "\n",
+        "[Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\n",
+        "\n",
+        "[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+        "\n",
+        "Happy Learning,\n",
+        "\n",
+        "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+        "   width=\"500\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
new file mode 100644
index 000000000..1198f5907
--- /dev/null
+++ b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
@@ -0,0 +1,392 @@
+---
+title: 'K-Means Clustering using Tidymodels and friends'
+output:
+  html_document:
+    #css: style_7.css
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Explore K-Means clustering using R and Tidy data principles.
+
+### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+
+In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 🧑‍🤝‍🧑), and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
+
+Terms you will learn about:
+
+-   Silhouette scoring
+
+-   Elbow method
+
+-   Inertia
+
+-   Variance
+
+### **Introduction**
+
+[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) is a method derived from the domain of signal processing. It is used to divide and partition groups of data into `k clusters` based on similarities in their features.
+
+The clusters can be visualized as [Voronoi diagrams](https://wikipedia.org/wiki/Voronoi_diagram), which include a point (or 'seed') and its corresponding region.
+
+![Infographic by Jen Looper](../../images/voronoi.png)
+
+K-Means clustering has the following steps:
+
+1.  The data scientist starts by specifying the desired number of clusters to be created.
+
+2.  Next, the algorithm randomly selects K observations from the data set to serve as the initial centers for the clusters (i.e., centroids).
+
+3.  Next, each of the remaining observations is assigned to its closest centroid.
+
+4.  Next, the new means of each cluster is computed and the centroid is moved to the mean.
+
+5.  Now that the centers have been recalculated, every observation is checked again to see if it might be closer to a different cluster. All the objects are reassigned again using the updated cluster means. The cluster assignment and centroid update steps are iteratively repeated until the cluster assignments stop changing (i.e., when convergence is achieved). Typically, the algorithm terminates when each new iteration results in negligible movement of centroids and the clusters become static.
+
+<div>
+
+> Note that due to randomization of the initial k observations used as the starting centroids, we can get slightly different results each time we apply the procedure. For this reason, most algorithms use several *random starts* and choose the iteration with the lowest WCSS. As such, it is strongly recommended to always run K-Means with several values of *nstart* to avoid an *undesirable local optimum.*
+
+</div>
+
+This short animation using the [artwork](https://github.com/allisonhorst/stats-illustrations) of Allison Horst explains the clustering process:
+
+![Artwork by \@allison_horst](../../images/kmeans.gif)
+
+A fundamental question that arises in clustering is this: how do you know how many clusters to separate your data into? One drawback of using K-Means includes the fact that you will need to establish `k`, that is the number of `centroids`. Fortunately the `elbow method` helps to estimate a good starting value for `k`. You'll try it in a minute.
+
+### 
+
+**Prerequisite**
+
+We'll pick off right from where we stopped in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), where we analysed the data set, made lots of visualizations and filtered the data set to observations of interest. Be sure to check it out!
+
+We'll require some packages to knock-off this module. You can have them installed as: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`
+
+Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case some are missing.
+
+```{r}
+suppressWarnings(if(!require("pacman")) install.packages("pacman"))
+
+pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')
+```
+
+Let's hit the ground running!
+
+## 1. A dance with data: Narrow down to the 3 most popular music genres
+
+This is a recap of what we did in the previous lesson. Let's slice and dice some data!
+
+```{r message=F, warning=F}
+# Load the core tidyverse and make it available in your current R session
+library(tidyverse)
+
+# Import the data into a tibble
+df <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv", show_col_types = FALSE)
+
+# Narrow down to top 3 popular genres
+nigerian_songs <- df %>% 
+  # Concentrate on top 3 genres
+  filter(artist_top_genre %in% c("afro dancehall", "afropop","nigerian pop")) %>% 
+  # Remove unclassified observations
+  filter(popularity != 0)
+
+
+
+# Visualize popular genres using bar plots
+theme_set(theme_light())
+nigerian_songs %>%
+  count(artist_top_genre) %>%
+  ggplot(mapping = aes(x = artist_top_genre, y = n,
+                       fill = artist_top_genre)) +
+  geom_col(alpha = 0.8) +
+  paletteer::scale_fill_paletteer_d("ggsci::category10_d3") +
+  ggtitle("Top genres") +
+  theme(plot.title = element_text(hjust = 0.5))
+
+
+```
+
+🤩 That went well!
+
+## 2. More data exploration.
+
+How clean is this data? Let's check for outliers using box plots. We will concentrate on numeric columns with fewer outliers (although you could clean out the outliers). Boxplots can show the range of the data and will help choose which columns to use. Note, Boxplots do not show variance, an important element of good clusterable data. Please see [this discussion](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) for further reading.
+
+[Boxplots](https://en.wikipedia.org/wiki/Box_plot) are used to graphically depict the distribution of `numeric` data, so let's start by *selecting* all numeric columns alongside the popular music genres.
+
+```{r select}
+# Select top genre column and all other numeric columns
+df_numeric <- nigerian_songs %>% 
+  select(artist_top_genre, where(is.numeric)) 
+
+# Display the data
+df_numeric %>% 
+  slice_head(n = 5)
+
+```
+
+See how the selection helper `where` makes this easy 💁? Explore such other functions [here](https://tidyselect.r-lib.org/).
+
+Since we'll be making a boxplot for each numeric features and we want to avoid using loops, let's reformat our data into a *longer* format that will allow us to take advantage of `facets` - subplots that each display one subset of the data.
+
+```{r pivot_longer}
+# Pivot data from wide to long
+df_numeric_long <- df_numeric %>% 
+  pivot_longer(!artist_top_genre, names_to = "feature_names", values_to = "values") 
+
+# Print out data
+df_numeric_long %>% 
+  slice_head(n = 15)
+```
+
+Much longer! Now time for some `ggplots`! So what `geom` will we use?
+
+```{r}
+# Make a box plot
+df_numeric_long %>% 
+  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +
+  geom_boxplot() +
+  facet_wrap(~ feature_names, ncol = 4, scales = "free") +
+  theme(legend.position = "none")
+```
+
+Easy-gg!
+
+Now we can see this data is a little noisy: by observing each column as a boxplot, you can see outliers. You could go through the dataset and remove these outliers, but that would make the data pretty minimal.
+
+For now, let's choose which columns we will use for our clustering exercise. Let's pick the numeric columns with similar ranges. We could encode the `artist_top_genre` as numeric but we'll drop it for now.
+
+```{r select_columns}
+# Select variables with similar ranges
+df_numeric_select <- df_numeric %>% 
+  select(popularity, danceability, acousticness, loudness, energy) 
+
+# Normalize data
+# df_numeric_select <- scale(df_numeric_select)
+```
+
+## 3. Computing k-means clustering in R
+
+We can compute k-means in R with the built-in `kmeans` function, see `help("kmeans()")`. `kmeans()` function accepts a data frame with all numeric columns as it's primary argument.
+
+The first step when using k-means clustering is to specify the number of clusters (k) that will be generated in the final solution. We know there are 3 song genres that we carved out of the dataset, so let's try 3:
+
+```{r kmeans}
+set.seed(2056)
+# Kmeans clustering for 3 clusters
+kclust <- kmeans(
+  df_numeric_select,
+  # Specify the number of clusters
+  centers = 3,
+  # How many random initial configurations
+  nstart = 25
+)
+
+# Display clustering object
+kclust
+```
+
+The kmeans object contains several bits of information which is well explained in `help("kmeans()")`. For now, let's focus on a few. We see that the data has been grouped into 3 clusters of sizes 65, 110, 111. The output also contains the cluster centers (means) for the 3 groups across the 5 variables.
+
+The clustering vector is the cluster assignment for each observation. Let's use the `augment` function to add the cluster assignment the original data set.
+
+```{r augment}
+# Add predicted cluster assignment to data set
+augment(kclust, df_numeric_select) %>% 
+  relocate(.cluster) %>% 
+  slice_head(n = 10)
+```
+
+Perfect, we have just partitioned our data set into a set of 3 groups. So, how good is our clustering 🤷? Let's take a look at the `Silhouette score`
+
+### **Silhouette score**
+
+[Silhouette analysis](https://en.wikipedia.org/wiki/Silhouette_(clustering)) can be used to study the separation distance between the resulting clusters. This score varies from -1 to 1, and if the score is near 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters.[source](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).
+
+The average silhouette method computes the average silhouette of observations for different values of *k*. A high average silhouette score indicates a good clustering.
+
+The `silhouette` function in the cluster package to compuate the average silhouette width.
+
+> The silhouette can be calculated with any [distance](https://en.wikipedia.org/wiki/Distance "Distance") metric, such as the [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance "Euclidean distance") or the [Manhattan distance](https://en.wikipedia.org/wiki/Manhattan_distance "Manhattan distance") which we discussed in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).
+
+```{r}
+# Load cluster package
+library(cluster)
+
+# Compute average silhouette score
+ss <- silhouette(kclust$cluster,
+                 # Compute euclidean distance
+                 dist = dist(df_numeric_select))
+mean(ss[, 3])
+
+```
+
+Our score is **.549**, so right in the middle. This indicates that our data is not particularly well-suited to this type of clustering. Let's see whether we can confirm this hunch visually. The [factoextra package](https://rpkgs.datanovia.com/factoextra/index.html) provides functions (`fviz_cluster()`) to visualize clustering.
+
+```{r fviz_cluster}
+library(factoextra)
+
+# Visualize clustering results
+fviz_cluster(kclust, df_numeric_select)
+
+```
+
+The overlap in clusters indicates that our data is not particularly well-suited to this type of clustering but let's continue.
+
+## 4. Determining optimal clusters
+
+A fundamental question that often arises in K-Means clustering is this - without known class labels, how do you know how many clusters to separate your data into?
+
+One way we can try to find out is to use a data sample to `create a series of clustering models` with an incrementing number of clusters (e.g from 1-10), and evaluate clustering metrics such as the **Silhouette score.**
+
+Let's determine the optimal number of clusters by computing the clustering algorithm for different values of *k* and evaluating the **Within Cluster Sum of Squares** (WCSS). The total within-cluster sum of square (WCSS) measures the compactness of the clustering and we want it to be as small as possible, with lower values meaning that the data points are closer.
+
+Let's explore the effect of different choices of `k`, from 1 to 10, on this clustering.
+
+```{r}
+# Create a series of clustering models
+kclusts <- tibble(k = 1:10) %>% 
+  # Perform kmeans clustering for 1,2,3 ... ,10 clusters
+  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),
+  # Farm out clustering metrics eg WCSS
+         glanced = map(model, ~ glance(.x))) %>% 
+  unnest(cols = glanced)
+  
+
+# View clustering rsulsts
+kclusts
+```
+
+Now that we have the total within-cluster sum-of-squares (tot.withinss) for each clustering algorithm with center *k*, we use the [elbow method](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) to find the optimal number of clusters. The method consists of plotting the WCSS as a function of the number of clusters, and picking the [elbow of the curve](https://en.wikipedia.org/wiki/Elbow_of_the_curve "Elbow of the curve") as the number of clusters to use.
+
+```{r elbow_method}
+set.seed(2056)
+# Use elbow method to determine optimum number of clusters
+kclusts %>% 
+  ggplot(mapping = aes(x = k, y = tot.withinss)) +
+  geom_line(size = 1.2, alpha = 0.8, color = "#FF7F0EFF") +
+  geom_point(size = 2, color = "#FF7F0EFF")
+```
+
+The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an `elbow` 💪in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points.
+
+We can now go ahead and extract the clustering model where `k = 3`:
+
+> `pull()`: used to extract a single column
+>
+> `pluck()`: used to index data structures such as lists
+
+```{r extract_model}
+# Extract k = 3 clustering
+final_kmeans <- kclusts %>% 
+  filter(k == 3) %>% 
+  pull(model) %>% 
+  pluck(1)
+
+
+final_kmeans
+```
+
+Great! Let's go ahead and visualize the clusters obtained. Care for some interactivity using `plotly`?
+
+```{r viz_clust}
+# Add predicted cluster assignment to data set
+results <-  augment(final_kmeans, df_numeric_select) %>% 
+  bind_cols(df_numeric %>% select(artist_top_genre)) 
+
+# Plot cluster assignments
+clust_plt <- results %>% 
+  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +
+  geom_point(size = 2, alpha = 0.8) +
+  paletteer::scale_color_paletteer_d("ggthemes::Tableau_10")
+
+ggplotly(clust_plt)
+
+```
+
+Perhaps we would have expected that each cluster (represented by different colors) would have distinct genres (represented by different shapes).
+
+Let's take a look at the model's accuracy.
+
+```{r ordinal_encode}
+# Assign genres to predefined integers
+label_count <- results %>% 
+  group_by(artist_top_genre) %>% 
+  mutate(id = cur_group_id()) %>% 
+  ungroup() %>% 
+  summarise(correct_labels = sum(.cluster == id))
+
+
+# Print results  
+cat("Result:", label_count$correct_labels, "out of", nrow(results), "samples were correctly labeled.")
+
+cat("\nAccuracy score:", label_count$correct_labels/nrow(results))
+
+```
+
+This model's accuracy is not bad, but not great. It may be that the data may not lend itself well to K-Means Clustering. This data is too imbalanced, too little correlated and there is too much variance between the column values to cluster well. In fact, the clusters that form are probably heavily influenced or skewed by the three genre categories we defined above.
+
+Nevertheless, that was quite a learning process!
+
+In Scikit-learn's documentation, you can see that a model like this one, with clusters not very well demarcated, has a 'variance' problem:
+
+![Infographic from Scikit-learn](../../images/problems.png)
+
+## **Variance**
+
+Variance is defined as "the average of the squared differences from the Mean" [source](https://www.mathsisfun.com/data/standard-deviation.html). In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.
+
+✅ This is a great moment to think about all the ways you could correct this issue. Tweak the data a bit more? Use different columns? Use a different algorithm? Hint: Try [scaling your data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) to normalize it and test other columns.
+
+> Try this '[variance calculator](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' to understand the concept a bit more.
+
+------------------------------------------------------------------------
+
+## **🚀Challenge**
+
+Spend some time with this notebook, tweaking parameters. Can you improve the accuracy of the model by cleaning the data more (removing outliers, for example)? You can use weights to give more weight to given data samples. What else can you do to create better clusters?
+
+Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
+
+## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+
+## **Review & Self Study**
+
+-   Take a look at a K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+
+-   Also, take a look at [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
+
+Want to try out your newly acquired clustering skills to data sets that lend well to K-Means clustering? Please see:
+
+-   [Train and Evaluate Clustering Models](https://rpubs.com/eR_ic/clustering) using Tidymodels and friends
+
+-   [K-means Cluster Analysis](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide
+
+-   [K-means clustering with tidy data principles](https://www.tidymodels.org/learn/statistics/k-means/)
+
+## **Assignment**
+
+[Try different clustering methods](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)
+
+## THANK YOU TO:
+
+[Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️
+
+[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).
+
+Happy Learning,
+
+[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.
+
+![Artwork by \@allison_horst](../../images/r_learners_sm.jpeg)
+
+```{r include=FALSE}
+library(here)
+library(rmd2jupyter)
+rmd2jupyter("lesson_14.Rmd")
+```
diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md
new file mode 100644
index 000000000..31f60d774
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/README.it.md
@@ -0,0 +1,251 @@
+# Clustering K-Means
+
+[![Andrew Ng spiega Clustering](https://img.youtube.com/vi/hDmNF9JG3lo/0.jpg)](https://youtu.be/hDmNF9JG3lo " Andrew Ng spiega Clustering")
+
+> 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng spiega il clustering
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/?loc=it)
+
+In questa lezione si imparerà come creare cluster utilizzando Scikit-learn e l'insieme di dati di musica nigeriana importato in precedenza. Si tratteranno le basi di K-Means per Clustering. Si tenga presente che, come appreso nella lezione precedente, ci sono molti modi per lavorare con i cluster e il metodo usato dipende dai propri dati. Si proverà K-Means poiché è la tecnica di clustering più comune. Si inizia!
+
+Temini che si imparerà a conoscere:
+
+- Silhouette scoring (punteggio silhouette)
+- Elbow method (metodo del gomito)
+- Inerzia
+- Varianza
+
+## Introduzione
+
+[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) è un metodo derivato dal campo dell'elaborazione del segnale. Viene utilizzato per dividere e partizionare gruppi di dati in cluster "k" utilizzando una serie di osservazioni. Ogni osservazione lavora per raggruppare un dato punto dati più vicino alla sua "media" più vicina, o punto centrale di un cluster.
+
+I cluster possono essere visualizzati come [diagrammi di Voronoi](https://wikipedia.org/wiki/Voronoi_diagram), che includono un punto (o 'seme') e la sua regione corrispondente.
+
+![diagramma di voronoi](../images/voronoi.png)
+
+> Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+Il processo di clustering K-Means [viene eseguito in tre fasi](https://scikit-learn.org/stable/modules/clustering.html#k-means):
+
+1. L'algoritmo seleziona il numero k di punti centrali campionando dall'insieme di dati. Dopo questo, esegue un ciclo:
+   1. Assegna ogni campione al centroide più vicino.
+   2. Crea nuovi centroidi prendendo il valore medio di tutti i campioni assegnati ai centroidi precedenti.
+   3. Quindi, calcola la differenza tra il nuovo e il vecchio centroide e ripete finché i centroidi non sono stabilizzati.
+
+Uno svantaggio dell'utilizzo di K-Means include il fatto che sarà necessario stabilire 'k', ovvero il numero di centroidi. Fortunatamente il "metodo del gomito" aiuta a stimare un buon valore iniziale per "k". Si proverà in un minuto.
+
+## Prerequisito
+
+Si lavorerà nel file _notebook.ipynb_ di questa lezione  che include l'importazione dei dati e la pulizia preliminare fatta nell'ultima lezione.
+
+## Esercizio - preparazione
+
+Iniziare dando un'altra occhiata ai dati delle canzoni.
+
+1. Creare un diagramma a scatola e baffi (boxplot), chiamando `boxplot()` per ogni colonna:
+
+   ```python
+   plt.figure(figsize=(20,20), dpi=200)
+
+   plt.subplot(4,3,1)
+   sns.boxplot(x = 'popularity', data = df)
+
+   plt.subplot(4,3,2)
+   sns.boxplot(x = 'acousticness', data = df)
+
+   plt.subplot(4,3,3)
+   sns.boxplot(x = 'energy', data = df)
+
+   plt.subplot(4,3,4)
+   sns.boxplot(x = 'instrumentalness', data = df)
+
+   plt.subplot(4,3,5)
+   sns.boxplot(x = 'liveness', data = df)
+
+   plt.subplot(4,3,6)
+   sns.boxplot(x = 'loudness', data = df)
+
+   plt.subplot(4,3,7)
+   sns.boxplot(x = 'speechiness', data = df)
+
+   plt.subplot(4,3,8)
+   sns.boxplot(x = 'tempo', data = df)
+
+   plt.subplot(4,3,9)
+   sns.boxplot(x = 'time_signature', data = df)
+
+   plt.subplot(4,3,10)
+   sns.boxplot(x = 'danceability', data = df)
+
+   plt.subplot(4,3,11)
+   sns.boxplot(x = 'length', data = df)
+
+   plt.subplot(4,3,12)
+   sns.boxplot(x = 'release_date', data = df)
+   ```
+
+   Questi dati sono un po' rumorosi: osservando ogni colonna come un boxplot, si possono vedere i valori anomali.
+
+   ![situazioni anomale](../images/boxplots.png)
+
+Si potrebbe esaminare l'insieme di dati e rimuovere questi valori anomali, ma ciò renderebbe i dati piuttosto minimi.
+
+1. Per ora, si scelgono quali colonne utilizzare per questo esercizio di clustering. Scegliere quelle con intervalli simili e codifica la colonna `artist_top_genre` come dati numerici:
+
+   ```python
+   from sklearn.preprocessing import LabelEncoder
+   le = LabelEncoder()
+
+   X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]
+
+   y = df['artist_top_genre']
+
+   X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])
+
+   y = le.transform(y)
+   ```
+
+1. Ora si deve scegliere quanti cluster scegliere come obiettivo. E' noto che ci sono 3 generi di canzoni ricavati dall'insieme di dati, quindi si prova 3:
+
+   ```python
+   from sklearn.cluster import KMeans
+
+   nclusters = 3
+   seed = 0
+
+   km = KMeans(n_clusters=nclusters, random_state=seed)
+   km.fit(X)
+
+   # Predict the cluster for each data point
+
+   y_cluster_kmeans = km.predict(X)
+   y_cluster_kmeans
+   ```
+
+Viene visualizzato un array con i cluster previsti (0, 1 o 2) per ogni riga del dataframe di dati.
+
+1. Usare questo array per calcolare un "punteggio silhouette":
+
+   ```python
+   from sklearn import metrics
+   score = metrics.silhouette_score(X, y_cluster_kmeans)
+   score
+   ```
+
+## Punteggio Silhouette
+
+Si vuole ottenere un punteggio silhouette più vicino a 1. Questo punteggio varia da -1 a 1 e, se il punteggio è 1, il cluster è denso e ben separato dagli altri cluster. Un valore vicino a 0 rappresenta cluster sovrapposti con campioni molto vicini al limite di decisione dei clusters vicini [fonte](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).
+
+Il punteggio è **.53**, quindi proprio nel mezzo. Ciò indica che i dati non sono particolarmente adatti a questo tipo di clustering, ma si prosegue.
+
+### Esercizio: costruire il proprio modello
+
+1. Importare `KMeans` e avviare il processo di clustering.
+
+   ```python
+   from sklearn.cluster import KMeans
+   wcss = []
+
+   for i in range(1, 11):
+       kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
+       kmeans.fit(X)
+       wcss.append(kmeans.inertia_)
+
+   ```
+
+   Ci sono alcune parti qui che meritano una spiegazione.
+
+   > 🎓 range: queste sono le iterazioni del processo di clustering
+
+   > 🎓 random_state: "Determina la generazione di numeri casuali per l'inizializzazione del centroide."[fonte](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
+
+   > 🎓 WCSS: "somma dei quadrati all'interno del cluster" misura la distanza media al quadrato di tutti i punti all'interno di un cluster rispetto al cluster centroid [fonte](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce).
+
+   > 🎓 Inerzia: gli algoritmi K-Means tentano di scegliere i centroidi per ridurre al minimo l’’inerzia’, "una misura di quanto siano coerenti i cluster".[fonte](https://scikit-learn.org/stable/modules/clustering.html). Il valore viene aggiunto alla variabile wcss ad ogni iterazione.
+
+   > 🎓 k-means++: in [Scikit-learn](https://scikit-learn.org/stable/modules/clustering.html#k-means) puoi utilizzare l'ottimizzazione 'k-means++', che "inizializza i centroidi in modo che siano (generalmente) distanti l'uno dall'altro, portando probabilmente a risultati migliori rispetto all'inizializzazione casuale.
+
+### Metodo del gomito
+
+In precedenza, si era supposto che, poiché sono stati presi di mira 3 generi di canzoni, si dovrebbero scegliere 3 cluster. E' questo il caso?
+
+1. Usare il "metodo del gomito" per assicurarsene.
+
+   ```python
+   plt.figure(figsize=(10,5))
+   sns.lineplot(range(1, 11), wcss,marker='o',color='red')
+   plt.title('Elbow')
+   plt.xlabel('Number of clusters')
+   plt.ylabel('WCSS')
+   plt.show()
+   ```
+
+   Usare la variabile `wcss` creata nel passaggio precedente per creare un grafico che mostra dove si trova la "piegatura" nel gomito, che indica il numero ottimale di cluster. Forse **sono** 3!
+
+   ![Metodo del gomito](../images/elbow.png)
+
+## Esercizio - visualizzare i cluster
+
+1. Riprovare il processo, questa volta impostando tre cluster e visualizzare i cluster come grafico a dispersione:
+
+   ```python
+   from sklearn.cluster import KMeans
+   kmeans = KMeans(n_clusters = 3)
+   kmeans.fit(X)
+   labels = kmeans.predict(X)
+   plt.scatter(df['popularity'],df['danceability'],c = labels)
+   plt.xlabel('popularity')
+   plt.ylabel('danceability')
+   plt.show()
+   ```
+
+1. Verificare la precisione del modello:
+
+   ```python
+   labels = kmeans.labels_
+
+   correct_labels = sum(y == labels)
+
+   print("Result: %d out of %d samples were correctly labeled." % (correct_labels, y.size))
+
+   print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))
+   ```
+
+   La precisione di questo modello non è molto buona e la forma dei grappoli fornisce un indizio sul perché.
+
+   ![cluster](../images/clusters.png)
+
+   Questi dati sono troppo sbilanciati, troppo poco correlati e c'è troppa varianza tra i valori della colonna per raggruppare bene. In effetti, i cluster che si formano sono probabilmente fortemente influenzati o distorti dalle tre categorie di genere definite sopra. È stato un processo di apprendimento!
+
+   Nella documentazione di Scikit-learn, si può vedere che un modello come questo, con cluster non molto ben delimitati, ha un problema di "varianza":
+
+   ![modelli problematici](../images/problems.png)
+   > Infografica da Scikit-learn
+
+## Varianza
+
+La varianza è definita come "la media delle differenze al quadrato dalla media" [fonte](https://www.mathsisfun.com/data/standard-deviation.html). Nel contesto di questo problema di clustering, si fa riferimento ai dati che i numeri dell'insieme di dati tendono a divergere un po' troppo dalla media.
+
+✅ Questo è un ottimo momento per pensare a tutti i modi in cui si potrebbe correggere questo problema. Modificare un po' di più i dati? Utilizzare colonne diverse? Utilizzare un algoritmo diverso? Suggerimento: provare a [ridimensionare i dati](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) per normalizzarli e testare altre colonne.
+
+> Provare questo "[calcolatore della varianza](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)" per capire un po’ di più il concetto.
+
+---
+
+## 🚀 Sfida
+
+Trascorrere un po' di tempo con questo notebook, modificando i parametri. E possibile migliorare l'accuratezza del modello pulendo maggiormente i dati (rimuovendo gli outlier, ad esempio)? È possibile utilizzare i pesi per dare più peso a determinati campioni di dati. Cos'altro si può fare per creare cluster migliori?
+
+Suggerimento: provare a ridimensionare i dati. C'è un codice commentato nel notebook che aggiunge il ridimensionamento standard per rendere le colonne di dati più simili tra loro in termini di intervallo. Si scoprirà che mentre il punteggio della silhouette diminuisce, il "kink" nel grafico del gomito si attenua. Questo perché lasciare i dati non scalati consente ai dati con meno varianza di avere più peso. Leggere un po' di più su questo problema [qui](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Dare un'occhiata a un simulatore di K-Means [tipo questo](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). È possibile utilizzare questo strumento per visualizzare i punti dati di esempio e determinarne i centroidi. Questo aiuta a farsi un'idea di come i dati possono essere raggruppati?
+
+Inoltre, dare un'occhiata a [questa dispensa sui K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) di Stanford.
+
+## Compito
+
+[Provare diversi metodi di clustering](assignment.it.md)
diff --git a/5-Clustering/2-K-Means/translations/README.ko.md b/5-Clustering/2-K-Means/translations/README.ko.md
new file mode 100644
index 000000000..d9417d6a3
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/README.ko.md
@@ -0,0 +1,251 @@
+# K-Means clustering
+
+[![Andrew Ng explains Clustering](https://img.youtube.com/vi/hDmNF9JG3lo/0.jpg)](https://youtu.be/hDmNF9JG3lo "Andrew Ng explains Clustering")
+
+> 🎥 영상을 보려면 이미지 클릭: Andrew Ng explains clustering
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+
+이 강의에서, Scikit-learn과 함께 이전에 가져온 나이지리아 음악 데이터셋으로 클러스터 제작 방식을 배울 예정입니다. Clustering을 위한 K-Means 기초를 다루게 됩니다. 참고로, 이전 강의에서 배웠던대로, 클러스터로 작업하는 여러 방식이 있고 데이터를 기반한 방식도 있습니다. 가장 일반적 clustering 기술인 K-Means을 시도해보려고 합니다. 시작해봅니다!
+
+다음 용어를 배우게 됩니다:
+
+- Silhouette scoring
+- Elbow method
+- Inertia
+- Variance
+
+## 소개
+
+[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering)은 신호 처리 도메인에서 파생된 방식입니다. observations 계열로서 데이터 그룹을 'k' 클러스터로 나누고 분할하며 사용했습니다. 각자 observation은 가까운 'mean', 또는 클러스터의 중심 포인트에 주어진 정밀한 데이터 포인트를 그룹으로 묶기 위해서 작동합니다.
+
+클러스터는 포인트(또는 'seed')와 일치하는 영역을 포함한, [Voronoi diagrams](https://wikipedia.org/wiki/Voronoi_diagram)으로 시각화할 수 있습니다.
+
+![voronoi diagram](../images/voronoi.png)
+
+> infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+K-Means clustering은 [executes in a three-step process](https://scikit-learn.org/stable/modules/clustering.html#k-means)로 처리됩니다:
+
+1. 알고리즘은 데이터셋에서 샘플링한 중심 포인트의 k-number를 선택합니다. 반복합니다:
+    1. 가장 가까운 무게 중심에 각자 샘플을 할당합니다.
+    2. 이전의 무게 중심에서 할당된 모든 샘플의 평균 값을 가지면서 새로운 무게 중심을 만듭니다.
+    3. 그러면, 새롭고 오래된 무게 중심 사이의 거리를 계산하고 무계 중심이 안정될 때까지 반복합니다.
+
+K-Means을 사용한 한 가지 약점은 무게 중심의 숫자를, 'k'로 해야 된다는 사실입니다. 다행스럽게 'elbow method'는 'k' 값을 좋게 시작할 수 있게 추정하는 데 도움을 받을 수 있습니다. 몇 분동안 시도할 예정입니다.
+
+## 전제 조건
+
+마지막 강의에서 했던 데이터를 가져와서 미리 정리한 이 강의의 _notebook.ipynb_ 파일로 작업할 예정입니다.
+
+## 연습 - 준비하기
+
+노래 데이터를 다시 보는 것부터 시작합니다.
+
+1. 각 열에 `boxplot()`을 불러서, boxplot을 만듭니다:
+
+    ```python
+    plt.figure(figsize=(20,20), dpi=200)
+    
+    plt.subplot(4,3,1)
+    sns.boxplot(x = 'popularity', data = df)
+    
+    plt.subplot(4,3,2)
+    sns.boxplot(x = 'acousticness', data = df)
+    
+    plt.subplot(4,3,3)
+    sns.boxplot(x = 'energy', data = df)
+    
+    plt.subplot(4,3,4)
+    sns.boxplot(x = 'instrumentalness', data = df)
+    
+    plt.subplot(4,3,5)
+    sns.boxplot(x = 'liveness', data = df)
+    
+    plt.subplot(4,3,6)
+    sns.boxplot(x = 'loudness', data = df)
+    
+    plt.subplot(4,3,7)
+    sns.boxplot(x = 'speechiness', data = df)
+    
+    plt.subplot(4,3,8)
+    sns.boxplot(x = 'tempo', data = df)
+    
+    plt.subplot(4,3,9)
+    sns.boxplot(x = 'time_signature', data = df)
+    
+    plt.subplot(4,3,10)
+    sns.boxplot(x = 'danceability', data = df)
+    
+    plt.subplot(4,3,11)
+    sns.boxplot(x = 'length', data = df)
+    
+    plt.subplot(4,3,12)
+    sns.boxplot(x = 'release_date', data = df)
+    ```
+
+    이 데이터는 약간의 노이즈가 있습니다: 각 열을 boxplot으로 지켜보면 아웃라이어를 볼 수 있습니다.
+
+    ![outliers](../images/boxplots.png)
+
+데이터셋을 찾고 이 아웃라이어를 제거하는 대신에, 데이터는 꽤 작아지게 됩니다.
+
+1. 지금부터, clustering 연습에서 사용할 열을 선택합니다. 유사한 범위로 하나 선택하고 `artist_top_genre` 열을 숫자 데이터로 인코딩합니다:
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    le = LabelEncoder()
+    
+    X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]
+    
+    y = df['artist_top_genre']
+    
+    X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])
+    
+    y = le.transform(y)
+    ```
+
+1. 이제 얼마나 많은 클러스터를 타겟으로 잡을지 선택해야 합니다. 데이터셋에서 조각낸 3개 장르가 있으므로, 3개를 시도합니다:
+
+    ```python
+    from sklearn.cluster import KMeans
+    
+    nclusters = 3 
+    seed = 0
+    
+    km = KMeans(n_clusters=nclusters, random_state=seed)
+    km.fit(X)
+    
+    # Predict the cluster for each data point
+    
+    y_cluster_kmeans = km.predict(X)
+    y_cluster_kmeans
+    ```
+
+데이터 프레임의 각 열에서 예측된 클러스터 (0, 1,또는 2)로 배열을 출력해서 볼 수 있습니다.
+
+1. 배열로 'silhouette score'를 계산합니다:
+
+    ```python
+    from sklearn import metrics
+    score = metrics.silhouette_score(X, y_cluster_kmeans)
+    score
+    ```
+
+## Silhouette score
+
+1에 근접한 silhouette score를 찾아봅니다. 이 점수는 -1에서 1까지 다양하며, 클러스터가 밀접하여 다른 것과 잘-분리됩니다. 0 근접 값은 주변 클러스터의 decision boundary에 매우 가까운 샘플과 함께 클러스터를 오버랩헤서 니타냅니다. [source](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).
+
+**.53** 점이므로, 중간에 위치합니다. 데이터가 이 clustering 타입에 특히 잘-맞지 않다는 점을 나타내고 있지만, 계속 진행합니다.
+
+### 연습 - 모델 만들기
+
+1. `KMeans`을 import 하고 clustering 처리를 시작합니다.
+
+    ```python
+    from sklearn.cluster import KMeans
+    wcss = []
+    
+    for i in range(1, 11):
+        kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
+        kmeans.fit(X)
+        wcss.append(kmeans.inertia_)
+    
+    ```
+
+    여기 설명을 뒷받침할 몇 파트가 있습니다.
+
+    > 🎓 range: clustering 프로세스의 반복입니다
+
+    > 🎓 random_state: "Determines random number generation for centroid initialization."[source](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
+
+    > 🎓 WCSS:  "within-cluster sums of squares"은 클러스터 무게 중심으로 클러스터에서 모든 포인트의 squared average 거리를 측정합니다. [source](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce).
+   
+    > 🎓 Inertia: K-Means 알고리즘은 'inertia'를 최소로 유지하기 위해서 무게 중심을 선택하려고 시도합니다, "a measure of how internally coherent clusters are."[source](https://scikit-learn.org/stable/modules/clustering.html). 값은 각 반복에서 wcss 변수로 추가됩니다.
+
+    > 🎓 k-means++: [Scikit-learn](https://scikit-learn.org/stable/modules/clustering.html#k-means)에서 'k-means++' 최적화를 사용할 수 있고, 무게 중심을 (일반적인) 거리로 각자 떨어져서 초기화하면, 아마 랜덤 초기화보다 더 좋은 결과로 이어질 수 있습니다.
+
+### Elbow method
+
+예전에 추측했던 것을 기반으로, 3개 노래 장르를 타겟팅 했으므로, 3게 클러스터를 선택해야 되었습니다. 그러나 그랬어야만 하나요?
+
+1. 'elbow method'을 사용해서 확인합니다.
+
+    ```python
+    plt.figure(figsize=(10,5))
+    sns.lineplot(range(1, 11), wcss,marker='o',color='red')
+    plt.title('Elbow')
+    plt.xlabel('Number of clusters')
+    plt.ylabel('WCSS')
+    plt.show()
+    ```
+
+    이전 단계에서 만들었던 `wcss` 변수로, 최적 클러스터 수를 나타낼 elbow의 'bend'가 어디있는지 보여주는 차트를 만듭니다. 아마도 3 **입니다**!
+
+    ![elbow method](../images/elbow.png)
+
+## 연습 - 클러스터 보이기
+
+1. 프로세스를 다시 시도하여, 이 시점에 3개 클러스터를 다시 설정하고, scatterplot으로 클러스터를 보여줍니다:
+
+    ```python
+    from sklearn.cluster import KMeans
+    kmeans = KMeans(n_clusters = 3)
+    kmeans.fit(X)
+    labels = kmeans.predict(X)
+    plt.scatter(df['popularity'],df['danceability'],c = labels)
+    plt.xlabel('popularity')
+    plt.ylabel('danceability')
+    plt.show()
+    ```
+
+1. 모델 정확도를 확인합니다:
+
+    ```python
+    labels = kmeans.labels_
+    
+    correct_labels = sum(y == labels)
+    
+    print("Result: %d out of %d samples were correctly labeled." % (correct_labels, y.size))
+    
+    print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))
+    ```
+
+    이 모델의 정확도는 매우 좋지 않으며, 클러스터의 형태가 왜 그랬는지 힌트를 줍니다.
+
+    ![clusters](../images/clusters.png)
+
+    이 데이터는 매우 불안정하며, 상관 관계가 낮고 열 값 사이에 편차가 커서 잘 클러스터될 수 없습니다. 사실, 만들어진 클러스터는 정의한 3개 장르 카테고리에 크게 영향받거나 뒤틀릴 수 있습니다. 학습 프로세스입니다!
+
+    Scikit-learn 문서에, 클러스터가 매우 명확하지 않은 모델, 'variance' 문제가 있습니다:
+
+    ![problem models](../images/problems.png)
+    > Infographic from Scikit-learn
+
+## Variance
+
+Variance는 "the average of the squared differences from the Mean."으로 정의되었습니다. [source](https://www.mathsisfun.com/data/standard-deviation.html) 이 clustering 문제의 컨텍스트에서, 데이터셋 숫자가 평균에서 너무 크게 이탈되어 데이터로 나타냅니다.
+
+✅ 이 이슈를 해결할 모든 방식을 생각해보는 훌륭한 순간입니다. 데이터를 조금 트윅해볼까요? 다른 열을 사용해볼까요? 다른 알고리즘을 사용해볼까요? 힌트: [scaling your data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/)로 노멀라이즈하고 다른 컬럼을 테스트헤봅니다.
+
+> '[variance calculator](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)'로 좀 더 개념을 이해해봅니다.
+
+---
+
+## 🚀 도전
+
+파라미터를 트윅하면서, 노트북으로 시간을 보냅니다. 데이터를 더 정리해서 (예시로, 아웃라이어 제거) 모델의 정확도를 개선할 수 있나요? 가중치로 주어진 데이터 샘플에서 더 가중치를 줄 수 있습니다. 괜찮은 클러스터를 만들기 위헤 어떤 다른 일을 할 수 있나요?
+
+힌트: 데이터를 더 키워봅니다. 가까운 범위 조건에 비슷한 데이터 열을 만들고자 추가하는 표준 스케일링 코드를 노트북에 주석으로 남겼습니다. silhouette 점수가 낮아지는 동안, elbow 그래프의 'kink'가 주름 펴지는 것을 볼 수 있습니다. 데이터를 조정하지 않고 남기면 덜 분산된 데이터가 더 많은 가중치로 나를 수 있다는 이유입니다. [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226) 이 문제를 조금 더 읽어봅니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+
+## 검토 & 자기주도 학습
+
+[such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)같은 K-Means 시뮬레이터를 찾아봅니다. 이 도구로 샘플 데이터 포인트를 시각화하고 무게 중심을 결정할 수 있습니다. 데이터의 랜덤성, 클러스터 수와 무게 중심 수를 고칠 수 있습니다. 데이터를 그룹으로 묶기 위한 아이디어를 얻는 게 도움이 되나요?
+
+또한, Stanford 의 [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) 을 찾아봅니다.
+
+## 과제
+
+[Try different clustering methods](../assignment.md)
diff --git a/5-Clustering/2-K-Means/translations/README.zh-cn.md b/5-Clustering/2-K-Means/translations/README.zh-cn.md
new file mode 100644
index 000000000..7cab44969
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/README.zh-cn.md
@@ -0,0 +1,253 @@
+# K-Means 聚类
+
+[![Andrew Ng explains Clustering](https://img.youtube.com/vi/hDmNF9JG3lo/0.jpg)](https://youtu.be/hDmNF9JG3lo "Andrew Ng explains Clustering")
+
+> 🎥 单击上图观看视频：Andrew Ng 解释聚类
+
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+
+在本课中，您将学习如何使用 Scikit-learn 和您之前导入的尼日利亚音乐数据集创建聚类。我们将介绍 K-Means 聚类 的基础知识。请记住，正如您在上一课中学到的，使用聚类的方法有很多种，您使用的方法取决于您的数据。我们将尝试 K-Means，因为它是最常见的聚类技术。让我们开始吧！
+
+您将了解的术语：
+
+- 轮廓打分
+- 手肘方法
+- 惯性
+- 方差
+
+## 介绍
+
+[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) 是一种源自信号处理领域的方法。它用于使用一系列观察将数据组划分和划分为“k”个聚类。每个观察都用于对最接近其最近“平均值”或聚类中心点的给定数据点进行分组。
+
+聚类可以可视化为 [Voronoi 图](https://wikipedia.org/wiki/Voronoi_diagram)，其中包括一个点（或“种子”）及其相应的区域。
+
+![voronoi diagram](../images/voronoi.png)
+
+> [Jen Looper](https://twitter.com/jenlooper)作图
+
+K-Means 聚类过程[分三步执行](https://scikit-learn.org/stable/modules/clustering.html#k-means)：
+
+1. 该算法通过从数据集中采样来选择 k 个中心点。在此之后，它循环：
+   1. 它将每个样本分配到最近的质心。
+   2. 它通过取分配给先前质心的所有样本的平均值来创建新质心。
+   3. 然后，它计算新旧质心之间的差异并重复直到质心稳定。
+
+使用 K-Means 的一个缺点包括您需要建立“k”，即质心的数量。幸运的是，“肘部法则”有助于估计“k”的良好起始值。试一下吧。
+
+## 前置条件
+
+您将使用本课的 *notebook.ipynb* 文件，其中包含您在上一课中所做的数据导入和初步清理。
+
+## 练习 - 准备
+
+首先再看看歌曲数据。
+
+1. 创建一个箱线图，`boxplot()` 为每一列调用：
+
+    ```python
+    plt.figure(figsize=(20,20), dpi=200)
+    
+    plt.subplot(4,3,1)
+    sns.boxplot(x = 'popularity', data = df)
+    
+    plt.subplot(4,3,2)
+    sns.boxplot(x = 'acousticness', data = df)
+    
+    plt.subplot(4,3,3)
+    sns.boxplot(x = 'energy', data = df)
+    
+    plt.subplot(4,3,4)
+    sns.boxplot(x = 'instrumentalness', data = df)
+    
+    plt.subplot(4,3,5)
+    sns.boxplot(x = 'liveness', data = df)
+    
+    plt.subplot(4,3,6)
+    sns.boxplot(x = 'loudness', data = df)
+    
+    plt.subplot(4,3,7)
+    sns.boxplot(x = 'speechiness', data = df)
+    
+    plt.subplot(4,3,8)
+    sns.boxplot(x = 'tempo', data = df)
+    
+    plt.subplot(4,3,9)
+    sns.boxplot(x = 'time_signature', data = df)
+    
+    plt.subplot(4,3,10)
+    sns.boxplot(x = 'danceability', data = df)
+    
+    plt.subplot(4,3,11)
+    sns.boxplot(x = 'length', data = df)
+    
+    plt.subplot(4,3,12)
+    sns.boxplot(x = 'release_date', data = df)
+    ```
+
+    这个数据有点嘈杂：通过观察每一列作为箱线图，你可以看到异常值。
+
+    ![outliers](../images/boxplots.png)
+
+您可以浏览数据集并删除这些异常值，但这会使数据非常少。
+
+1. 现在，选择您将用于聚类练习的列。选择具有相似范围的那些并将 `artist_top_genre` 列编码为数字类型的数据：
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    le = LabelEncoder()
+    
+    X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]
+    
+    y = df['artist_top_genre']
+    
+    X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])
+    
+    y = le.transform(y)
+    ```
+
+1. 现在您需要选择要定位的聚类数量。您知道我们从数据集中挖掘出 3 种歌曲流派，所以让我们尝试 3 种：
+
+    ```python
+    from sklearn.cluster import KMeans
+    
+    nclusters = 3 
+    seed = 0
+    
+    km = KMeans(n_clusters=nclusters, random_state=seed)
+    km.fit(X)
+    
+    # Predict the cluster for each data point
+    
+    y_cluster_kmeans = km.predict(X)
+    y_cluster_kmeans
+    ```
+
+您会看到打印出的数组，其中包含数据帧每一行的预测聚类（0、1 或 2）。
+
+1. 使用此数组计算“轮廓分数”：
+
+    ```python
+    from sklearn import metrics
+    score = metrics.silhouette_score(X, y_cluster_kmeans)
+    score
+    ```
+
+## 轮廓分数
+
+寻找接近 1 的轮廓分数。该分数从 -1 到 1 不等，如果分数为 1，则该聚类密集且与其他聚类分离良好。接近 0 的值表示重叠聚类，样本非常接近相邻聚类的决策边界。[来源](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam)。
+
+我们的分数是 **0.53**，所以正好在中间。这表明我们的数据不是特别适合这种类型的聚类，但让我们继续。
+
+### 练习 - 建立模型
+
+1. 导入 `KMeans` 并启动聚类过程。
+
+    ```python
+    from sklearn.cluster import KMeans
+    wcss = []
+    
+    for i in range(1, 11):
+        kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
+        kmeans.fit(X)
+        wcss.append(kmeans.inertia_)
+    
+    ```
+
+    这里有几个部分需要解释。
+
+    > 🎓 range：这些是聚类过程的迭代
+
+    > 🎓 random_state：“确定质心初始化的随机数生成。” [来源](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
+
+    > 🎓 WCSS：“聚类内平方和”测量聚类内所有点到聚类质心的平方平均距离。[来源](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce)。
+
+    > 🎓 Inertia：K-Means 算法尝试选择质心以最小化“惯性”，“惯性是衡量内部相干程度的一种方法”。[来源](https://scikit-learn.org/stable/modules/clustering.html)。该值在每次迭代时附加到 wcss 变量。
+
+    > 🎓 k-means++：在 [Scikit-learn 中，](https://scikit-learn.org/stable/modules/clustering.html#k-means)您可以使用“k-means++”优化，它“将质心初始化为（通常）彼此远离，导致可能比随机初始化更好的结果。
+
+### 手肘方法
+
+之前，您推测，因为您已经定位了 3 个歌曲 genre，所以您应该选择 3 个聚类。但真的是这样吗？
+
+1. 使用手肘方法来确认。
+
+    ```python
+    plt.figure(figsize=(10,5))
+    sns.lineplot(range(1, 11), wcss,marker='o',color='red')
+    plt.title('Elbow')
+    plt.xlabel('Number of clusters')
+    plt.ylabel('WCSS')
+    plt.show()
+    ```
+
+    使用 `wcss` 您在上一步中构建的变量创建一个图表，显示肘部“弯曲”的位置，这表示最佳聚类数。也许**是** 3！
+
+    ![elbow method](../images/elbow.png)
+
+## 练习 - 显示聚类
+
+1. 再次尝试该过程，这次设置三个聚类，并将聚类显示为散点图：
+
+    ```python
+    from sklearn.cluster import KMeans
+    kmeans = KMeans(n_clusters = 3)
+    kmeans.fit(X)
+    labels = kmeans.predict(X)
+    plt.scatter(df['popularity'],df['danceability'],c = labels)
+    plt.xlabel('popularity')
+    plt.ylabel('danceability')
+    plt.show()
+    ```
+
+1. 检查模型的准确性：
+
+    ```python
+    labels = kmeans.labels_
+    
+    correct_labels = sum(y == labels)
+    
+    print("Result: %d out of %d samples were correctly labeled." % (correct_labels, y.size))
+    
+    print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))
+    ```
+
+    这个模型的准确性不是很好，聚类的形状给了你一个提示。
+
+    ![clusters](../images/clusters.png)
+
+    这些数据太不平衡，相关性太低，列值之间的差异太大，无法很好地聚类。事实上，形成的聚类可能受到我们上面定义的三个类型类别的严重影响或扭曲。那是一个学习的过程！
+
+    在 Scikit-learn 的文档中，你可以看到像这样的模型，聚类划分不是很好，有一个“方差”问题：
+
+    ![problem models](../images/problems.png)
+    
+    > 图来自 Scikit-learn
+
+## 方差
+
+> 方差被定义为“来自均值的平方差的平均值”[源](https://www.mathsisfun.com/data/standard-deviation.html)。在这个聚类问题的上下文中，它指的是我们数据集的数量往往与平均值相差太多的数据。
+>
+> ✅这是考虑可以纠正此问题的所有方法的好时机。稍微调整一下数据？使用不同的列？使用不同的算法？提示：尝试[缩放数据](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/)以对其进行标准化并测试其他列。
+>
+> > 试试这个“[方差计算器](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)”来更多地理解这个概念。
+
+---
+
+## 🚀挑战
+
+花一些时间在这个笔记本上，调整参数。您能否通过更多地清理数据（例如，去除异常值）来提高模型的准确性？您可以使用权重为给定的数据样本赋予更多权重。你还能做些什么来创建更好的聚类？
+
+提示：尝试缩放您的数据。笔记本中的注释代码添加了标准缩放，使数据列在范围方面更加相似。您会发现，当轮廓分数下降时，肘部图中的“扭结”变得平滑。这是因为不缩放数据可以让方差较小的数据承载更多的权重。在[这里](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)阅读更多关于这个问题的[信息](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)。
+
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+
+## 复习与自学
+
+看看[像这样](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)的 K-Means 模拟器。您可以使用此工具来可视化样本数据点并确定其质心。您可以编辑数据的随机性、聚类数和质心数。这是否有助于您了解如何对数据进行分组？
+
+另外，看看斯坦福大学的 [K-Means 讲义](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html)。
+
+## 作业
+
+[尝试不同的聚类方法](./assignment.zh-cn.md)
+
diff --git a/5-Clustering/2-K-Means/translations/assignment.it.md b/5-Clustering/2-K-Means/translations/assignment.it.md
new file mode 100644
index 000000000..59fc79de1
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/assignment.it.md
@@ -0,0 +1,10 @@
+# Provare diversi metodi di clustering
+
+## Istruzioni
+
+In questa lezione si è imparato a conoscere il clustering K-Means. A volte K-Means non è appropriato per i propri dati. Creare un notebook usando i dati da queste lezioni o da qualche altra parte (accreditare la fonte) e mostrare un metodo di clustering diverso NON usando K-Means. Che cosa si è imparato?
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------------------- |
+|          | Viene presentato un notebook con un modello di clustering ben documentato | Un notebook è presentato senza una buona documentazione e/o incompleto | E' stato inviato un lavoro incompleto |
diff --git a/5-Clustering/2-K-Means/translations/assignment.zh-cn.md b/5-Clustering/2-K-Means/translations/assignment.zh-cn.md
new file mode 100644
index 000000000..c21058d3c
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/assignment.zh-cn.md
@@ -0,0 +1,12 @@
+# 尝试不同的聚类方法
+
+
+## 说明
+
+在本课中，您学习了 K-Means 聚类。有时 K-Means 不适合您的数据。使用来自这些课程或其他地方的数据（归功于您的来源）创建notebook，并展示不使用 K-Means 的不同聚类方法。你学到了什么？
+## 评判规则
+
+| 评判标准 | 优秀                                                       | 中规中矩                                                             | 仍需努力            |
+| -------- | --------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------------------- |
+|          | 一个具有良好文档记录的聚类模型的notebook | 一个没有详细文档或不完整的notebook| 提交了一个不完整的工作 |
+
diff --git a/5-Clustering/README.md b/5-Clustering/README.md
index b5010bba9..e93e7d7eb 100644
--- a/5-Clustering/README.md
+++ b/5-Clustering/README.md
@@ -8,19 +8,21 @@ Nigeria's diverse audience has diverse musical tastes. Using data scraped from S
 
 ![A turntable](./images/turntable.jpg)
 
-Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+> Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 In this series of lessons, you will discover new ways to analyze data using clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then classification techniques such as those you learned in previous lessons might be more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.
 
 > There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
 ## Lessons
 
 1. [Introduction to clustering](1-Visualize/README.md)
 2. [K-Means clustering](2-K-Means/README.md)
+
 ## Credits
 
 These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
 
 The [Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) dataset was sourced from Kaggle as scraped from Spotify.
 
-Useful K-Means examples that aided in creating this lesson include this [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), this [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), and this [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
\ No newline at end of file
+Useful K-Means examples that aided in creating this lesson include this [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), this [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), and this [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
diff --git a/5-Clustering/translations/README.it.md b/5-Clustering/translations/README.it.md
new file mode 100644
index 000000000..9aa64ceb6
--- /dev/null
+++ b/5-Clustering/translations/README.it.md
@@ -0,0 +1,29 @@
+# Modelli di clustering per machine learning
+
+Il clustering è un'attività di machine learning che cerca di trovare oggetti che si assomigliano per raggrupparli in gruppi chiamati cluster. Ciò che differenzia il clustering da altri approcci in machine learning è che le cose accadono automaticamente, infatti, è giusto dire che è l'opposto dell'apprendimento supervisionato.
+
+## Tema regionale: modelli di clustering per il gusto musicale di un pubblico nigeriano 🎧
+
+Il pubblico eterogeneo della Nigeria ha gusti musicali diversi. Usando i dati recuperati da Spotify (ispirato da [questo articolo](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421), si dà un'occhiata a un po' di musica popolare in Nigeria. Questo insieme di dati include dati sul punteggio di "danzabilità", acustica, volume, "speechness" (un numero compreso tra zero e uno che indica la probabilità che un particolare file audio sia parlato - n.d.t.) popolarità ed energia di varie canzoni. Sarà interessante scoprire modelli in questi dati!
+
+![Un giradischi](../images/turntable.jpg)
+
+> Foto di <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> su <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+In questa serie di lezioni si scopriranno nuovi modi per analizzare i dati utilizzando tecniche di clustering. Il clustering è particolarmente utile quando l'insieme di dati non ha etichette. Se ha etichette, le tecniche di classificazione come quelle apprese nelle lezioni precedenti potrebbero essere più utili. Ma nei casi in cui si sta cercando di raggruppare dati senza etichetta, il clustering è un ottimo modo per scoprire i modelli.
+
+> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di clustering. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## Lezioni
+
+
+1. [Introduzione al clustering](../1-Visualize/translations/README.it.md)
+2. [K-Means clustering](../2-K-Means/translations/README.it.md)
+
+## Crediti
+
+Queste lezioni sono state scritte con 🎶 da [Jen Looper](https://www.twitter.com/jenlooper) con utili recensioni di [Rishit Dagli](https://rishit_dagli) e [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
+
+L'insieme di dati [Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) è stato prelevato da Kaggle, a sua volta recuperato da Spotify.
+
+Esempi utili di K-Means che hanno aiutato nella creazione di questa lezione includono questa [esplorazione dell'iride](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), questo [notebook introduttivo](https://www.kaggle.com/prashant111/k-means-clustering-with-python) e questo [ipotetico esempio di ONG](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
diff --git a/5-Clustering/translations/README.ko.md b/5-Clustering/translations/README.ko.md
new file mode 100644
index 000000000..e06a82770
--- /dev/null
+++ b/5-Clustering/translations/README.ko.md
@@ -0,0 +1,28 @@
+# 머신러닝을 위한 Clustering 모델
+
+Clustering 은 서로 비슷한 오브젝트를 찾고 clusters 라고 불린 그룹으로 묶는 머신러닝 작업입니다. Clustering 이 머신러닝의 다른 접근법과 다른 점은, 자동으로 어떤 일이 생긴다는 것이며, 사실은, supervised learning 의 반대라고 말하는 게 맞습니다.
+
+## 지역 토픽: 나이지리아 사람들의 음악 취향을 위한 clustering 모델 🎧
+
+나이지리아의 다양한 사람들은 다양한 음악 취향이 있습니다. Spotify 에서 긁어온 데이터를 사용해서 ([this article](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421) 에서 영감받았습니다), 나이지니아에서 인기있는 음악을 알아보겠습니다. 데이터셋에 다양한 노래의 'danceability' 점수, 'acousticness', loudness, 'speechiness', 인기도와 에너지 데이터가 포함됩니다. 데이터에서 패턴을 찾는 것은 흥미로울 예정입니다!
+
+![A turntable](../images/turntable.jpg)
+
+> Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+이 강의의 시리즈에서, clustering 기술로 데이터를 분석하는 새로운 방식을 찾아볼 예정입니다. Clustering 은 데이터셋에 라벨이 없으면 더욱 더 유용합니다. 만약 라벨이 있다면, 이전 강의에서 배운대로 classification 기술이 더 유용할 수 있습니다. 그러나 라벨링되지 않은 데이터를 그룹으로 묶으려면, clustering 은 패턴을 발견하기 위한 좋은 방식입니다.
+
+> clustering 모델 작업을 배울 때 도움을 받을 수 있는 유용한 low-code 도구가 있습니다. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)를 시도해봅니다.
+
+## 강의
+
+1. [clustering 소개하기](../1-Visualize/translations/README.ko.md)
+2. [K-Means clustering](../2-K-Means/translations/README.ko.md)
+
+## 크레딧
+
+These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
+
+[Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) 데이터셋은 Spotify 스크랩해서 Kaggle 에서 가져왔습니다.
+
+이 강의를 만들 때 도움된 유용한 K-Means 예시는 [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), 과 [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering)이 포함됩니다.
diff --git a/5-Clustering/translations/README.ru.md b/5-Clustering/translations/README.ru.md
index eb0241b47..11f3342a7 100644
--- a/5-Clustering/translations/README.ru.md
+++ b/5-Clustering/translations/README.ru.md
@@ -8,7 +8,7 @@
 
 ![Поворотный стол](./images/turntable.jpg)
 
-Фото <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Марсела Ласкоски </a> на <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
+> Фото <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Марсела Ласкоски </a> на <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
   
 В этой серии уроков вы откроете для себя новые способы анализа данных с помощью методов кластеризации. Кластеризация особенно полезна, когда в наборе данных отсутствуют метки. Если на нем есть ярлыки, тогда могут быть более полезными методы классификации, подобные тем, которые вы изучили на предыдущих уроках. Но в случаях, когда вы хотите сгруппировать немаркированные данные, кластеризация - отличный способ обнаружить закономерности.
 
@@ -23,4 +23,4 @@
 
 Набор данных [Нигерийские песни](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) был получен из Kaggle, как и из Spotify.
 
-Полезные примеры K-Means, которые помогли в создании этого урока, включают [исследование радужной оболочки глаза](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), [вводный блокнот](https://www.kaggle.com/prashant111/k-means-clustering-with-python) и [пример гипотетической НПО](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
\ No newline at end of file
+Полезные примеры K-Means, которые помогли в создании этого урока, включают [исследование радужной оболочки глаза](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), [вводный блокнот](https://www.kaggle.com/prashant111/k-means-clustering-with-python) и [пример гипотетической НПО](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
diff --git a/5-Clustering/translations/README.zh-cn.md b/5-Clustering/translations/README.zh-cn.md
new file mode 100644
index 000000000..cfe1d6f0e
--- /dev/null
+++ b/5-Clustering/translations/README.zh-cn.md
@@ -0,0 +1,29 @@
+# 机器学习中的聚类模型
+
+聚类（clustering）是一项机器学习任务，用于寻找类似对象并将他们分成不同的组（这些组称做“聚类”（cluster））。聚类与其它机器学习方法的不同之处在于聚类是自动进行的。事实上，我们可以说它是监督学习的对立面。
+
+## 本节主题: 尼日利亚观众音乐品味的聚类模型🎧
+
+尼日利亚多样化的观众有着多样化的音乐品味。使用从 Spotify 上抓取的数据（受到[本文](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)的启发），让我们看看尼日利亚流行的一些音乐。这个数据集包括关于各种歌曲的舞蹈性、声学、响度、言语、流行度和活力的分数。从这些数据中发现一些模式（pattern）会是很有趣的事情!
+
+![A turntable](../images/turntable.jpg)
+
+> <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> 在 <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> 上的照片
+
+在本系列课程中，您将发现使用聚类技术分析数据的新方法。当数据集缺少标签的时候，聚类特别有用。如果它有标签，那么分类技术(比如您在前面的课程中所学的那些)可能会更有用。但是如果要对未标记的数据进行分组，聚类是发现模式的好方法。
+
+> 这里有一些有用的低代码工具可以帮助您了解如何使用聚类模型。尝试 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## 课程安排
+
+1. [介绍聚类](../1-Visualize/translations/README.zh-cn.md)
+2. [K-Means 聚类](../2-K-Means/translations/README.zh-cn.md)
+
+## 致谢
+
+这些课程由 Jen Looper 在 🎶 上撰写，并由 [Rishit Dagli](https://rishit_dagli) 和 [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 进行了有帮助的评审。
+
+[尼日利亚歌曲数据集](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) 来自 Kaggle 抓取的 Spotify 数据。
+
+一些帮助创造了这节课程的 K-Means 例子包括:[虹膜探索(iris exploration)](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering)，[介绍性的笔记(introductory notebook)](https://www.kaggle.com/prashant111/k-means-clustering-with-python)，和 [假设非政府组织的例子(hypothetical NGO example)](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering)。
+
diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 0d47a1d70..ef7444cb5 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -2,7 +2,7 @@
 
 This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
 
 ## Introduction
 
@@ -17,7 +17,7 @@ You will learn about:
 
 ## Computational linguistics
 
-Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages.
+Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. Natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages.
 
 ### Example - phone dictation
 
@@ -69,7 +69,7 @@ The idea for this came from a party game called *The Imitation Game* where an in
 
 ### Developing Eliza
 
-In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https:/wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
+In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https://wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
 
 This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response.
 
@@ -81,7 +81,7 @@ This gave the impression that Eliza understood the statement and was asking a fo
 
 ## Exercise - coding a basic conversational bot
 
-A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, out bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
+A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, our bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
 
 ### The plan
 
@@ -149,7 +149,7 @@ Choose one of the "stop and consider" elements above and either try to implement
 
 In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.it.md b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
new file mode 100644
index 000000000..1c96d6649
--- /dev/null
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
@@ -0,0 +1,165 @@
+# Introduzione all'elaborazione del linguaggio naturale
+
+Questa lezione copre una breve storia e concetti importanti dell' *elaborazione del linguaggio naturale*, un sottocampo della *linguistica computazionale*.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/?loc=it)
+
+## Introduzione
+
+NLP, come è comunemente conosciuto, è una delle aree più note in cui machine learning è stato applicato e utilizzato nei software di produzione.
+
+✅ Si riesce a pensare a un software che si usa tutti i giorni che probabilmente ha NLP incorporato? Che dire dei programmi di elaborazione testi o le app mobili che si usano regolarmente?
+
+Si imparerà a conoscere:
+
+- **L'idea delle lingue**. Come si sono sviluppate le lingue e quali sono state le principali aree di studio.
+- **Definizione e concetti**. Si impareranno anche definizioni e concetti su come i computer elaborano il testo, inclusa l'analisi, la grammatica e l'identificazione di nomi e verbi. Ci sono alcune attività di codifica in questa lezione e vengono introdotti diversi concetti importanti che si imparerà a codificare più avanti nelle lezioni successive.
+
+## Linguistica computazionale
+
+La linguistica computazionale è un'area di ricerca e sviluppo che da molti decenni studia come i computer possono lavorare e persino capire, tradurre e comunicare con le lingue. L'elaborazione del linguaggio naturale (NLP) è un campo correlato incentrato su come i computer possono elaborare le lingue "naturali" o umane.
+
+### Esempio: dettatura telefonica
+
+Se si è mai dettato al telefono invece di digitare o posto una domanda a un assistente virtuale, il proprio discorso è stato convertito in formato testuale e quindi elaborato o *analizzato* dalla lingua con la quale si è parlato. Le parole chiave rilevate sono state quindi elaborate in un formato che il telefono o l'assistente possono comprendere e utilizzare.
+
+![comprensione](../images/comprehension.png)
+> La vera comprensione linguistica è difficile! Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+### Come è resa possibile questa tecnologia?
+
+Questo è possibile perché qualcuno ha scritto un programma per computer per farlo. Alcuni decenni fa, alcuni scrittori di fantascienza prevedevano che le persone avrebbero parlato principalmente con i loro computer e che i computer avrebbero sempre capito esattamente cosa intendevano. Purtroppo, si è rivelato essere un problema più difficile di quanto molti immaginavano, e sebbene sia un problema molto meglio compreso oggi, ci sono sfide significative nel raggiungere un'elaborazione del linguaggio naturale "perfetta" quando si tratta di comprendere il significato di una frase. Questo è un problema particolarmente difficile quando si tratta di comprendere l'umore o rilevare emozioni come il sarcasmo in una frase.
+
+A questo punto, si potrebbero ricordare le lezioni scolastiche in cui l'insegnante ha coperto le parti della grammatica in una frase. In alcuni paesi, agli studenti viene insegnata la grammatica e la linguistica come materie dedicate, ma in molti questi argomenti sono inclusi nell'apprendimento di una lingua: o la prima lingua nella scuola primaria (imparare a leggere e scrivere) e forse una seconda lingua in post-primario o liceo. Non occorre preoccuparsi se non si è esperti nel distinguere i nomi dai verbi o gli avverbi dagli aggettivi!
+
+Se si fa fatica a comprendere la differenza tra il *presente semplice* e il *presente progressivo*, non si è soli. Questa è una cosa impegnativa per molte persone, anche madrelingua di una lingua. La buona notizia è che i computer sono davvero bravi ad applicare regole formali e si imparerà a scrivere codice in grado di *analizzare* una frase così come un essere umano. La sfida più grande che si esaminerà in seguito è capire il *significato* e il *sentimento* di una frase.
+
+## Prerequisiti
+
+Per questa lezione, il prerequisito principale è essere in grado di leggere e comprendere la lingua di questa lezione. Non ci sono problemi di matematica o equazioni da risolvere. Sebbene l'autore originale abbia scritto questa lezione in inglese, è anche tradotta in altre lingue, quindi si potrebbe leggere una traduzione. Ci sono esempi in cui vengono utilizzati un numero di lingue diverse (per confrontare le diverse regole grammaticali di lingue diverse). Questi *non* sono tradotti, ma il testo esplicativo lo è, quindi il significato dovrebbe essere chiaro.
+
+Per le attività di codifica, si utilizzerà Python e gli esempi utilizzano Python 3.8.
+
+In questa sezione servirà e si utilizzerà:
+
+- **Comprensione del linguaggio Python 3**. Questa lezione utilizza input, loop, lettura di file, array.
+- **Visual Studio Code + estensione**. Si userà Visual Studio Code e la sua estensione Python. Si può anche usare un IDE Python a propria scelta.
+- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob) è una libreria di elaborazione del testo semplificata per Python. Seguire le istruzioni sul sito TextBlob per installarlo sul proprio sistema (installare anche i corpora, come mostrato di seguito):
+
+   ```bash
+   pip install -U textblob
+   python -m textblob.download_corpora
+   ```
+
+> 💡 Suggerimento: si può eseguire Python direttamente negli ambienti VS Code. Controllare la [documentazione](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) per ulteriori informazioni.
+
+## Parlare con le macchine
+
+La storia del tentativo di far capire ai computer il linguaggio umano risale a decenni fa e uno dei primi scienziati a considerare l'elaborazione del linguaggio naturale è stato *Alan Turing*.
+
+### Il Test di Turing.
+
+Quando Turing stava facendo ricerche sull'*intelligenza artificiale* negli anni '50, considerò se un test di conversazione potesse essere somministrato a un essere umano e a un computer (tramite corrispondenza digitata) in cui l'essere umano nella conversazione non era sicuro se stesse conversando con un altro umano o un computer.
+
+Se, dopo una certa durata di conversazione, l'essere umano non è riuscito a determinare se le risposte provenivano da un computer o meno, allora si potrebbe dire che il computer *sta pensando*?
+
+### L'ispirazione - 'il gioco dell'imitazione'
+
+L'idea è nata da un gioco di società chiamato *The Imitation Game* in cui un interrogatore è da solo in una stanza e ha il compito di determinare quale delle due persone (in un'altra stanza) sono rispettivamente maschio e femmina. L'interrogatore può inviare note e deve cercare di pensare a domande in cui le risposte scritte rivelano il sesso della persona misteriosa. Ovviamente, i giocatori nell'altra stanza stanno cercando di ingannare l'interrogatore rispondendo alle domande in modo tale da fuorviare o confondere l'interrogatore, dando anche l'impressione di rispondere onestamente.
+
+### Lo sviluppo di Eliza
+
+Negli anni '60 uno scienziato del MIT chiamato *Joseph* Weizenbaum sviluppò [*Eliza*](https:/wikipedia.org/wiki/ELIZA), un "terapista" informatico che poneva domande a un umano e dava l'impressione di comprendere le loro risposte. Tuttavia, mentre Eliza poteva analizzare una frase e identificare alcuni costrutti grammaticali e parole chiave in modo da dare una risposta ragionevole, non si poteva dire *che capisse* la frase. Se a Eliza viene presentata una frase che segue il formato "**Sono** _triste_", potrebbe riorganizzare e sostituire le parole nella frase per formare la risposta "Da quanto tempo **sei** _triste_".
+
+Questo dava l'impressione che Eliza avesse capito la frase e stesse facendo una domanda successiva, mentre in realtà stava cambiando il tempo e aggiungendo alcune parole. Se Eliza non fosse stata in grado di identificare una parola chiave per la quale aveva una risposta, avrebbe dato invece una risposta casuale che potrebbe essere applicabile a molte frasi diverse. Eliza avrebbe potuto essere facilmente ingannata, ad esempio se un utente avesse scritto "**Sei** una _bicicletta_" avrebbe potuto rispondere con "Da quanto tempo **sono** una _bicicletta_?", invece di una risposta più ragionata.
+
+[![Chiacchierare conEliza](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco " Chiaccherare con Eliza")
+
+> 🎥 Fare clic sull'immagine sopra per un video sul programma ELIZA originale
+
+> Nota: si può leggere la descrizione originale di [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) pubblicata nel 1966 se si dispone di un account ACM. In alternativa, leggere di Eliza su [wikipedia](https://it.wikipedia.org/wiki/ELIZA_(chatterbot))
+
+## Esercizio: codificare un bot conversazionale di base
+
+Un bot conversazionale, come Eliza, è un programma che sollecita l'input dell'utente e sembra capire e rispondere in modo intelligente. A differenza di Eliza, questo bot non avrà diverse regole che gli danno l'impressione di avere una conversazione intelligente. Invece, il bot avrà una sola capacità, per mantenere viva la conversazione con risposte casuali che potrebbero funzionare in quasi tutte le conversazioni banali.
+
+### Il piano
+
+I passaggi durante la creazione di un bot conversazionale:
+
+1. Stampare le istruzioni che consigliano all'utente come interagire con il bot
+2. Iniziare un ciclo
+   1. Accettare l'input dell'utente
+   2. Se l'utente ha chiesto di uscire, allora si esce
+   3. Elaborare l'input dell'utente e determinare la risposta (in questo caso, la risposta è una scelta casuale da un elenco di possibili risposte generiche)
+   4. Stampare la risposta
+3. Riprendere il ciclo dal passaggio 2
+
+### Costruire il bot
+
+Si crea il bot. Si inizia definendo alcune frasi.
+
+1. Creare questo bot in Python con le seguenti risposte casuali:
+
+   ```python
+   random_responses = ["That is quite interesting, please tell me more.",
+                       "I see. Do go on.",
+                       "Why do you say that?",
+                       "Funny weather we've been having, isn't it?",
+                       "Let's change the subject.",
+                       "Did you catch the game last night?"]
+   ```
+
+   Ecco un esempio di output come guida (l'input dell'utente è sulle righe che iniziano con `>`):
+
+   ```output
+   Hello, I am Marvin, the simple robot.
+   You can end this conversation at any time by typing 'bye'
+   After typing each answer, press 'enter'
+   How are you today?
+   > I am good thanks
+   That is quite interesting, please tell me more.
+   > today I went for a walk     
+   Did you catch the game last night?
+   > I did, but my team lost
+   Funny weather we've been having, isn't it?
+   > yes but I hope next week is better
+   Let's change the subject.
+   > ok, lets talk about music
+   Why do you say that?
+   > because I like music!
+   Why do you say that?
+   > bye
+   It was nice talking to you, goodbye!
+   ```
+
+   Una possibile soluzione al compito è [qui](../solution/bot.py)
+
+   ✅ Fermarsi e riflettere
+
+   1. Si ritiene che le risposte casuali "ingannerebbero" qualcuno facendogli pensare che il bot le abbia effettivamente capite?
+   2. Di quali caratteristiche avrebbe bisogno il bot per essere più efficace?
+   3. Se un bot potesse davvero "capire" il significato di una frase, avrebbe bisogno di "ricordare" anche il significato delle frasi precedenti in una conversazione?
+
+---
+
+## 🚀 Sfida
+
+Scegliere uno degli elementi "fermarsi e riflettere" qui sopra e provare a implementarli nel codice o scrivere una soluzione su carta usando pseudocodice.
+
+Nella prossima lezione si impareranno una serie di altri approcci all'analisi del linguaggio naturale e dell'machine learning.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Dare un'occhiata ai riferimenti di seguito come ulteriori opportunità di lettura.
+
+### Bibliografia
+
+1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Edizione primavera 2020), Edward N. Zalta (a cura di), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
+2. Università di Princeton "About WordNet". [WordNet](https://wordnet.princeton.edu/). Princeton University 2010.
+
+## Compito
+
+[Cercare un bot](assignment.it.md)
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
new file mode 100644
index 000000000..719775ea7
--- /dev/null
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
@@ -0,0 +1,165 @@
+# Natural language processing 소개하기
+
+이 강의애서 *computational linguistics* 하위인, *natural language processing*의 간단한 역사와 중요 컨셉을 다룹니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
+
+## 소개
+
+일반적으로 알고있는, NLP는, 머신러닝이 프로덕션 소프트웨어에 적용되어 사용하는 잘-알려진 영역 중 하나입니다.
+
+✅ 항상 사용하는 소프트웨어에서 NLP가 들어갈 수 있는지 생각할 수 있나요? 규칙적으로 사용하는 워드 프로그램이나 모바일 앱은 어떤가요?
+
+해당 내용을 배우게 됩니다:
+
+- **언어의 아이디어**. 언어가 어떻게 발전했고 어떤 주요 연구 영역인가요?
+- **정의와 컨셉**. 또한 파싱, 문법, 그리고 명사와 동사를 식별하는 것을 합쳐서, 컴퓨터가 텍스트를 처리하는 방식에 대한 정의와 개념을 배우게 됩니다. 이 강의에서 약간의 코딩 작업을 하며, 다음 강의 뒤에 배울 코드에서 중요한 개념을 소개합니다.
+
+## 전산 언어학
+
+전산 언어학은 컴퓨터가 언어와 합쳐서 이해, 번역, 그리고 커뮤니케이션 방식을 연구하는 수십 년을 넘어 연구 개발하고 있는 영역입니다. natural language processing (NLP)은 컴퓨터가 인간 언어를, 'natural'하게, 처리할 수 있는 것에 초점을 맞춘 관련 필드입니다.
+
+### 예시 - 전화번호 받아쓰기
+
+만약 핸드폰에 타이핑하거나 가상 어시스턴트에 질문을 했다면, 음성은 텍스트 형태로 변환되고 언급한 언어에서 처리되거나 *파싱*됩니다. 감지된 키워드는 핸드폰이나 어시스턴트가 이해하고 행동할 수 있는 포맷으로 처리됩니다.
+
+![comprehension](../images/comprehension.png)
+> Real linguistic comprehension is hard! Image by [Jen Looper](https://twitter.com/jenlooper)
+
+### 이 기술은 어떻게 만들어지나요?
+
+누군가 컴퓨터 프로그램을 작성했기 때문에 가능합니다. 수십 년 전에, 과학소설 작가는 사람들이 컴퓨터와 이야기하며, 컴퓨터가 그 의미를 항상 정확히 이해할 것이라고 예측했습니다. 슬프게, 많은 사람들이 상상했던 내용보다 더 어려운 문제로 밝혀졌고, 이제 더 잘 이해되는 문제이지만, 문장 의미를 이해함에 있어서 'perfect'한 natural language processing을 성공하기에는 상당히 어렵습니다. 유머를 이해하거나 문장에서 풍자처럼 감정을 알아차릴 때 특히 어렵습니다.
+
+이 포인트에서, 학교 수업에서 선생님이 문장의 문법 파트를 가르쳤던 기억을 회상할 수 있습니다. 일부 국가에서는, 학생에게 문법과 언어학을 전공 과목으로 가르치지만, 많은 곳에서, 이 주제를 언어 학습의 일부로 합칩니다: 초등학교에서 모국어(읽고 쓰는 방식 배우기)와 중학교나, 고등학교에서 제2 외국어를 배울 수 있습니다. 만약 명사와 형용사 또는 부사를 구분하는 전문가가 아니라고 해도 걱정하지 맙시다!
+
+만약 *simple present*와 *present progressive* 사이에서 몸부림치면, 혼자가 아닙니다. 모국어를 언어로 쓰는, 많은 사람들에게 도전입니다. 좋은 소식은 컴퓨터가 형식적인 규칙을 적용하는 것은 매우 좋고, 사람말고 문장을 *parse*할 수 있는 코드로 작성하는 방식을 배우게 됩니다. 나중에 할 큰 도전은 문장의 *meaning*과, *sentiment*를 이해하는 것입니다. 
+
+## 전제 조건
+
+이 강의에서, 주요 전제 조건은 이 강의의 언어를 읽고 이해해야 합니다. 풀 수 있는 수학 문제나 방정식이 아닙니다. 원작자가 영어로 이 강의를 작성했지만, 다른 언어로 번역되었으므로, 번역본으로 읽을 수 있게 되었습니다. 다른 언어로 사용된 (다른 언어의 다른 문법을 비교하는) 예시가 있습니다. 번역을 *하지 않았어도*, 설명 텍스트는, 의미가 명확해야 합니다.
+
+코딩 작업이면, Python으로 Python 3.8 버전을 사용할 예정입니다.
+
+이 섹션에서, 필요하고, 사용할 예정입니다:
+
+- **Python 3 이해**.  Python 3의 프로그래밍 언어 이해. 이 강의에서는 입력, 반복, 파일 입력, 배열을 사용합니다.
+- **Visual Studio Code + 확장**. Visual Studio Code와 Python 확장을 사용할 예정입니다. 선택에 따라 Python IDE를 사용할 수 있습니다.
+- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob)은 간단한 Python 텍스트 처리 라이브러리입니다. TextBlob 사이트 설명을 따라서 시스템에 설치합니다 (보이는 것처럼, corpora도 설치합니다):
+
+   ```bash
+   pip install -U textblob
+   python -m textblob.download_corpora
+   ```
+
+> 💡 팁: VS Code 환경에서 Python을 바로 실행할 수 있습니다. [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)으로 정보를 더 확인합니다.
+
+## 기계와 대화하기
+
+컴퓨터가 인간 언어를 이해하려 시도한 역사는 수십 년전으로, natural language processing을 고려한 초창기 사이언티스트 중 한 사람이 *Alan Turing*입니다.
+
+### 'Turing test'
+
+Turing이 1950년에 *artificial intelligence*를 연구하고 있을 때, 만약 대화하고 있는 사람이 다른 사람이나 컴퓨터와 대화하고 있는지 확신할 수 없다면, 사람과 컴퓨터의 대화를 (타이핑된 통신으로) 테스트할 수 있는지 고려했습니다.
+
+만약, 일정 대화 이후에, 사람이 컴퓨터에서 나온 대답인지 결정할 수 없다면, 컴퓨터가 *thinking*하고 있다고 말할 수 있나요?
+
+### 영감 - 'the imitation game'
+
+*The Imitation Game*으로 불리는 파티 게임에서 유래된 아이디어로 질문자가 방에 혼자있고 (다른 방의) 두 사람 중 남성과 여성을 결정할 일을 맡게 됩니다. 질문하는 사람은 노트를 보낼 수 있으며, 성별을 알 수 없는 사람이 작성해서 보낼 답변을 생각하고 질문해야 합니다. 당연하게, 다른 방에 있는 사람도 잘 못 이끌거나 혼동하는 방식으로 답변하며, 정직하게 대답해주는 모습을 보여 질문하는 사람을 속이려고 합니다.
+
+### Eliza 개발
+
+1960년에 *Joseph Weizenbaum*으로 불린 MIT 사이언티스트는, 사람의 질문을 답변하고 답변을 이해하는 모습을 주는 컴퓨터 'therapist' [*Eliza*](https://wikipedia.org/wiki/ELIZA)를 개발했습니다. 하지만, Eliza는 문장을 파싱하고 특정 문법 구조와 키워드를 식별하여 이유있는 답변을 준다고 할 수 있지만, 문장을 *understand*한다고 말할 수 없습니다. 만약 Eliza가 "**I am** <u>sad</u>" 포맷과 유사한 문장을 제시받으면 문장에서 단어를 재배열하고 대치해서 "How long have **you been** <u>sad</u>" 형태로 응답할 수 있습니다.
+
+Eliza가 문장을 이해하고 다음 질문을 대답하는 것처럼 인상을 줬지만, 실제로는, 시제를 바꾸고 일부 단어를 추가했을 뿐입니다. 만약 Eliza가 응답할 키워드를 식별하지 못하는 경우, 여러 다른 문장에 적용할 수 있는 랜덤 응답으로 대신합니다. 만약 사용자가 "**You are** a <u>bicycle</u>"라고 작성하면 더 이유있는 응답 대신에, "How long have **I been** a <u>bicycle</u>?"처럼 답변하므로, Eliza는 쉽게 속을 수 있습니다.
+
+[![Chatting with Eliza](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco "Chatting with Eliza")
+
+> 🎥 original ELIZA program에 대한 영상보려면 이미지 클릭
+
+> 노트: ACM 계정을 가지고 있다면 출판된 [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) 원본 설명을 읽을 수 있습니다. 대신, [wikipedia](https://wikipedia.org/wiki/ELIZA)에서 Eliza에 대한 내용을 읽을 수도 있습니다.
+
+## 연습 - 기초 대화 봇 코드 작성하기
+
+Eliza와 같은, 대화 봇은, 사용자 입력을 유도해서 지능적으로 이해하고 답변하는 프로그램입니다. Eliza와 다르게, 봇은 지능적 대화 형태를 띄는 여러 룰이 없습니다. 대신, 봇은 대부분 사소한 대화에서 작동할 랜덤으로 응답해서 대화를 지속하는, 하나의 능력만 가지고 있을 것입니다.
+
+### 계획
+
+대화 봇을 만들 몇 단계가 있습니다:
+
+1. 봇과 상호작용하는 방식을 사용자에 알려줄 명령 출력
+2. 반복 시작
+   1. 사용자 입력 승인
+   2. 만약 사용자 종료 요청하면, 종료
+   3. 사용자 입력 처리 및 응답 결정 (이 케이스는, 응답할 수 있는 일반적인 대답 목록에서 랜덤 선택)
+   4. 응답 출력
+3. 2 단계로 돌아가서 반복
+
+### 봇 만들기
+
+다음으로 봇을 만듭니다. 약간의 구문을 정의해서 시작해볼 예정입니다.
+
+1. 해당 랜덤 응답으로 Python에서 봇을 만듭니다:
+
+    ```python
+    random_responses = ["That is quite interesting, please tell me more.",
+                        "I see. Do go on.",
+                        "Why do you say that?",
+                        "Funny weather we've been having, isn't it?",
+                        "Let's change the subject.",
+                        "Did you catch the game last night?"]
+    ```
+
+    가이드하는 약간의 샘플 출력이 있습니다 (사용자 입력은 라인의 시작점에 `>` 있습니다):
+
+    ```output
+    Hello, I am Marvin, the simple robot.
+    You can end this conversation at any time by typing 'bye'
+    After typing each answer, press 'enter'
+    How are you today?
+    > I am good thanks
+    That is quite interesting, please tell me more.
+    > today I went for a walk     
+    Did you catch the game last night?
+    > I did, but my team lost
+    Funny weather we've been having, isn't it?
+    > yes but I hope next week is better
+    Let's change the subject.
+    > ok, lets talk about music
+    Why do you say that?
+    > because I like music!
+    Why do you say that?
+    > bye
+    It was nice talking to you, goodbye!
+    ```
+
+    작업에 맞는 하나의 솔루션은 [here](../solution/bot.py) 입니다
+
+    ✅ 잠시 멈추고 생각합니다
+
+    1. 랜덤 응답이 실제로 누군가를 이해했다고 생각하게 'trick'을 쓴다고 생각하나요?
+    2. 봇이 더 효과있으려면 어떤 기능을 해야 될까요?
+    3. 만약 봇이 문장의 의미를 정말 'understand' 했다면, 대화에서 이전 문장의 의미도 'remember'할 필요가 있을까요?
+
+---
+
+## 🚀 도전
+
+"잠시 멈추고 생각합니다" 항목 중 하나를 골라서 코드를 구현하거나 의사 코드로 종이에 솔루션을 작성합니다.
+
+다음 강의에서, natural language와 머신러닝을 분석하는 여러 다른 접근 방식에 대해 배울 예정입니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
+
+## 검토 & 자기주도 학습
+
+더 읽을 수 있는 틈에 아래 레퍼런스를 찾아봅니다.
+
+### 레퍼런스
+
+1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
+2. Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010. 
+
+## 과제 
+
+[Search for a bot](../assignment.md)
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
new file mode 100644
index 000000000..1252f57ee
--- /dev/null
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -0,0 +1,163 @@
+# 自然语言处理介绍
+这节课讲解了 *自然语言处理* 的简要历史和重要概念，*自然语言处理*是计算语言学的一个子领域。
+
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
+
+## 介绍
+众所周知，自然语言处理（Natural Language Processing, NLP）是机器学习在生产软件中应用最广泛的领域之一。
+
+✅ 你能想到哪些你日常生活中使用的软件可能嵌入了自然语言处理技术呢？或者，你经常使用的文字处理程序或移动应用程序中是否嵌入了自然语言处理技术呢？
+
+你将会学习到：
+
+- **什么是「语言」**。语言的发展历程，以及相关研究的主要领域。
+- **定义和概念**。你还将了解关于计算机文本处理的概念。包括解析（parsing）、语法（grammar）以及识别名词与动词。这节课中有一些编程任务；还有一些重要概念将在以后的课程中被引入，届时你也会练习通过编程实现其它概念。
+
+## 计算语言学
+
+计算语言学 (Computational Linguistics) 是一个经过几十年研究和发展的领域，它研究如何让计算机能使用、理解、翻译语言并使用语言交流。自然语言处理（NLP）是计算语言学中一个专注于计算机如何处理「自然的」（或者说，人类的）语言的相关领域。
+
+### 举例：电话号码识别
+
+如果你曾经在手机上使用语音输入替代键盘输入，或者使用过虚拟语音助手，那么你的语音将被转录（或者叫*解析*）为文本形式后进行处理。被检测到的关键字最后将被处理成手机或语音助手可以理解并可以依此做出行为的格式。
+
+![comprehension](../images/comprehension.png)
+> 真正意义上的语言理解很难！图源：[Jen Looper](https://twitter.com/jenlooper)
+
+### 这项技术是如何实现的？
+
+我们之所以可能完成这样的任务，是因为有人编写了一个计算机程序来实现它。几十年前，一些科幻作家预测，在未来，人类很大可能会能够他们的电脑对话，而电脑总是能准确地理解人类的意思。可惜的是，事实证明这个问题的解决比我们想象的更困难。虽然今天这个问题已经被初步解决，但在理解句子的含义时，要实现 “完美” 的自然语言处理仍然存在重大挑战 —— 理解幽默或是检测感情（比如讽刺）对于计算机来说尤其困难。
+
+现在，你可能会想起课堂上老师讲解的语法。在某些国家/地区，语法和语言学知识是学生的专题课内容。但在另一些国家/地区，不管是从小学习的第一语言（学习阅读和写作），还是之后学习的第二语言中，语法及语言学知识都是作为语言的一部分教学的。所以，如果你不能很好地区分名词与动词或者区分副词与形容词，请不要担心！
+
+你还为难以区分*一般现在时*与*现在进行时*而烦恼吗？没关系的，即使是对以这门语言为母语的人在内的大多数人来说，区分它们都很有挑战性。但是，计算机非常善于应用标准的规则，你将学会编写可以像人一样“解析”句子的代码。稍后你将面对的更大挑战是理解句子的*语义*和*情绪*。
+
+## 前提
+
+本节教程的主要先决条件是能够阅读和理解本节教程的语言。本节中没有数学问题或方程需要解决。虽然原作者用英文写了这教程，但它也被翻译成其他语言，所以你可能在阅读翻译内容。这节课的示例中涉及到很多语言种类（以比较不同语言的不同语法规则）。这些是*未*翻译的，但对它们的解释是翻译过的，所以你应该能理解它在讲什么。
+
+编程任务中，你将会使用 Python 语言，示例使用的是 Python 3.8 版本。
+
+在本节中你将需要并使用如下技能：
+
+- **Python 3**。你需要能够理解并使用 Python 3. 本课将会使用输入、循环、文件读取、数组功能。
+- **Visual Studio Code + 扩展**。 我们将使用 Visual Studio Code 及其 Python 扩展。你也可以使用你喜欢的 Python IDE。
+- **TextBlob**。[TextBlob](https://github.com/sloria/TextBlob) 是一个精简的 Python 文本处理库。请按照 TextBlob 网站上的说明，在您的系统上安装它（也需要安装语料库，安装代码如下所示）：
+- 
+   ```bash
+   pip install -U textblob
+   python -m textblob.download_corpora
+   ```
+
+> 💡 提示：你可以在 VS Code 环境中直接运行 Python。 点击 [文档](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) 查看更多信息。
+
+## 与机器对话
+
+试图让计算机理解人类语言的尝试最早可以追溯到几十年前。*Alan Turing* 是最早研究自然语言处理问题的科学家之一。
+
+### 图灵测试
+
+当图灵在 1950 年代研究*人工智能*时，他想出了这个思维实验：让人类和计算机通过打字的方式来交谈，其中人类并不知道对方是人类还是计算机。
+
+如果经过一定时间的交谈，人类无法确定对方是否是计算机，那么是否可以认为计算机正在“思考”？
+
+### 灵感 - “模仿游戏”
+
+这个想法来自一个名为 *模仿游戏* 的派对游戏，其中一名审讯者独自一人在一个房间里，负责确定在另一个房间里的两人的性别（男性或女性）。审讯者可以传递笔记，并且需要想出能够揭示神秘人性别的问题。当然，另一个房间的玩家也可以通过回答问题的方式来欺骗审讯者，例如用看似真诚的方式误导或迷惑审讯者。
+
+### Eliza 的研发
+
+在 1960 年代的麻省理工学院，一位名叫 *Joseph Weizenbaum* 的科学家开发了 [*Eliza*](https:/wikipedia.org/wiki/ELIZA)。Eliza 是一位计算机“治疗师”，它可以向人类提出问题并让人类觉得它能理解人类的回答。然而，虽然 Eliza 可以解析句子并识别某些语法结构和关键字以给出合理的答案，但不能说它*理解*了句子。如果 Eliza 看到的句子格式为“**I am** <u>sad</u>”（**我很** <u>难过</u>），它可能会重新排列并替换句子中的单词，回答 “How long have **you been** <u>sad</u>"（**你已经** <u>难过</u> 多久了）。
+
+看起来像是 Eliza 理解了这句话，还在询问关于这句话的问题，而实际上，它只是在改变时态和添加词语。如果 Eliza 没有在回答中发现它知道如何响应的词汇，它会给出一个随机响应，该响应可以适用于许多不同的语句。 Eliza 很容易被欺骗，例如，如果用户写了 "**You are** a <u>bicycle</u>"（**你是** 个 <u>自行车</u>），它可能会回复 "How long have **I been** a <u>bicycle</u>?"（**我已经是** 一个 <u>自行车</u> 多久了?），而不是更合理的回答。
+
+[![跟 Eliza 聊天](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco "跟 Eliza 聊天")
+
+> 🎥 点击上方的图片查看关于 Eliza 原型的视频
+
+> 旁注：如果你拥有 ACM 账户，你可以阅读 1996 年发表的 [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) 的原始介绍。或者，在[维基百科](https://wikipedia.org/wiki/ELIZA)上阅读有关 Eliza 的信息。
+
+## 练习 - 编程实现一个基础的对话机器人
+
+像 Eliza 一样的对话机器人是一个看起来可以智能地理解和响应用户输入的程序。与 Eliza 不同的是，我们的机器人不会用规则让它看起来像是在进行智能对话。我们的对话机器人将只有一种能力：它只会通过基本上可以糊弄所有普通对话的句子来随机回答，使得谈话能够继续进行。
+
+### 计划
+
+搭建聊天机器人的步骤
+
+1. 打印用户与机器人交互的使用说明
+2. 开启循环
+   1. 获取用户输入
+   2. 如果用户要求退出，就退出
+   3. 处理用户输入并选择一个回答（在这个例子中，从回答列表中随机选择一个回答）
+   4. 打印回答
+3. 重复步骤 2
+
+### 构建聊天机器人
+
+接下来让我们建一个聊天机器人。我们将从定义一些短语开始。
+
+1. 使用以下随机的回复（`random_responses`）在 Python 中自己创建此机器人：
+
+    ```python
+    random_responses = ["That is quite interesting, please tell me more.",
+                        "I see. Do go on.",
+                        "Why do you say that?",
+                        "Funny weather we've been having, isn't it?",
+                        "Let's change the subject.",
+                        "Did you catch the game last night?"]
+    ```
+
+    程序运行看起来应该是这样：（用户输入位于以 `>` 开头的行上）
+
+    ```output
+    Hello, I am Marvin, the simple robot.
+    You can end this conversation at any time by typing 'bye'
+    After typing each answer, press 'enter'
+    How are you today?
+    > I am good thanks
+    That is quite interesting, please tell me more.
+    > today I went for a walk     
+    Did you catch the game last night?
+    > I did, but my team lost
+    Funny weather we've been having, isn't it?
+    > yes but I hope next week is better
+    Let's change the subject.
+    > ok, lets talk about music
+    Why do you say that?
+    > because I like music!
+    Why do you say that?
+    > bye
+    It was nice talking to you, goodbye!
+    ```
+
+    示例程序在[这里](../solution/bot.py)。这只是一种可能的解决方案。
+
+    ✅ 停下来，思考一下
+   
+    1. 你认为这些随机响应能够“欺骗”人类，使人类认为机器人实际上理解了他们的意思吗？
+    2. 机器人需要哪些功能才能更有效的回应？
+    3. 如果机器人真的可以“理解”一个句子的意思，它是否也需要“记住”前面句子的意思？
+
+---
+
+## 🚀挑战
+
+在上面的「停下来，思考一下」板块中选择一个问题，尝试编程实现它们，或使用伪代码在纸上编写解决方案。
+
+在下一课中，您将了解解析自然语言和机器学习的许多其他方法。
+
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
+
+## 复习与自学
+
+看看下面的参考资料作为进一步的参考阅读。
+
+### 参考
+
+1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
+2. Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010. 
+
+## 任务
+
+[查找一个机器人](../assignment.md)
diff --git a/6-NLP/1-Introduction-to-NLP/translations/assignment.it.md b/6-NLP/1-Introduction-to-NLP/translations/assignment.it.md
new file mode 100644
index 000000000..02150752b
--- /dev/null
+++ b/6-NLP/1-Introduction-to-NLP/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Cercare un bot
+
+## Istruzioni
+
+I bot sono ovunque. Il compito: trovarne uno e adottarlo! È possibile trovarli sui siti web, nelle applicazioni bancarie e al telefono, ad esempio quando si chiamano società di servizi finanziari per consigli o informazioni sull'account. Analizzare il bot e vedire se si riesce a confonderlo. Se si riesce a confondere il bot, perché si pensa sia successo? Scrivere un breve articolo sulla propria esperienza.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------------------- |
+|          | Viene scritto un documento a pagina intera, che spiega la presunta architettura del bot e delinea l'esperienza con esso | Un documento è incompleto o non ben concepito | Nessun documento inviato |
diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md
index d816b7fe3..829df67ba 100644
--- a/6-NLP/2-Tasks/README.md
+++ b/6-NLP/2-Tasks/README.md
@@ -2,7 +2,7 @@
 
 For most *natural language processing* tasks, the text to be processed, must be broken down, examined, and the results stored or cross referenced with rules and data sets. These tasks, allows the programmer to derive the _meaning_ or _intent_ or only the _frequency_ of terms and words in a text.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
 
 Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist.
 
@@ -203,7 +203,7 @@ Implement the bot in the prior knowledge check and test it on a friend. Can it t
 
 Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?'
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md
new file mode 100644
index 000000000..fe70ccc03
--- /dev/null
+++ b/6-NLP/2-Tasks/translations/README.it.md
@@ -0,0 +1,214 @@
+# Compiti e tecniche comuni di elaborazione del linguaggio naturale
+
+Per la maggior parte delle attività di *elaborazione del linguaggio naturale* , il testo da elaborare deve essere suddiviso, esaminato e i risultati archiviati o incrociati con regole e insiemi di dati. Queste attività consentono al programmatore di derivare il _significato_ o l'_intento_ o solo la _frequenza_ di termini e parole in un testo.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/?loc=it)
+
+Si esaminano le comuni tecniche utilizzate nell'elaborazione del testo. Combinate con machine learning, queste tecniche aiutano ad analizzare grandi quantità di testo in modo efficiente. Prima di applicare machine learning a queste attività, tuttavia, occorre cercare di comprendere i problemi incontrati da uno specialista in NLP.
+
+## Compiti comuni per NLP
+
+Esistono diversi modi per analizzare un testo su cui si sta lavorando. Ci sono attività che si possono eseguire e attraverso le quali si è in grado di valutare la comprensione del testo e trarre conclusioni. Di solito si eseguono queste attività in sequenza.
+
+### Tokenizzazione
+
+Probabilmente la prima cosa che la maggior parte degli algoritmi di NLP deve fare è dividere il testo in token o parole. Anche se questo sembra semplice, dover tenere conto della punteggiatura e dei delimitatori di parole e frasi di lingue diverse può renderlo complicato. Potrebbe essere necessario utilizzare vari metodi per determinare le demarcazioni.
+
+![Tokenizzazione](../images/tokenization.png)
+> Tokenizzazione di una frase da **Orgoglio e Pregiudizio**. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+### Embedding
+
+I [word embeddings](https://it.wikipedia.org/wiki/Word_embedding) sono un modo per convertire numericamente i dati di testo. Gli embedding vengono eseguiti in modo tale che le parole con un significato simile o le parole usate insieme vengano raggruppate insieme.
+
+![word embeddings](../images/embedding.png)
+> "I have the highest respect for your nerves, they are my old friends." - Incorporazioni di parole per una frase in **Orgoglio e Pregiudizio**. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+✅ Provare [questo interessante strumento](https://projector.tensorflow.org/) per sperimentare i word embedding. Facendo clic su una parola vengono visualizzati gruppi di parole simili: gruppi di "toy" con "disney", "lego", "playstation" e "console".
+
+### Analisi e codifica di parti del discorso
+
+Ogni parola che è stata tokenizzata può essere etichettata come parte del discorso: un sostantivo, un verbo o un aggettivo. La frase `the quick red fox jumped over the lazy brown dog` potrebbe essere etichettata come fox = sostantivo, jumped = verbo.
+
+![elaborazione](../images/parse.png)
+
+> Analisi di una frase da **Orgoglio e Pregiudizio**. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+L'analisi consiste nel riconoscere quali parole sono correlate tra loro in una frase - per esempio `the quick red fox jumped` è una sequenza aggettivo-sostantivo-verbo che è separata dalla sequenza `lazy brown dog` .
+
+### Frequenze di parole e frasi
+
+Una procedura utile quando si analizza un corpo di testo di grandi dimensioni è creare un dizionario di ogni parola o frase di interesse e con quale frequenza viene visualizzata. La frase `the quick red fox jumped over the lazy brown dog` ha una frequenza di parole di 2 per the.
+
+Si esamina un testo di esempio in cui si conta la frequenza delle parole. La poesia di Rudyard Kipling The Winners contiene i seguenti versi:
+
+```output
+What the moral? Who rides may read.
+When the night is thick and the tracks are blind
+A friend at a pinch is a friend, indeed,
+But a fool to wait for the laggard behind.
+Down to Gehenna or up to the Throne,
+He travels the fastest who travels alone.
+```
+
+Poiché le frequenze delle frasi possono essere o meno insensibili alle maiuscole o alle maiuscole, a seconda di quanto richiesto, la frase `a friend` ha una frequenza di 2, `the` ha una frequenza di 6 e `travels` è 2.
+
+### N-grammi
+
+Un testo può essere suddiviso in sequenze di parole di una lunghezza prestabilita, una parola singola (unigramma), due parole (bigrammi), tre parole (trigrammi) o un numero qualsiasi di parole (n-grammi).
+
+Ad esempio, `the quick red fox jumped over the lazy brown dog` con un punteggio n-grammo di 2 produce i seguenti n-grammi:
+
+1. the quick
+2. quick red
+3. red fox
+4. fox jumped
+5. jumped over
+6. over the
+7. the lazy
+8. lazy brown
+9. brown dog
+
+Potrebbe essere più facile visualizzarlo come una casella scorrevole per la frase. Qui è per n-grammi di 3 parole, l'n-grammo è in grassetto in ogni frase:
+
+1. **the quick red** fox jumped over the lazy brown dog
+2. the **quick red fox** jumped over the lazy brown dog
+3. the quick **red fox jumped** over the lazy brown dog
+4. the quick red **fox jumped over** the lazy brown dog
+5. the quick red fox **jumped over the** lazy brown dog
+6. the quick red fox jumped **over the lazy** brown dog
+7. the quick red fox jumped over **the lazy brown** dog
+8. the quick red fox jumped over the **lazy brown dog**
+
+![finestra scorrevole n-grammi](../images/n-grams.gif)
+
+> Valore N-gram di 3: Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+### Estrazione frase nominale
+
+Nella maggior parte delle frasi, c'è un sostantivo che è il soggetto o l'oggetto della frase. In inglese, è spesso identificabile con "a" o "an" o "the" che lo precede. Identificare il soggetto o l'oggetto di una frase "estraendo la frase nominale" è un compito comune in NLP quando si cerca di capire il significato di una frase.
+
+✅ Nella frase "I cannot fix on the hour, or the spot, or the look or the words, which laid the foundation. It is too long ago. I was in the middle before I knew that I had begun.", si possono identificare i nomi nelle frasi?
+
+Nella frase `the quick red fox jumped over the lazy brown dog` ci sono 2 frasi nominali: **quick red fox** e **lazy brown dog**.
+
+### Analisi del sentiment
+
+Una frase o un testo può essere analizzato per il sentimento, o quanto *positivo* o *negativo* esso sia. Il sentimento si misura in *polarità* e *oggettività/soggettività*. La polarità è misurata da -1,0 a 1,0 (da negativo a positivo) e da 0,0 a 1,0 (dal più oggettivo al più soggettivo).
+
+✅ In seguito si imparerà che ci sono diversi modi per determinare il sentimento usando machine learning ma un modo è avere un elenco di parole e frasi che sono classificate come positive o negative da un esperto umano e applicare quel modello al testo per calcolare un punteggio di polarità. Si riesce a vedere come funzionerebbe in alcune circostanze e meno bene in altre?
+
+### Inflessione
+
+L'inflessione consente di prendere una parola e ottenere il singolare o il plurale della parola.
+
+### Lemmatizzazione
+
+Un *lemma* è la radice o il lemma per un insieme di parole, ad esempio *volò*, *vola*, *volando* ha un lemma del verbo *volare*.
+
+Ci sono anche utili database disponibili per il ricercatore NPL, in particolare:
+
+### WordNet
+
+[WordNet](https://wordnet.princeton.edu/) è un database di parole, sinonimi, contari e molti altri dettagli per ogni parola in molte lingue diverse. È incredibilmente utile quando si tenta di costruire traduzioni, correttori ortografici o strumenti di lingua di qualsiasi tipo.
+
+## Librerie NPL
+
+Fortunatamente, non è necessario creare tutte queste tecniche da soli, poiché sono disponibili eccellenti librerie Python che le rendono molto più accessibili agli sviluppatori che non sono specializzati nell'elaborazione del linguaggio naturale o in machine learning. Le prossime lezioni includono altri esempi di queste, ma qui si impareranno alcuni esempi utili che aiuteranno con il prossimo compito.
+
+### Esercizio: utilizzo della libreria `TextBlob`
+
+Si usa una libreria chiamata TextBlob in quanto contiene API utili per affrontare questi tipi di attività. TextBlob "sta sulle spalle giganti di [NLTK](https://nltk.org) e [pattern](https://github.com/clips/pattern), e si sposa bene con entrambi". Ha una notevole quantità di ML incorporato nella sua API.
+
+> Nota: per TextBlob è disponibile un'utile [guida rapida](https://textblob.readthedocs.io/en/dev/quickstart.html#quickstart), consigliata per sviluppatori Python esperti
+
+Quando si tenta di identificare *le frasi nominali*, TextBlob offre diverse opzioni di estrattori per trovarle.
+
+1. Dare un'occhiata a `ConllExtractor`.
+
+   ```python
+   from textblob import TextBlob
+   from textblob.np_extractors import ConllExtractor
+   # importa e crea un extrattore Conll da usare successivamente
+   extractor = ConllExtractor()
+
+   # quando serve un estrattore di frasi nominali:
+   user_input = input("> ")
+   user_input_blob = TextBlob(user_input, np_extractor=extractor)  # notare specificato estrattore non predefinito
+   np = user_input_blob.noun_phrases                                    
+   ```
+
+   > Cosa sta succedendo qui? [ConllExtractor](https://textblob.readthedocs.io/en/dev/api_reference.html?highlight=Conll#textblob.en.np_extractors.ConllExtractor) è "Un estrattore di frasi nominali che utilizza l'analisi dei blocchi addestrata con il corpus di formazione ConLL-2000". ConLL-2000 si riferisce alla Conferenza del 2000 sull'apprendimento computazionale del linguaggio naturale. Ogni anno la conferenza ha ospitato un workshop per affrontare uno spinoso problema della NPL, e nel 2000 è stato lo spezzettamento dei sostantivi. Un modello è stato addestrato sul Wall Street Journal, con "sezioni 15-18 come dati di addestramento (211727 token) e sezione 20 come dati di test (47377 token)". Si possono guardare le procedure utilizzate [qui](https://www.clips.uantwerpen.be/conll2000/chunking/) e i [risultati](https://ifarm.nl/erikt/research/np-chunking.html).
+
+### Sfida: migliorare il bot con NPL
+
+Nella lezione precedente si è creato un bot di domande e risposte molto semplice. Ora si renderà Marvin un po' più comprensivo analizzando l'input per il sentimento e stampando una risposta che corrisponda al sentimento. Si dovrà anche identificare una frase nominale `noun_phrase` e chiedere informazioni su di essa.
+
+I passaggi durante la creazione di un bot conversazionale:
+
+1. Stampare le istruzioni che consigliano all'utente come interagire con il bot
+2. Avviare il ciclo
+   1. Accettare l'input dell'utente
+   2. Se l'utente ha chiesto di uscire, allora si esce
+   3. Elaborare l'input dell'utente e determinare la risposta di sentimento appropriata
+   4. Se viene rilevata una frase nominale nel sentimento, pluralizzala e chiedere ulteriori input su quell'argomento
+   5. Stampare la risposta
+3. Riprendere il ciclo dal passo 2
+
+Ecco il frammento di codice per determinare il sentimento usando TextBlob. Si noti che ci sono solo quattro *gradienti* di risposta al sentimento (se ne potrebbero avere di più se lo si desidera):
+
+```python
+if user_input_blob.polarity <= -0.5:
+  response = "Oh dear, that sounds bad. "  # Oh caro, è terribile
+elif user_input_blob.polarity <= 0: 
+  response = "Hmm, that's not great. "  # Mmm, non è eccezionale
+elif user_input_blob.polarity <= 0.5:
+  response = "Well, that sounds positive. "  # Bene, questo è positivo
+elif user_input_blob.polarity <= 1:
+  response = "Wow, that sounds great. "  # Wow, sembra eccezionale
+```
+
+Ecco un risultato di esempio a scopo di guida (l'input utente è sulle righe che iniziano per >):
+
+```output
+Hello, I am Marvin, the friendly robot.
+You can end this conversation at any time by typing 'bye'
+After typing each answer, press 'enter'
+How are you today?
+> I am ok
+Well, that sounds positive. Can you tell me more?
+> I went for a walk and saw a lovely cat
+Well, that sounds positive. Can you tell me more about lovely cats?
+> cats are the best. But I also have a cool dog
+Wow, that sounds great. Can you tell me more about cool dogs?
+> I have an old hounddog but he is sick
+Hmm, that's not great. Can you tell me more about old hounddogs?
+> bye
+It was nice talking to you, goodbye!
+```
+
+Una possibile soluzione al compito è [qui](../solution/bot.py)
+
+Verifica delle conoscenze
+
+1. Si ritiene che le risposte casuali "ingannerebbero" qualcuno facendogli pensare che il bot le abbia effettivamente capite?
+2. Identificare la frase nominale rende il bot più 'credibile'?
+3. Perché estrarre una "frase nominale" da una frase sarebbe una cosa utile da fare?
+
+---
+
+Implementare il bot nel controllo delle conoscenze precedenti e testarlo su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?'
+
+## 🚀 Sfida
+
+Prendere un'attività dalla verifica delle conoscenze qui sopra e provare a implementarla. Provare il bot su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?'
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Nelle prossime lezioni si imparerà di più sull'analisi del sentiment. Fare ricerche su questa interessante tecnica in articoli come questi su [KDNuggets](https://www.kdnuggets.com/tag/nlp)
+
+## Compito
+
+[Fare rispondere un bot](assignment.it.md)
diff --git a/6-NLP/2-Tasks/translations/README.ko.md b/6-NLP/2-Tasks/translations/README.ko.md
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+# 일반적인 natural language processing 작업과 기술
+
+대부분 *natural language processing* 작업으로, 처리한 텍스트를 분해하고, 검사하고, 그리고 결과를 저장하거나 룰과 데이터셋을 서로 참조했습니다. 이 작업들로, 프로그래머가 _meaning_ 또는 _intent_ 또는 오직 텍스트에 있는 용어와 단어의 _frequency_ 만 끌어낼 수 있게 합니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
+
+텍스트를 처리하며 사용했던 일반적인 기술을 찾아봅니다. 머신러닝에 결합된, 이 기술은 효율적으로 많은 텍스트를 분석하는데 도와줍니다. 그러나, 이 작업에 ML을 적용하기 전에, NLP 스페셜리스트가 일으킨 문제를 이해합니다.
+
+## NLP의 공통 작업
+
+작업하고 있는 텍스트를 분석하는 다양한 방식이 있습니다. 진행할 작업과 이 작업으로 텍스트 이해도로 측정하고 결론을 지을 수 있습니다. 대부분 순서대로 작업합니다.
+
+### Tokenization
+
+아마 많은 NLP 알고리즘으로 처음 할 일은 토큰이나, 단어로 텍스트를 나누는 것입니다. 간단하게 들리지만, 문장 부호와 다른 언어의 단어와 문장 구분 기호를 고려하는 건 까다로울 수 있습니다.
+
+![tokenization](../images/tokenization.png)
+> Tokenizing a sentence from **Pride and Prejudice**. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+### Embeddings
+
+[Word embeddings](https://wikipedia.org/wiki/Word_embedding)은 텍스트 데이터를 숫자처럼 변환하는 방식입니다. Embeddings은 의미가 비슷한 단어이거나 cluster와 단어를 함께 쓰는 방식으로 이루어집니다.
+
+![word embeddings](../images/embedding.png)
+> "I have the highest respect for your nerves, they are my old friends." - Word embeddings for a sentence in **Pride and Prejudice**. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+✅ [this interesting tool](https://projector.tensorflow.org/)로 단어 embeddings를 실험해봅니다. 하나의 단어를 클릭하면 비슷한 단어의 클러스터를 보여줍니다: 'disney', 'lego', 'playstation', 그리고 'console'이 'toy' 클러스터에 있있습니다.
+
+### 파싱 & Part-of-speech Tagging
+
+토큰화된 모든 단어는 품사를 명사, 동사, 형용사로 테그할 수 있습니다. `the quick red fox jumped over the lazy brown dog` 문장은 fox = noun, jumped = verb로 POS 태그될 수 있습니다.
+
+![parsing](../images/parse.png)
+
+> Parsing a sentence from **Pride and Prejudice**. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+파싱은 문장에서 각자 단어들이 관련있는지 인식하는 것입니다. 예시로 `the quick red fox jumped`는 `lazy brown dog` 시퀀스와 나눠진 형용사-명사-동사 시퀀스 입니다.
+
+### 단어와 구문 빈도
+
+텍스트의 많은 분량을 분석할 때 유용한 순서는 흥미있는 모든 단어 또는 자주 나오는 사전을 만드는 것입니다. `the quick red fox jumped over the lazy brown dog` 문구는 단어 빈도가 2 입니다.
+
+단어 빈도를 세는 예시를 찾아봅니다. Rudyard Kipling의 시인 The Winners는 다음을 담고 있습니다:
+
+```output
+What the moral? Who rides may read.
+When the night is thick and the tracks are blind
+A friend at a pinch is a friend, indeed,
+But a fool to wait for the laggard behind.
+Down to Gehenna or up to the Throne,
+He travels the fastest who travels alone.
+```
+
+구문 빈도는 필요에 의해서 대소문자를 구분하지 않거나 구분하므로, `a friend`는 빈도 2이고 `the`는 빈도 6, 그리고 `travels`는 2입니다.
+
+### N-grams
+
+텍스트는 지정한 길이의 단어 시퀀스, 한 단어(unigram), 두 단어(bigrams), 세 단어(trigrams) 또는 모든 수의 단어(n-grams)로 나눌 수 있습니다.
+
+예시로 n-gram 2점인 `the quick red fox jumped over the lazy brown dog`는 다음 n-grams을 만듭니다:
+
+1. the quick 
+2. quick red 
+3. red fox
+4. fox jumped 
+5. jumped over 
+6. over the 
+7. the lazy 
+8. lazy brown 
+9. brown dog
+
+문장 위 슬라이드 박스로 시각화하는 게 쉬울 수 있습니다. 여기는 3 단어로 이루어진 n-grams이며, n-gram은 각 문장에서 볼드체로 있습니다:
+
+1.   <u>**the quick red**</u> fox jumped over the lazy brown dog
+2.   the **<u>quick red fox</u>** jumped over the lazy brown dog
+3.   the quick **<u>red fox jumped</u>** over the lazy brown dog
+4.   the quick red **<u>fox jumped over</u>** the lazy brown dog
+5.   the quick red fox **<u>jumped over the</u>** lazy brown dog
+6.   the quick red fox jumped **<u>over the lazy</u>** brown dog
+7.   the quick red fox jumped over <u>**the lazy brown**</u> dog
+8.   the quick red fox jumped over the **<u>lazy brown dog</u>**
+
+![n-grams sliding window](../images/n-grams.gif)
+
+> N-gram value of 3: Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+### Noun phrase 추출 
+
+대부분 문장에서, 문장의 주어나, 목적어인 명사가 있습니다. 영어에서, 자주  'a' 또는 'an' 또는 'the'가 앞에 오게 가릴 수 있습니다. "noun phrase 추출'로 문장의 주어 또는 목적어를 가려내려 하는 것은 NLP에서 문장의 의미를 이해할 때 일반적인 작업입니다.
+
+✅ "I cannot fix on the hour, or the spot, or the look or the words, which laid the foundation. It is too long ago. I was in the middle before I knew that I had begun." 문장에서, noun phrases를 가려낼 수 있나요?
+
+`the quick red fox jumped over the lazy brown dog` 문장에서 noun phrases 2개가 있습니다: **quick red fox** 와 **lazy brown dog**.
+
+### 감정 분석
+
+문장이나 텍스트는 감정이나, *positive* 또는 *negative*인지 분석할 수 있습니다. 감정은 *polarity* 와 *objectivity/subjectivity*로 측정됩니다. Polarity는 -1.0 에서 1.0 (negative 에서 positive) 이며 0.0 에서 1.0 (가장 객관적에서 가장 주관적으로)으로 측정됩니다.
+
+✅ 나중에 머신러닝으로 감정을 판단하는 다른 방식을 배울 수 있지만, 하나의 방식은 전문가가 positive 또는 negative로 분류된 단어와 구분의 리스트를 가지고 polarity 점수를 계산한 텍스트로 모델을 적용하는 것입니다. 일부 상황에서 어떻게 작동하고 다른 상황에서도 잘 동작하는지 볼 수 있나요?
+
+### Inflection
+
+Inflection은 단어를 가져와서 단수나 복수 단어를 얻게 됩니다.
+
+### Lemmatization
+
+*lemma*는 단어 세트에서 어원이나 표제어고, 예시로 *flew*, *flies*, *flying*은 *fly* 동사의 lemma를 가지고 있습니다.
+
+특히, NLP 연구원이 사용할 수 있는 유용한 데이터베이스도 있습니다:
+
+### WordNet
+
+[WordNet](https://wordnet.princeton.edu/)은 다양한 언어로 모든 단어를 단어, 동의어, 반의어 그리고 다양한 기타 내용으로 이룬 데이터베이스입니다. 번역, 맞춤법 검사, 또는 모든 타입의 언어 도구를 만드려고 시도할 때 매우 유용합니다.
+
+## NLP 라이브러리
+
+운 좋게, 훌륭한 Python 라이브러리로 natural language processing이나 머신러닝에 전문적이지 않은 개발자도 쉽게 접근할 수 있으므로, 이 기술을 스스로 다 만들지 않아도 됩니다. 다음 강의에서 더 많은 예시를 포함하지만, 여기에서 다음 작업에 도움이 될 몇 유용한 예시를 배울 예정입니다.
+
+### 연습 - `TextBlob` 라이브러리 사용
+
+이 타입의 작업을 처리하는 유용한 API를 포함한 TextBlob이라고 불리는 라이브리를 사용합니다. TextBlob은 "stands on the giant shoulders of [NLTK](https://nltk.org) and [pattern](https://github.com/clips/pattern), and plays nicely with both."이며  API에서 상당히 많이 ML이 녹아들어졌습니다.
+
+> 노트: 잘하는 Python 개발자를 위해서 추천하는 TextBlob의 유용한 [Quick Start](https://textblob.readthedocs.io/en/dev/quickstart.html#quickstart) 가이드가 존재합니다
+
+*noun phrases* 식별하려고 시도하는 순간, TextBlob은 noun phrases를 찾고자 몇 추출 옵션을 제공합니다.
+
+1. `ConllExtractor` 봅니다.
+
+    ```python
+    from textblob import TextBlob
+    from textblob.np_extractors import ConllExtractor
+    # import and create a Conll extractor to use later 
+    extractor = ConllExtractor()
+    
+    # later when you need a noun phrase extractor:
+    user_input = input("> ")
+    user_input_blob = TextBlob(user_input, np_extractor=extractor)  # note non-default extractor specified
+    np = user_input_blob.noun_phrases                                    
+    ```
+
+    > 어떤 일이 생기나요? [ConllExtractor](https://textblob.readthedocs.io/en/dev/api_reference.html?highlight=Conll#textblob.en.np_extractors.ConllExtractor)는 "A noun phrase extractor that uses chunk parsing trained with the ConLL-2000 training corpus."입니다. ConLL-2000은 2000 Conference on Computational Natural Language Learning을 의미합니다. 매년 까다로운 NLP 문제를 해결하기 위한 워크숍을 호스트하는 컨퍼런스이며, 2000년에는 noun chunking이었습니다. 모델은 "sections 15-18 as training data (211727 tokens) and section 20 as test data (47377 tokens)"로 Wall Street Journal에서 훈련되었습니다. [here](https://www.clips.uantwerpen.be/conll2000/chunking/)에서 사용한 순서와 [results](https://ifarm.nl/erikt/research/np-chunking.html)를 볼 수 있습니다.
+
+### 도전 - NLP로 봇 개선하기
+
+이전 강의에서 매우 간단한 Q&A 봇을 만들었습니다. 이제, 감정을 넣어서 분석하고 감정과 맞는 응답을 출력하여 Marvin을 좀 더 감성적으로 만듭니다. 또 `noun_phrase`를 식별하고 물어볼 필요가 있습니다.
+
+더 좋은 대화 봇을 만들 때 단계가 있습니다:
+
+1. 사용자에게 봇과 상호작용하는 방식 출력
+2. 반복 시작
+   1. 사용자 입력 승인
+   2. 만약 사용자가 종료 요청하면, 종료
+   3. 사용자 입력 처리하고 적절한 감정 응답 결정
+   4. 만약 감정에서 noun phrase 탐지되면, 복수형 변경하고 이 토픽에서 입력 추가 요청
+   5. 응답 출력
+3. 2 단계로 돌아가서 반복
+
+여기 TextBlob으로 감정을 탐지하는 코드 스니펫이 있습니다. 감정 응답에 4개 *gradients*만 있다는 점을 참고합니다 (좋아하는 경우 더 가질 수 있음):
+
+```python
+if user_input_blob.polarity <= -0.5:
+  response = "Oh dear, that sounds bad. "
+elif user_input_blob.polarity <= 0:
+  response = "Hmm, that's not great. "
+elif user_input_blob.polarity <= 0.5:
+  response = "Well, that sounds positive. "
+elif user_input_blob.polarity <= 1:
+  response = "Wow, that sounds great. "
+```
+
+여기는 가이드할 약간의 샘플 출력이 있습니다 (사용자 입력은 라인 시작에 > 있습니다):
+
+```output
+Hello, I am Marvin, the friendly robot.
+You can end this conversation at any time by typing 'bye'
+After typing each answer, press 'enter'
+How are you today?
+> I am ok
+Well, that sounds positive. Can you tell me more?
+> I went for a walk and saw a lovely cat
+Well, that sounds positive. Can you tell me more about lovely cats?
+> cats are the best. But I also have a cool dog
+Wow, that sounds great. Can you tell me more about cool dogs?
+> I have an old hounddog but he is sick
+Hmm, that's not great. Can you tell me more about old hounddogs?
+> bye
+It was nice talking to you, goodbye!
+```
+
+작업에 대한 하나의 가능한 솔루션은 [here](../solution/bot.py) 있습니다.
+
+✅ 지식 점검
+
+1. 봇이 그 사람들을 실제로 이해했다고 생각할 수 있게 감성적인 반응으로 'trick'할 수 있다고 생각하나요?
+2. noun phrase를 식별하면 봇을 더 '믿을' 수 있나요?
+3. 문장에서 'noun phrase'를 추출하는 이유는 무엇인가요?
+
+---
+
+이전의 지식 점검에서 봇을 구현하고 친구에게 테스트해봅니다. 그들을 속일 수 있나요? 좀 더 '믿을 수'있게 봇을 만들 수 있나요?
+
+## 🚀 도전
+
+이전의 지식 점검에서 작업하고 구현합니다. 친구에게 봇을 테스트합니다. 그들을 속일 수 있나요? 좀 더 '믿을 수'있게 봇을 만들 수 있나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
+
+## 검토 & 자기주도 학습
+
+다음 몇 강의에서 감정 분석에 대하여 더 배울 예정입니다. [KDNuggets](https://www.kdnuggets.com/tag/nlp) 같은 아티클에서 흥미로운 기술을 연구합니다.
+
+## 과제 
+
+[Make a bot talk back](../assignment.md)
diff --git a/6-NLP/2-Tasks/translations/assignment.it.md b/6-NLP/2-Tasks/translations/assignment.it.md
new file mode 100644
index 000000000..fddf44bc0
--- /dev/null
+++ b/6-NLP/2-Tasks/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Fare rispondere un bot
+
+## Istruzioni
+
+Nelle ultime lezioni, si è programmato un bot di base con cui chattare. Questo bot fornisce risposte casuali finché non si dice ciao ("bye"). Si possono rendere le risposte un po' meno casuali e attivare le risposte se si dicono cose specifiche, come "perché" o "come"? Si pensi a come machine learning potrebbe rendere questo tipo di lavoro meno manuale mentre si estende il bot. Si possono utilizzare le librerie NLTK o TextBlob per semplificare le attività.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------------------------------------------- | ------------------------------------------------ | ----------------------- |
+|          | Viene presentato e documentato un nuovo file bot.py | Viene presentato un nuovo file bot ma contiene bug | Non viene presentato un file |
diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md
index bcd6cdd1a..e60bd9554 100644
--- a/6-NLP/3-Translation-Sentiment/README.md
+++ b/6-NLP/3-Translation-Sentiment/README.md
@@ -2,7 +2,7 @@
 
 In the previous lessons you learned how to build a basic bot using `TextBlob`, a library that embeds ML behind-the-scenes to perform basic NLP tasks such as noun phrase extraction. Another important challenge in computational linguistics is accurate _translation_ of a sentence from one spoken or written language to another.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
 
 Translation is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This approach means that you would take the following steps:
 
@@ -103,7 +103,7 @@ Sentiment is measured in with a *polarity* of -1 to 1, meaning -1 is the most ne
 
 Take another look at Jane Austen's *Pride and Prejudice*. The text is available here at [Project Gutenberg](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm). The sample below shows a short program which analyses the sentiment of first and last sentences from the book and display its sentiment polarity and subjectivity/objectivity score.
 
-You should us the `TextBlob` library (described above) to determine `sentiment` (you do not have to write your own sentiment calculator) in the following task.
+You should use the `TextBlob` library (described above) to determine `sentiment` (you do not have to write your own sentiment calculator) in the following task.
 
 ```python
 from textblob import TextBlob
@@ -143,7 +143,7 @@ Your task is to determine, using sentiment polarity, if *Pride and Prejudice* ha
    1. If the polarity is 1 or -1 store the sentence in an array or list of positive or negative messages
 5. At the end, print out all the positive sentences and negative sentences (separately) and the number of each.
 
-Here is a sample [solution](solutions/notebook.ipynb).
+Here is a sample [solution](solution/notebook.ipynb).
 
 ✅ Knowledge Check
 
@@ -176,7 +176,7 @@ Here is a sample [solution](solutions/notebook.ipynb).
 
 Can you make Marvin even better by extracting other features from the user input?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/6-NLP/3-Translation-Sentiment/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/6-NLP/3-Translation-Sentiment/solution/R/README.md b/6-NLP/3-Translation-Sentiment/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md
new file mode 100644
index 000000000..298939894
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md
@@ -0,0 +1,187 @@
+# Traduzione e analisi del sentiment con ML
+
+Nelle lezioni precedenti si è imparato come creare un bot di base utilizzando `TextBlob`, una libreria che incorpora machine learning dietro le quinte per eseguire attività di base di NPL come l'estrazione di frasi nominali. Un'altra sfida importante nella linguistica computazionale è _la traduzione_ accurata di una frase da una lingua parlata o scritta a un'altra.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/?loc=it)
+
+La traduzione è un problema molto difficile, aggravato dal fatto che ci sono migliaia di lingue e ognuna può avere regole grammaticali molto diverse. Un approccio consiste nel convertire le regole grammaticali formali per una lingua, come l'inglese, in una struttura non dipendente dalla lingua e quindi tradurla convertendola in un'altra lingua. Questo approccio significa che si dovrebbero eseguire i seguenti passaggi:
+
+1. **Identificazione**. Identificare o taggare le parole nella lingua di input in sostantivi, verbi, ecc.
+2. **Creare la traduzione**. Produrre una traduzione diretta di ogni parola nel formato della lingua di destinazione.
+
+### Frase di esempio, dall'inglese all'irlandese
+
+In inglese, la frase _I feel happy_ (sono felice) è composta da tre parole nell'ordine:
+
+- **soggetto** (I)
+- **verbo** (feel)
+- **aggettivo** (happy)
+
+Tuttavia, nella lingua irlandese, la stessa frase ha una struttura grammaticale molto diversa - emozioni come "*felice*" o "*triste*" sono espresse come se fossero *su se stessi*.
+
+La frase inglese `I feel happy` in irlandese sarebbe `Tá athas orm`. Una traduzione *letterale* sarebbe `Happy is upon me` (felicità su di me).
+
+Un oratore irlandese che traduce in inglese direbbe `I feel happy`, non `Happy is upon me`, perché capirebbe il significato della frase, anche se le parole e la struttura della frase sono diverse.
+
+L'ordine formale per la frase in irlandese sono:
+
+- **verbo** (Tá o is)
+- **aggettivo** (athas, o happy)
+- **soggetto** (orm, o upon me)
+
+## Traduzione
+
+Un programma di traduzione ingenuo potrebbe tradurre solo parole, ignorando la struttura della frase.
+
+✅ Se si è imparato una seconda (o terza o più) lingua da adulto, si potrebbe aver iniziato pensando nella propria lingua madre, traducendo un concetto parola per parola nella propria testa nella seconda lingua, e poi pronunciando la traduzione. Questo è simile a quello che stanno facendo i programmi per computer di traduzione ingenui. È importante superare questa fase per raggiungere la fluidità!
+
+La traduzione ingenua porta a cattive (e talvolta esilaranti) traduzioni errate: `I feel happy` si traduce letteralmente in `Mise bhraitheann athas` in irlandese. Ciò significa (letteralmente) `me feel happy` e non è una frase irlandese valida. Anche se l'inglese e l'irlandese sono lingue parlate su due isole vicine, sono lingue molto diverse con strutture grammaticali diverse.
+
+> E' possibile guardare alcuni video sulle tradizioni linguistiche irlandesi come [questo](https://www.youtube.com/watch?v=mRIaLSdRMMs)
+
+### Approcci di machine learning
+
+Finora, si è imparato a conoscere l'approccio delle regole formali all'elaborazione del linguaggio naturale. Un altro approccio consiste nell'ignorare il significato delle parole e _utilizzare invece machine learning per rilevare i modelli_. Questo può funzionare nella traduzione se si ha molto testo (un *corpus*) o testi (*corpora*) sia nella lingua di origine che in quella di destinazione.
+
+Si prenda ad esempio il caso di *Pride and Prejudice (Orgoglio* e pregiudizio),un noto romanzo inglese scritto da Jane Austen nel 1813. Se si consulta il libro in inglese e una traduzione umana del libro in *francese*, si potrebberoi rilevare frasi in uno che sono tradotte *idiomaticamente* nell'altro. Si farà fra un minuto.
+
+Ad esempio, quando una frase inglese come `I have no money` (non ho denaro) viene tradotta letteralmente in francese, potrebbe diventare `Je n'ai pas de monnaie`. "Monnaie" è un complicato "falso affine" francese, poiché "money" e "monnaie" non sono sinonimi. Una traduzione migliore che un essere umano potrebbe fare sarebbe `Je n'ai pas d'argent`, perché trasmette meglio il significato che non si hanno soldi (piuttosto che "moneta spicciola" che è il significato di "monnaie").
+
+![monnaie](../images/monnaie.png)
+
+> Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+Se un modello ML ha abbastanza traduzioni umane su cui costruire un modello, può migliorare l'accuratezza delle traduzioni identificando modelli comuni in testi che sono stati precedentemente tradotti da umani esperti parlanti di entrambe le lingue.
+
+### Esercizio - traduzione
+
+Si può usare `TextBlob` per tradurre le frasi. Provare la famosa prima riga di **Orgoglio e Pregiudizio**:
+
+```python
+from textblob import TextBlob
+
+blob = TextBlob(
+    "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife!"
+)
+print(blob.translate(to="fr"))
+
+```
+
+`TextBlob` fa un buon lavoro con la traduzione: "C'est une vérité universalllement reconnue, qu'un homme célibataire en possession d'une bonne fortune doit avoir besoin d'une femme!".
+
+Si può sostenere che la traduzione di TextBlob è molto più esatta, infatti, della traduzione francese del 1932 del libro di V. Leconte e Ch. Pressoir:
+
+"C'est une vérité universelle qu'un celibataire pourvu d'une belle fortune doit avoir envie de se marier, et, si peu que l'on sache de son sentiment à cet egard, lorsqu'il arrive dans une nouvelle residence, cette idée est si bien fixée dans l'esprit de ses voisins qu'ils le considèrent sur-le-champ comme la propriété légitime de l'une ou l'autre de leurs filles."
+
+In questo caso, la traduzione informata da ML fa un lavoro migliore del traduttore umano che mette inutilmente parole nella bocca dell'autore originale per "chiarezza".
+
+> Cosa sta succedendo qui? e perché TextBlob è così bravo a tradurre? Ebbene, dietro le quinte, utilizza Google translate, una sofisticata intelligenza artificiale in grado di analizzare milioni di frasi per prevedere le migliori stringhe per il compito da svolgere. Non c'è niente di manuale in corso qui e serve una connessione Internet per usare `blob.translate`.
+
+✅ Provare altre frasi. Qual'è migliore, ML o traduzione umana? In quali casi?
+
+## Analisi del sentiment
+
+Un'altra area in cui l'apprendimento automatico può funzionare molto bene è l'analisi del sentiment. Un approccio non ML al sentiment consiste nell'identificare parole e frasi che sono "positive" e "negative". Quindi, dato un nuovo pezzo di testo, calcolare il valore totale delle parole positive, negative e neutre per identificare il sentimento generale.
+
+Questo approccio è facilmente ingannabile come si potrebbe aver visto nel compito di Marvin: la frase `Great, that was a wonderful waste of time, I'm glad we are lost on this dark road` (Grande, è stata una meravigliosa perdita di tempo, sono contento che ci siamo persi su questa strada oscura) è una frase sarcastica e negativa, ma il semplice algoritmo rileva 'great' (grande), 'wonderful' (meraviglioso), 'glad' (contento) come positivo e 'waste' (spreco), 'lost' (perso) e 'dark' (oscuro) come negativo. Il sentimento generale è influenzato da queste parole contrastanti.
+
+✅ Si rifletta un momento su come si trasmette il sarcasmo come oratori umani. L'inflessione del tono gioca un ruolo importante. Provare a dire la frase "Beh, quel film è stato fantastico" in modi diversi per scoprire come la propria voce trasmette significato.
+
+### Approcci ML
+
+L'approccio ML sarebbe quello di raccogliere manualmente corpi di testo negativi e positivi: tweet, recensioni di film o qualsiasi cosa in cui l'essere umano abbia assegnato un punteggio *e* un parere scritto. Quindi le tecniche di NPL possono essere applicate alle opinioni e ai punteggi, in modo che emergano modelli (ad esempio, le recensioni positive di film tendono ad avere la frase "degno di un Oscar" più delle recensioni di film negative, o le recensioni positive di ristoranti dicono "gourmet" molto più di "disgustoso").
+
+> ⚖️ **Esempio**: se si è lavorato nell'ufficio di un politico e c'era qualche nuova legge in discussione, gli elettori potrebbero scrivere all'ufficio con e-mail a sostegno o e-mail contro la nuova legge specifica. Si supponga che si abbia il compito di leggere le e-mail e ordinarle in 2 pile, *pro* e *contro*. Se ci fossero molte e-mail, si potrebbe essere sopraffatti dal tentativo di leggerle tutte. Non sarebbe bello se un bot potesse leggerle tutte, capirle e dire a quale pila apparteneva ogni email?
+>
+> Un modo per raggiungere questo obiettivo è utilizzare machine learning. Si addestrerebbe il modello con una parte delle email *contro* e una parte delle email *per* . Il modello tenderebbe ad associare frasi e parole con il lato contro o il lato per, *ma non capirebbe alcun contenuto*, solo che è più probabile che alcune parole e modelli in una email appaiano in un *contro* o in un *pro*. Si potrebbe fare una prova con alcune e-mail non usate per addestrare il modello e vedere se si arriva alla stessa conclusione tratta da un umano. Quindi, una volta soddisfatti dell'accuratezza del modello, si potrebbero elaborare le email future senza doverle leggere tutte.
+
+✅ Questo processo ricorda processi usati nelle lezioni precedenti?
+
+## Esercizio - frasi sentimentali
+
+Il sentimento viene misurato con una *polarità* da -1 a 1, il che significa che -1 è il sentimento più negativo e 1 è il più positivo. Il sentimento viene anche misurato con un punteggio 0 - 1 per oggettività (0) e soggettività (1).
+
+Si dia un'altra occhiata a *Orgoglio e pregiudizio* di Jane Austen. Il testo è disponibile qui su [Project Gutenberg](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm). L'esempio seguente mostra un breve programma che analizza il sentimento della prima e dell'ultima frase del libro e ne mostra la polarità del sentimento e il punteggio di soggettività/oggettività.
+
+Si dovrebbe utilizzare la libreria `TextBlob` (descritta sopra) per determinare il `sentiment` (non si deve scrivere il proprio calcolatore del sentimento) nella seguente attività.
+
+```python
+from textblob import TextBlob
+
+quote1 = """It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."""
+
+quote2 = """Darcy, as well as Elizabeth, really loved them; and they were both ever sensible of the warmest gratitude towards the persons who, by bringing her into Derbyshire, had been the means of uniting them."""
+
+sentiment1 = TextBlob(quote1).sentiment
+sentiment2 = TextBlob(quote2).sentiment
+
+print(quote1 + " has a sentiment of " + str(sentiment1))
+print(quote2 + " has a sentiment of " + str(sentiment2))
+```
+
+Si dovrebbe ottenere il seguente risultato:
+
+```output
+It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want # of a wife. has a sentiment of Sentiment(polarity=0.20952380952380953, subjectivity=0.27142857142857146)
+
+Darcy, as well as Elizabeth, really loved them; and they were
+     both ever sensible of the warmest gratitude towards the persons
+      who, by bringing her into Derbyshire, had been the means of
+      uniting them. has a sentiment of Sentiment(polarity=0.7, subjectivity=0.8)
+```
+
+## Sfida: controllare la polarità del sentimento
+
+Il compito è determinare, usando la polarità del sentiment, se *Orgoglio e Pregiudizio* ha più frasi assolutamente positive di quelle assolutamente negative. Per questa attività, si può presumere che un punteggio di polarità di 1 o -1 sia rispettivamente assolutamente positivo o negativo.
+
+**Procedura:**
+
+1. Scaricare una [copia di Orgoglio e pregiudizio](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm) dal Progetto Gutenberg come file .txt. Rimuovere i metadati all'inizio e alla fine del file, lasciando solo il testo originale
+2. Aprire il file in Python ed estrare il contenuto come una stringa
+3. Creare un TextBlob usando la stringa del libro
+4. Analizzare ogni frase del libro in un ciclo
+   1. Se la polarità è 1 o -1, memorizzare la frase in un array o in un elenco di messaggi positivi o negativi
+5. Alla fine, stampare tutte le frasi positive e negative (separatamente) e il numero di ciascuna.
+
+Ecco una [soluzione](../solution/notebook.ipynb) di esempio.
+
+✅ Verifica delle conoscenze
+
+1. Il sentimento si basa sulle parole usate nella frase, ma il codice *comprende* le parole?
+2. Si ritiene che la polarità del sentimento sia accurata o, in altre parole, si è *d'accordo* con i punteggi?
+   1. In particolare, si è d'accordo o in disaccordo con l'assoluta polarità **positiva** delle seguenti frasi?
+      * What an excellent father you have, girls! (Che padre eccellente avete, ragazze!) said she, when the door was shut. (disse lei, non appena si chiuse la porta).
+      * “Your examination of Mr. Darcy is over, I presume,” said Miss Bingley; “and pray what is the result?” ("Il vostro esame di Mr. Darcy è finito, presumo", disse Miss Bingley; "e vi prego qual è il risultato?") “I am perfectly convinced by it that Mr. Darcy has no defect. (Sono perfettamente convinta che il signor Darcy non abbia difetti).
+      * How wonderfully these sort of things occur! (Come accadono meravigliosamente questo genere di cose!).
+      * I have the greatest dislike in the world to that sort of thing. (Ho la più grande antipatia del mondo per quel genere di cose).
+      * Charlotte is an excellent manager, I dare say (Charlotte è un'eccellente manager, oserei dire).
+      * “This is delightful indeed! (“Questo è davvero delizioso!)
+      * I am so happy! (Che gioia!)
+      * Your idea of the ponies is delightful. (La vostra idea dei pony è deliziosa).
+   2. Le successive 3 frasi sono state valutate con un sentimento assolutamente positivo, ma a una lettura attenta, non sono frasi positive. Perché l'analisi del sentiment ha pensato che fossero frasi positive?
+      * Happy shall I be, when his stay at Netherfield is over!” “I wish I could say anything to comfort you,” replied Elizabeth; “but it is wholly out of my power. (Come sarò felice, quando il suo soggiorno a Netherfield sarà finito!» "Vorrei poter dire qualcosa per consolarti", rispose Elizabeth; “ma proprio non ci riesco).
+      * If I could but see you as happy! (Se solo potessi vederti felice!)
+      * Our distress, my dear Lizzy, is very great. (La nostra angoscia, mia cara Lizzy, è devvero grande).
+   3. Sei d'accordo o in disaccordo con la polarità **negativa** assoluta delle seguenti frasi?
+      - Everybody is disgusted with his pride. (Tutti sono disgustati dal suo orgoglio).
+      - “I should like to know how he behaves among strangers.” “You shall hear then—but prepare yourself for something very dreadful. ("Vorrei sapere come si comporta in mezzo agli estranei." "Allora sentirete, ma preparatevi a qualcosa di terribile).
+      - The pause was to Elizabeth’s feelings dreadful. (La pausa fu terribile per i sentimenti di Elizabeth).
+      - It would be dreadful! (Sarebbe terribile!)
+
+✅ Qualsiasi appassionato di Jane Austen capirebbe che usa spesso i suoi libri per criticare gli aspetti più ridicoli della società inglese Regency. Elizabeth Bennett, la protagonista di *Orgoglio e pregiudizio,* è un'attenta osservatrice sociale (come l'autrice) e il suo linguaggio è spesso pesantemente sfumato. Anche Mr. Darcy (l'interesse amoroso della storia) nota l'uso giocoso e canzonatorio del linguaggio di Elizabeth: "I have had the pleasure of your acquaintance long enough to know that you find great enjoyment in occasionally professing opinions which in fact are not your own." ("Ho il piacere di conoscervi da abbastanza tempo per sapere quanto vi divertiate a esprimere di tanto in tanto delle opinioni che in realtà non vi appartengono")
+
+---
+
+## 🚀 Sfida
+
+Si può rendere Marvin ancora migliore estraendo altre funzionalità dall'input dell'utente?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Esistono molti modi per estrarre il sentiment dal testo. Si pensi alle applicazioni aziendali che potrebbero utilizzare questa tecnica. Pensare a cosa potrebbe andare storto. Ulteriori informazioni sui sistemi sofisticati pronti per l'azienda che analizzano il sentiment come l'[analisi del testo di Azure](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Provare alcune delle frasi di Orgoglio e Pregiudizio sopra e vedere se può rilevare sfumature.
+
+## Compito
+
+[Licenza poetica](assignment.it.md)
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.ko.md b/6-NLP/3-Translation-Sentiment/translations/README.ko.md
new file mode 100644
index 000000000..c9c6526e6
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/translations/README.ko.md
@@ -0,0 +1,188 @@
+# ML로 번역하고 감정 분석하기
+
+이전 강의에서 noun phrase 추출하는 기초 NLP 작업을 하기 위해 ML behind-the-scenes을 포함한 라이브러리인, `TextBlob`으로 기본적인 봇을 만드는 방식을 배웠습니다. 컴퓨터 언어학에서 다른 중요한 도전은 구두나 다른 언어로 문장을 정확하게 _translation_ 하는 것입니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
+
+번역은 천여 개 언어와 각자 많이 다른 문법 규칙이 있다는 사실에 의해서 합쳐진 매우 어려운 문제입니다. 한 접근 방식은 영어처럼, 한 언어의 형식적인 문법 규칙을 비-언어 종속 구조로 변환하고, 다른 언어로 변환하면서 번역합니다. 이 접근 방식은 다음 단계로 진행된다는 점을 의미합니다:
+
+1. **Identification**. nouns, verbs 등으로 입력하는 언어의 단어를 식별하거나 태그를 답니다.
+2. **Create translation**. 타겟 언어 포맷의 각 단어로 바로 번역합니다.
+
+### 예시 문장, 영어를 아일랜드어로
+
+'영어'에서, _I feel happy_ 문장은 순서대로 3개 단어가 이루어집니다:
+
+- **subject** (I)
+- **verb** (feel)
+- **adjective** (happy)
+
+그러나, '아일랜드' 언어에서, 같은 문장은 매우 다른 문법적인 구조를 가지고 있습니다 - "*happy*" 또는 "*sad*" 같은 감정이 *다가오는* 것으로 표현되었습니다.
+
+아일랜드어로  `Tá athas orm`인 영어 표현은 `I feel happy`입니다. *문자 그대로* 번역하면 `Happy is upon me`입니다.
+
+영어로 번역하는 아일랜드 사람은 `I feel happy`라고 말하며, `Happy is upon me`는 아니라고 합니다,
+그 이유는 단어와 문장 구조가 다르다면, 문장의 의미를 이해하는 게 달라진다고 생각했기 때문입니다.
+
+아일랜드어 문장의 형식적인 순서는 이렇습니다:
+
+- **verb** (Tá or is)
+- **adjective** (athas, or happy)
+- **subject** (orm, or upon me)
+
+## 번역
+
+전문적이지 않은 변역 프로그램은 문장 구조를 무시하고, 단어만 번역할 수 있습니다.
+
+✅ 만약 성인이 되고나서 두번째 (혹은 세 번보다 더 많은) 언어를 배웠다면, 번역문을 말할 때, 모국어로 생각하고 머리 속 개념으로 두번째 언어를 번역했을 것입니다. 전문적이지 않은 번역 프로그램이 하는 일과 유사합니다. 유창하게 하려면 이 단계를 넘기는 것이 중요합니다!
+
+전문적이지 않은 번역은 나쁘고 (그리고 때떄로 명쾌한) 잘 못된 번역으로 될 수 있습니다: 아일랜드어에서 `Mise bhraitheann athas`는 문자 그대로 `I feel happy`로 번역합니다. (문자 그대로) `me feel happy`를 의미하지만 올바른 아일랜드어 문장은 아닙니다. 영어와 아일랜드어는 섬에 가깝게 붙어서 사용하는 언어지만, 다른 문법 구조로 인해서 매우 다른 언어가 되었습니다.
+
+> [this one](https://www.youtube.com/watch?v=mRIaLSdRMMs)에서 아일랜드 언어 전통에 관련한 약간의 비디오를 시청할 수 있습니다.
+
+### 머신러닝 접근 방식
+
+아직, natural language processing 에 형식적인 룰 접근하는 방식에 대하여 배웠습니다. 다른 접근 방식은 단어의 의미를 무시하고, _대신 머신러닝으로 패턴을 감지_ 하는 방식입니다. 만약 원본과 타겟 언어 모두에 많은 텍스트 (a *corpus*) 또는 몇 텍스트 (*corpora*)를 가진다면 번역할 수 있습니다.
+
+예시로, 1813년에 Jane Austen이 쓴 잘 알려진 영어 소설, *Pride and Prejudice* 케이스를 고려해봅니다. 만약 영어로 된 책과 *French*로 되어 번역한 책을 참고해보면, _idiomatically_ 가 다르게 번역된 구문을 찾을 수 있습니다. 몇 분에 할 수 있습니다.
+
+예시로, `I have no money` 같은 영어 구문을 불어로 그대로 번역할 때, `Je n'ai pas de monnaie`로 될 수 있습니다. "Monnaie"는 'money' 와 'monnaie'가 동의어가 아니므로, 까다로운 불어 'false cognate' 입니다.  돈이 없다는 ('monnaie' 의미인 'loose change' 보다) 의미를 더 잘 전달할 수 있기 때문에, 사람이 만들 수 있는 더 좋은 번역은 `Je n'ai pas d'argent` 일 것 입니다.
+
+![monnaie](../images/monnaie.png)
+
+> Image by [Jen Looper](https://twitter.com/jenlooper)
+
+만약 ML 모델에 이를 만들 충분한 수동 번역이 되는 경우, 언어에 다 능숙한 사람이 이전에 번역한 텍스트에서 공통적인 패턴을 식별하여 번역의 정확도를 향상시킬 수 있습니다.
+
+### 연습 - 번역
+
+`TextBlob`을 사용해서 문장을 번역합니다. **Pride and Prejudice**의 유명한 첫 라인으로 시도합니다:
+
+```python
+from textblob import TextBlob
+
+blob = TextBlob(
+    "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife!"
+)
+print(blob.translate(to="fr"))
+
+```
+
+`TextBlob`은 번역 작업을 은근히 잘합니다: "C'est une vérité universellement reconnue, qu'un homme célibataire en possession d'une bonne fortune doit avoir besoin d'une femme!". 
+
+사실은, V. Leconte and Ch. Pressoir 책의 1932년 불어 번역보다, TextBlob의 번역이 더 정확하다고 주장할 수 있습니다:
+
+"C'est une vérité universelle qu'un celibataire pourvu d'une belle fortune doit avoir envie de se marier, et, si peu que l'on sache de son sentiment à cet egard, lorsqu'il arrive dans une nouvelle residence, cette idée est si bien fixée dans l'esprit de ses voisins qu'ils le considèrent sur-le-champ comme la propriété légitime de l'une ou l'autre de leurs filles."
+
+이 케이스는, 'clarity'하게 원저자가 불필요한 단어를 넣는 수작업 번역보다 ML에서 제안하는 번역이 더 좋게 작업합니다.
+
+> 어떤가요? TextBlob이 더 좋게 번역되는 이유는 무엇인가요? 음, 그 뒤에는, 수작업으로 가장 적당한 문자열을 예측한 수백만 문장을 파싱할 수 있는 정교한, AI Google 번역을 사용하고 있습니다. 여기에 수동으로 진행되지 않고, `blob.translate`를 사용하려면 인터넷 연결이 필요합니다.
+
+✅ 몇 문장을 더 시도해봅니다. ML이나 수작업 번역 중에, 어떤 게 좋나요? 어떤 케이스에서 말이죠?
+
+## 감정 분석
+
+머신러닝이 감정 분석을 더 잘 작업하는 것은 다른 영역입니다. 감정에 대한 비-ML 방식은 'positive' 와 'negative'인 단어와 구문를 식별합니다. 그러면, 새로운 텍스트의 조각이 주어졌을 때, 긍정, 부정과 중립 단어의 모든 값을 계산하여 전체적인 감정을 식별합니다.
+
+이 방식은 Marvin 작업에서 봤던 것처럼 쉽게 속았습니다 - 문장 `Great, that was a wonderful waste of time, I'm glad we are lost on this dark road` 는 빈정되고 부정적인 감정 문장이지만, 간단한 알고리즘은 긍정적으로 'great', 'wonderful', 'glad'를, 부정적으로는 'waste', 'lost'와 'dark'를 파악했습니다. 충돌되는 단어로 전체적인 감정이 흔들립니다.
+
+✅ 잠시 시간을 내어 사람이 이야기할 때 어떻게 풍자를 전달하는지 멈추고 생각해봅니다. 목소리 톤 조절하는 게 큰 룰입니다. 목소리가 의미를 어떻게 전달하는지 발견하는 다른 방식으로  "Well, that film was awesome" 구문을 말해봅니다.
+
+### ML 접근 방식
+
+ML 접근 방식은 텍스트의 부정과 긍정적인 본문을 수동으로 수집합니다 - 트윗, 또는 영화 리뷰, 또는 사람이 점수를 주고 의견을 작성할 수 있는 모든 것. NLP 기술이 의견과 점수를 적용할 수 있는 순간에, 패턴이 드러납니다 (예시. 긍정적인 영화 리뷰는 'Oscar worthy' 구문을 부정적인 영화 리뷰보다 더 많이 사용하는 경향이 있으며 긍정적인 레스토랑 리뷰는 'disgusting'보다 'gourmet'라고 더 말하는 경향이 있습니다). 
+
+> ⚖️ **Example**: 만약 정치인 사무실에서 일하고 있으며 새 법을 검토하면, 선거권자들은 새로운 법에 대한 서포팅하거나 반대하는 메일을 사무실로 보낼 수 있습니다. 이메일을 읽고  *for*와 *against* 기준으로 2 파일로 분류하는 일을 한다고 가정합니다. 만약 많은 메일을 받으면, 모든 것을 읽으려고 시도하다가 숨막힐 수 있습니다. 만약 봇이 모든 것을 읽고 이해해서 이메일이 어느 파일에 속하는지 알려줄 수 있다면, 좋을 수 있나요?
+> 
+> 하나의 방식은 머신러닝을 사용해서 이루어내는 것입니다. 이메일의 *against* 일부와 *for* 일부를 모델에 훈련합니다. 모델은 구문과 단어를 찬성과 반대 측면으로 연관하려는 경향이 있지만, *모든 컨텐츠를 이해하지 못하고*, 오직 특정 단어와 패턴이 *against* 또는 *for* 이메일에 나타날 것 같다는 것만 알게됩니다. 모델을 훈련하지 않은 몇 이메일로 테스트할 수 있으며, 같은 결말이 나왔다는 것을 볼 수 있습니다. 그렇게, 모델의 정확도를 만족하게 된다면, 각자 읽을 필요없이 이후 이메일을 처리할 수 있습니다.
+
+✅ 이 프로세스가 이전 강의에서 사용했던 것처럼 들리나요?
+
+## 연습 - 감정적인 문장
+
+감정은 -1 에서 1로 *polarity* 측정하며, 가장 부정적인 문장은 -1 으로 의미하고, 그리고 1은 가장 긍정적입니다. 감정은 objectivity (0) 와 subjectivity (1)를 0 - 1 점으로 측정하기도 합니다.
+
+Jane Austen의 *Pride and Prejudice*를 다르게 봅니다. 텍스트는 [Project Gutenberg](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm)에서 존재합니다. 샘플은 책 처음과 마지막 문장의 감정을 분석하고 감정 polarity와 subjectivity/objectivity 점수를 출력하는 짧은 프로그램이며 다음과 같이 보여집니다.
+
+다음 작업에서 (설명한) `TextBlob` 라이브러리로 `sentiment`를 (감정 계산기를 작성하지 않아도) 탐지합니다.
+
+```python
+from textblob import TextBlob
+
+quote1 = """It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."""
+
+quote2 = """Darcy, as well as Elizabeth, really loved them; and they were both ever sensible of the warmest gratitude towards the persons who, by bringing her into Derbyshire, had been the means of uniting them."""
+
+sentiment1 = TextBlob(quote1).sentiment
+sentiment2 = TextBlob(quote2).sentiment
+
+print(quote1 + " has a sentiment of " + str(sentiment1))
+print(quote2 + " has a sentiment of " + str(sentiment2))
+```
+
+다음 출력을 보게 됩니다:
+
+```output
+It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want # of a wife. has a sentiment of Sentiment(polarity=0.20952380952380953, subjectivity=0.27142857142857146)
+
+Darcy, as well as Elizabeth, really loved them; and they were
+     both ever sensible of the warmest gratitude towards the persons
+      who, by bringing her into Derbyshire, had been the means of
+      uniting them. has a sentiment of Sentiment(polarity=0.7, subjectivity=0.8)
+```
+
+## 도전 - 감정 polarity 확인
+
+만약 *Pride and Prejudice*가 절대적 부정적인 내용보다 긍정적인 문장이 더 있다면, 작업은 감정 polarity로 탐지합니다. 이 작업에서, 1 or -1 polarity 점수가 각자 절대적으로 positive 하거나 negative 하다고 생각할 수 있습니다.
+
+**단계:**
+
+1. .txt 파일로 이루어진 Project Gutenberg의 [copy of Pride and Prejudice](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm)를 내려받습니다. 파일의 시작과 끝에 있는 메타데이터를 제거해서, 원본 텍스트만 남깁니다
+2. Python으로 파일을 열고 문자열로 컨텐츠를 풉니다 
+3. 책의 문자열로 TextBlob을 만듭니다
+4. 책의 각 문장을 반복해서 분석합니다
+   1. 만약 polarity가 1 또는 -1이면 문장을 배열이나 positive 또는 negative 메시지 리스트에 저장합니다
+5. 마지막으로, (각자) 모든 긍정적인 문장과 부정적인 문장, 각 수를 출력합니다
+
+여기에 샘플 [solution](../solution/notebook.ipynb)이 있습니다.
+
+✅ 지식 점검
+
+1. 감정은 문장에서 사용된 단어를 기반하지만, 코드는 단어를 *이해하나요*? 
+2. 감정 polarity가 정확하다고 생각하거나, 다시 말하면, 점수에 *동의* 하나요?
+   1. 특히나, 다음 문장이 절대적으로 **긍정** polarity라는 점을 동의하거나 거부하나요?
+      * “What an excellent father you have, girls!” said she, when the door was shut.
+      * “Your examination of Mr. Darcy is over, I presume,” said Miss Bingley; “and pray what is the result?” “I am perfectly convinced by it that Mr. Darcy has no defect.
+      * How wonderfully these sort of things occur!
+      * I have the greatest dislike in the world to that sort of thing.
+      * Charlotte is an excellent manager, I dare say.
+      * “This is delightful indeed!
+      * I am so happy!
+      * Your idea of the ponies is delightful.
+   2. 다음 3개 문장은 절대적으로 긍정 문장이지만, 자세히 읽으면, 긍정 문장이 아닙니다. 왜 감정이 긍정 문장으로 생각하도록 분석되었나요?
+      * Happy shall I be, when his stay at Netherfield is over!” “I wish I could say anything to comfort you,” replied Elizabeth; “but it is wholly out of my power.
+      * If I could but see you as happy!
+      * Our distress, my dear Lizzy, is very great.
+   3. 다음 문장을 절대적인 **부정** polarity로 동의하거나 거부하나요?
+      - Everybody is disgusted with his pride.
+      - “I should like to know how he behaves among strangers.” “You shall hear then—but prepare yourself for something very dreadful.
+      - The pause was to Elizabeth’s feelings dreadful.
+      - It would be dreadful!
+
+✅ 모든 Jane Austen의 팬은 자주 자신의 책으로 English Regency 사회의 말도 안되는 측면을 비판하는 것을 이해합니다. *Pride and Prejudice*의 주요 캐릭터인 Elizabeth Bennett은, (작성자 같은) 예민한 소셜 옵저버이며 그녀의 언어는 가끔 뉘앙스가 미묘합니다. 심지어 Mr. Darcy (이야기에 흥미로운 것을 좋아하는 자)도 Elizabeth의 장난스럽고 놀리는 언어를 주목합니다: "I have had the pleasure of your acquaintance long enough to know that you find great enjoyment in occasionally professing opinions which in fact are not your own."
+
+---
+
+## 🚀 도전
+
+사용자 입력으로 다른 features를 추출해서 Marvin을 더 좋게 만들 수 있나요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
+
+## 검토 & 자기주도 학습
+
+텍스트에서 감정을 추출하는 많은 방식이 있습니다. 이 기술로 사용할 수 있는 비지니스 애플리케이션을 생각해봅니다. 어떻게 틀릴 수 있는지도 생각해봅니다. [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa) 같이 감정 분석을 하는 정교한 enterprise-ready 시스템에 대하여 읽어봅니다. Pride and Prejudice 일부 문장에서 미묘한 차이를 감지할 수 있는지 테스트 합니다.
+
+## 과제 
+
+[Poetic license](../assignment.md)
diff --git a/6-NLP/3-Translation-Sentiment/translations/assignment.it.md b/6-NLP/3-Translation-Sentiment/translations/assignment.it.md
new file mode 100644
index 000000000..1b786ba26
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Licenza poetica
+
+## Istruzioni
+
+In [questo notebook](https://www.kaggle.com/jenlooper/emily-dickinson-word-frequency) si possono trovare oltre 500 poesie di Emily Dickinson precedentemente analizzate per il sentiment utilizzando l'analisi del testo di Azure. Utilizzando questo insieme di dati, analizzarlo utilizzando le tecniche descritte nella lezione. Il sentimento suggerito di una poesia corrisponde alla decisione più sofisticata del servizio Azure? Perché o perché no, secondo il proprio parere? C’è qualcosa che sorprende?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | -------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------ |
+|          | Un notebook viene presentato con una solida analisi del risultato da un campione di un autore | Il notebook è incompleto o non esegue l'analisi | Nessun notebook presentato |
diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md
index a4b5a556f..dd6a573a9 100644
--- a/6-NLP/4-Hotel-Reviews-1/README.md
+++ b/6-NLP/4-Hotel-Reviews-1/README.md
@@ -6,7 +6,7 @@ In this section you will use the techniques in the previous lessons to do some e
 - how to calculate some new data based on the existing columns
 - how to save the resulting dataset for use in the final challenge
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
 
 ### Introduction
 
@@ -393,7 +393,7 @@ Now that you have explored the dataset, in the next lesson you will filter the d
 
 This lesson demonstrates, as we saw in previous lessons, how critically important it is to understand your data and its foibles before performing operations on it. Text-based data, in particular, bears careful scrutiny. Dig through various text-heavy datasets and see if you can discover areas that could introduce bias or skewed sentiment into a model. 
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/6-NLP/4-Hotel-Reviews-1/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
new file mode 100644
index 000000000..10622683a
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
@@ -0,0 +1,412 @@
+# Analisi del sentiment con le recensioni degli hotel - elaborazione dei dati
+
+In questa sezione si utilizzeranno le tecniche delle lezioni precedenti per eseguire alcune analisi esplorative dei dati di un grande insieme di dati. Una volta compresa bene l'utilità delle varie colonne, si imparerà:
+
+- come rimuovere le colonne non necessarie
+- come calcolare alcuni nuovi dati in base alle colonne esistenti
+- come salvare l'insieme di dati risultante per l'uso nella sfida finale
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/?loc=it)
+
+### Introduzione
+
+Finora si è appreso come i dati di testo siano abbastanza diversi dai tipi di dati numerici. Se è un testo scritto o parlato da un essere umano, può essere analizzato per trovare schemi e frequenze, sentiment e significati. Questa lezione entra in un vero insieme di dati con una vera sfida: **[515K dati di recensioni di hotel in Europa](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe)** e include una [licenza CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). È stato ricavato da Booking.com da fonti pubbliche. Il creatore dell'insieme di dati è stato Jiashen Liu.
+
+### Preparazione
+
+Ecco l'occorrente:
+
+* La possibilità di eseguire notebook .ipynb utilizzando Python 3
+* pandas
+* NLTK, [che si dovrebbe installare localmente](https://www.nltk.org/install.html)
+* L'insieme di dati disponibile su Kaggle [515K Hotel Reviews Data in Europe](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe). Sono circa 230 MB decompressi. Scaricarlo nella cartella radice `/data` associata a queste lezioni di NLP.
+
+## Analisi esplorativa dei dati
+
+Questa sfida presuppone che si stia creando un bot di raccomandazione di hotel utilizzando l'analisi del sentiment e i punteggi delle recensioni degli ospiti. L'insieme di dati da utilizzare include recensioni di 1493 hotel diversi in 6 città.
+
+Utilizzando Python, un insieme di dati di recensioni di hotel e l'analisi del sentiment di NLTK si potrebbe scoprire:
+
+* quali sono le parole e le frasi più usate nelle recensioni?
+* i *tag* ufficiali che descrivono un hotel correlato con punteggi di recensione (ad es. sono le più negative per un particolare hotel per *famiglia con bambini piccoli* rispetto a *viaggiatore singolo*, forse indicando che è meglio per i *viaggiatori sinogli*?)
+* i punteggi del sentiment NLTK "concordano" con il punteggio numerico del recensore dell'hotel?
+
+#### Insieme di dati
+
+Per esplorare l'insieme di dati scaricato e salvato localmente, aprire il file in un editor tipo VS Code o anche Excel.
+
+Le intestazioni nell'insieme di dati sono le seguenti:
+
+*Hotel_Address, Additional_Number_of_Scoring, Review_Date, Average_Score, Hotel_Name, Reviewer_Nationality, Negative_Review, Review_Total_Negative_Word_Counts, Total_Number_of_Reviews, Positive_Review, Review_Total_Positive_Word_Counts, Total_Number_of_Reviews_Reviewer_Has_Given, Reviewer_Score, Tags, days_since_review, lat, lng*
+
+Qui sono raggruppati in un modo che potrebbe essere più facile da esaminare:
+
+##### Colonne Hotel
+
+* `Hotel_Name` (nome hotel), `Hotel_Address` (indirizzo hotel), `lat` (latitudine)`,` lng (longitudine)
+   * Usando *lat* e *lng* si può tracciare una mappa con Python che mostra le posizioni degli hotel (forse codificate a colori per recensioni negative e positive)
+   * Hotel_Address non è ovviamente utile e probabilmente verrà sostituito con una nazione per semplificare l'ordinamento e la ricerca
+
+**Colonne di meta-recensione dell'hotel**
+
+* `Average_Score` (Punteggio medio)
+   * Secondo il creatore dell'insieme di dati, questa colonna è il *punteggio medio dell'hotel, calcolato in base all'ultimo commento dell'ultimo anno*. Questo sembra un modo insolito per calcolare il punteggio, ma sono i dati recuperati, quindi per ora si potrebbero prendere come valore nominale.
+
+   ✅ Sulla base delle altre colonne di questi dati, si riesce a pensare a un altro modo per calcolare il punteggio medio?
+
+* `Total_Number_of_Reviews` (Numero totale di recensioni)
+   * Il numero totale di recensioni ricevute da questo hotel - non è chiaro (senza scrivere del codice) se si riferisce alle recensioni nell'insieme di dati.
+* `Additional_Number_of_Scoring` (Numero aggiuntivo di punteggio
+   * Ciò significa che è stato assegnato un punteggio di recensione ma nessuna recensione positiva o negativa è stata scritta dal recensore
+
+**Colonne di recensione**
+
+- `Reviewer_Score` (Punteggio recensore)
+   - Questo è un valore numerico con al massimo 1 cifra decimale tra i valori minimo e massimo 2,5 e 10
+   - Non è spiegato perché 2,5 sia il punteggio più basso possibile
+- `Negative_Review` (Recensione Negativa)
+   - Se un recensore non ha scritto nulla, questo campo avrà "**No Negative" (Nessun negativo)**
+   - Si tenga presente che un recensore può scrivere una recensione positiva nella colonna delle recensioni negative (ad es. "non c'è niente di negativo in questo hotel")
+- `Review_Total_Negative_Word_Counts` (Conteggio parole negative totali per revisione)
+   - Conteggi di parole negative più alti indicano un punteggio più basso (senza controllare il sentiment)
+- `Positive_Review` (Recensioni positive)
+   - Se un recensore non ha scritto nulla, questo campo avrà "**No Positive" (Nessun positivo)**
+   - Si tenga presente che un recensore può scrivere una recensione negativa nella colonna delle recensioni positive (ad es. "non c'è niente di buono in questo hotel")
+- `Review_Total_Positive_Word_Counts` (Conteggio parole positive totali per revisione)
+   - Conteggi di parole positive più alti indicano un punteggio più alto (senza controllare il sentiment)
+- `Review_Date` e `days_since_review` (Data revisione e giorni trascorsi dalla revisione)
+   - Una misura di freschezza od obsolescenza potrebbe essere applicata a una recensione (le recensioni più vecchie potrebbero non essere accurate quanto quelle più recenti perché la gestione dell'hotel è cambiata, o sono stati effettuati lavori di ristrutturazione, o è stata aggiunta una piscina, ecc.)
+- `Tag`
+   - Questi sono brevi descrittori che un recensore può selezionare per descrivere il tipo di ospite in cui rientra (ad es. da soli o in famiglia), il tipo di camera che aveva, la durata del soggiorno e come è stata inviata la recensione.
+   - Sfortunatamente, l'uso di questi tag è problematico, controllare la sezione sottostante che discute la loro utilità
+
+**Colonne dei recensori**
+
+- `Total_Number_of_Reviews_Reviewer_Has_Given` (Numero totale di revisioni per recensore)
+   - Questo potrebbe essere un fattore in un modello di raccomandazione, ad esempio, se si potesse determinare che i recensori più prolifici con centinaia di recensioni avevano maggiori probabilità di essere negativi piuttosto che positivi. Tuttavia, il recensore di una particolare recensione non è identificato con un codice univoco e quindi non può essere collegato a un insieme di recensioni. Ci sono 30 recensori con 100 o più recensioni, ma è difficile vedere come questo possa aiutare il modello di raccomandazione.
+- `Reviewer_Nationality` (Nazionalità recensore)
+   - Alcune persone potrebbero pensare che alcune nazionalità abbiano maggiore propensione a dare una recensione positiva o negativa a causa di un'inclinazione nazionalista. Fare attenzione a creare tali punti di vista aneddotici nei propri modelli. Questi sono stereotipi nazionalisti (e talvolta razziali) e ogni recensore era un individuo che ha scritto una recensione in base alla propria esperienza. Potrebbe essere stata filtrata attraverso molte lenti come i loro precedenti soggiorni in hotel, la distanza percorsa e la loro indole. Pensare che la loro nazionalità sia stata la ragione per un punteggio di recensione è difficile da giustificare.
+
+##### Esempi
+
+| Average  Score | Total Number   Reviews | Reviewer   Score | Negative <br />Review                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | Positive   Review                 | Tags                                                                                      |
+| -------------- | ---------------------- | ---------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------- |
+| 7.8            | 1945                   | 2.5              | This is  currently not a hotel but a construction site I was terroized from early  morning and all day with unacceptable building noise while resting after a  long trip and working in the room People were working all day i e with  jackhammers in the adjacent rooms I asked for a room change but no silent  room was available To make thinks worse I was overcharged I checked out in  the evening since I had to leave very early flight and received an appropiate  bill A day later the hotel made another charge without my concent in excess  of booked price It s a terrible place Don t punish yourself by booking  here | Nothing  Terrible place Stay away | Business trip                                Couple Standard Double  Room Stayed 2 nights |
+
+Come si può vedere, questo ospite non ha trascorso un soggiorno felice in questo hotel. L'hotel ha un buon punteggio medio di 7,8 e 1945 recensioni, ma questo recensore ha dato 2,5 e ha scritto 115 parole su quanto sia stato negativo il suo soggiorno. Se non è stato scritto nulla nella colonna Positive_Review, si potrebbe supporre che non ci sia stato nulla di positivo, ma purtroppo sono state scritte 7 parole di avvertimento. Se si contassero solo le parole invece del significato o del sentiment delle parole, si potrebbe avere una visione distorta dell'intento dei recensori. Stranamente, il punteggio di 2,5 è fonte di confusione, perché se il soggiorno in hotel è stato così negativo, perché dargli dei punti? Indagando da vicino l'insieme di dati, si vedrà che il punteggio più basso possibile è 2,5, non 0. Il punteggio più alto possibile è 10.
+
+##### Tag
+
+Come accennato in precedenza, a prima vista, l'idea di utilizzare i `tag` per classificare i dati ha senso. Sfortunatamente questi tag non sono standardizzati, il che significa che in un determinato hotel le opzioni potrebbero essere *Camera singola* , *Camera a due letti* e *Camera doppia*, ma nell'hotel successivo potrebbero essere *Camera singola deluxe*, *Camera matrimoniale classica* e *Camera king executive*. Potrebbero essere le stesse cose, ma ci sono così tante varianti che la scelta diventa:
+
+1. Tentare di modificare tutti i termini in un unico standard, il che è molto difficile, perché non è chiaro quale sarebbe il percorso di conversione in ciascun caso (ad es. *Camera singola classica* mappata in *camera singola*, ma *camera Queen Superior con cortile giardino o vista città* è molto più difficile da mappare)
+
+1. Si può adottare un approccio NLP e misurare la frequenza di determinati termini come *Solo*, *Business Traveller* (Viaggiatore d'affari) o *Family with young kids* (Famiglia con bambini piccoli) quando si applicano a ciascun hotel e tenerne conto nella raccomandazione
+
+I tag sono in genere (ma non sempre) un singolo campo contenente un elenco di valori separati da 5 a 6 virgole allineati per *Type of trip* (Tipo di viaggio), *Type of guests* (Tipo di ospiti), *Type of room* (Tipo di camera), *Number of nights* (Numero di notti) e *Type of device* (Tipo di dispositivo con il quale è stata inviata la recensione).Tuttavia, poiché alcuni recensori non compilano ogni campo (potrebbero lasciarne uno vuoto), i valori non sono sempre nello stesso ordine.
+
+Ad esempio, si prenda *Type of group*  (Tipo di gruppo). Ci sono 1025 possibilità uniche in questo campo nella colonna `Tag`, e purtroppo solo alcune di esse fanno riferimento a un gruppo (alcune sono il tipo di stanza ecc.). Se si filtra solo quelli che menzionano la famiglia, saranno ricavati molti risultati relativi al tipo di *Family room* . Se si include il termine *with* (con), cioè si conta i valori per *Family with*, i risultati sono migliori, con oltre 80.000 dei 515.000 risultati che contengono la frase "Family with young children" (Famiglia con figli piccoli) o "Family with older children" (Famiglia con figli grandi).
+
+Ciò significa che la colonna dei tag non è completamente inutile allo scopo, ma richiederà del lavoro per renderla utile.
+
+##### Punteggio medio dell'hotel
+
+Ci sono una serie di stranezze o discrepanze con l'insieme di dati che non si riesce a capire, ma sono illustrate qui in modo che ci siano note quando si costruiscono i propri modelli. Se ci si capisce qualcosa, renderlo noto nella sezione discussione!
+
+L'insieme di dati ha le seguenti colonne relative al punteggio medio e al numero di recensioni:
+
+1. Hotel_Name (Nome Hotel)
+2. Additional_Number_of_Scoring (Numero aggiuntivo di punteggio
+3. Average_Score (Punteggio medio)
+4. Total_Number_of_Reviews (Numero totale di recensioni)
+5. Reviewer_Score (Punteggio recensore)
+
+L'hotel con il maggior numero di recensioni in questo insieme di dati è *il Britannia International Hotel Canary Wharf* con 4789 recensioni su 515.000. Ma se si guarda al valore `Total_Number_of_Reviews` per questo hotel, è 9086. Si potrebbe supporre che ci siano molti più punteggi senza recensioni, quindi forse si dovrebbe aggiungere il valore della colonna `Additional_Number_of_Scoring` . Quel valore è 2682 e aggiungendolo a 4789 si ottiene 7.471 che è ancora 1615 in meno del valore di `Total_Number_of_Reviews`.
+
+Se si prende la colonna `Average_Score`, si potrebbe supporre che sia la media delle recensioni nell'insieme di dati, ma la descrizione di Kaggle è "*Punteggio medio dell’hotel, calcolato in base all’ultimo commento nell’ultimo anno*". Non sembra così utile, ma si può calcolare la media in base ai punteggi delle recensioni nell'insieme di dati. Utilizzando lo stesso hotel come esempio, il punteggio medio dell'hotel è 7,1 ma il punteggio calcolato (punteggio medio del recensore nell'insieme di dati) è 6,8. Questo è vicino, ma non lo stesso valore, e si può solo supporre che i punteggi dati nelle recensioni `Additional_Number_of_Scoring` hanno aumentato la media a 7,1. Sfortunatamente, senza alcun modo per testare o dimostrare tale affermazione, è difficile utilizzare o fidarsi di `Average_Score`, `Additional_Number_of_Scoring` e `Total_Number_of_Reviews` quando si basano su o fanno riferimento a dati che non sono presenti.
+
+Per complicare ulteriormente le cose, l'hotel con il secondo numero più alto di recensioni ha un punteggio medio calcolato di 8,12 e l'insieme di dati `Average_Score` è 8,1. Questo punteggio corretto è una coincidenza o il primo hotel è una discrepanza?
+
+Sulla possibilità che questi hotel possano essere un valore anomalo e che forse la maggior parte dei valori coincidano (ma alcuni non lo fanno per qualche motivo)  si scriverà un breve programma per esplorare i valori nell'insieme di dati e determinare l'utilizzo corretto (o mancato utilizzo) dei valori.
+
+> 🚨 Una nota di cautela
+>
+> Quando si lavora con questo insieme di dati, si scriverà un codice che calcola qualcosa dal testo senza dover leggere o analizzare il testo da soli. Questa è l'essenza di NLP, interpretare il significato o il sentiment senza che lo faccia un essere umano. Tuttavia, è possibile che si leggano alcune delle recensioni negative. Non è raccomandabile farlo. Alcune di esse sono recensioni negative sciocche o irrilevanti, come "Il tempo non era eccezionale", qualcosa al di fuori del controllo dell'hotel, o di chiunque, in effetti. Ma c'è anche un lato oscuro in alcune recensioni. A volte le recensioni negative sono razziste, sessiste o antietà. Questo è un peccato, ma è prevedibile in un insieme di dati recuperato da un sito web pubblico. Alcuni recensori lasciano recensioni che si potrebbe trovare sgradevoli, scomode o sconvolgenti. Meglio lasciare che il codice misuri il sentiment piuttosto che leggerle da soli e arrabbiarsi. Detto questo, è una minoranza che scrive queste cose, ma esistono lo stesso.
+
+## Esercizio - Esplorazione dei dati
+
+### Caricare i dati
+
+Per ora l'esame visivo dei dati è sufficiente, adesso si scriverà del codice e si otterranno alcune risposte! Questa sezione utilizza la libreria pandas. Il primo compito è assicurarsi di poter caricare e leggere i dati CSV. La libreria pandas ha un veloce caricatore CSV e il risultato viene inserito in un dataframe, come nelle lezioni precedenti. Il CSV che si sta caricando ha oltre mezzo milione di righe, ma solo 17 colonne. Pandas offre molti modi potenti per interagire con un dataframe, inclusa la possibilità di eseguire operazioni su ogni riga.
+
+Da qui in poi in questa lezione, ci saranno frammenti di codice e alcune spiegazioni del codice e alcune discussioni su cosa significano i risultati. Usare il _notebook.ipynb_ incluso per il proprio codice.
+
+Si inizia con il caricamento del file di dati da utilizzare:
+
+```python
+# Carica il CSV con le recensioni degli hotel
+import pandas as pd
+import time
+# importa time per determinare orario di inizio e fine caricamento per poterne calcolare la durata
+print("Loading data file now, this could take a while depending on file size")
+start = time.time()
+# df è un 'DataFrame' - assicurarsi di aver scaricato il file nella cartelle data
+df = pd.read_csv('../../data/Hotel_Reviews.csv')
+end = time.time()
+print("Loading took " + str(round(end - start, 2)) + " seconds")
+```
+
+Ora che i dati sono stati caricati, si possono eseguire alcune operazioni su di essi. Tenere questo codice nella parte superiore del programma per la parte successiva.
+
+## Esplorare i dati
+
+In questo caso, i dati sono già *puliti*, il che significa che sono pronti per essere lavorati e non ci sono caratteri in altre lingue che potrebbero far scattare algoritmi che si aspettano solo caratteri inglesi.
+
+✅ Potrebbe essere necessario lavorare con dati che richiedono un'elaborazione iniziale per formattarli prima di applicare le tecniche di NLP, ma non questa volta. Se si dovesse, come si gestirebero i caratteri non inglesi?
+
+Si prenda un momento per assicurarsi che una volta caricati, i dati possano essere esplorarati con il codice. È molto facile volersi concentrare sulle colonne `Negative_Review` e `Positive_Review` . Sono pieni di testo naturale per essere elaborato dagli algoritmi di NLP. Ma non è finita qui! Prima di entrare nel NLP e nel sentiment, si dovrebbe seguire il codice seguente per accertarsi se i valori forniti nell'insieme di dati corrispondono ai valori calcolati con pandas.
+
+## Operazioni con dataframe
+
+Il primo compito di questa lezione è verificare se le seguenti asserzioni sono corrette scrivendo del codice che esamini il dataframe (senza modificarlo).
+
+> Come molte attività di programmazione, ci sono diversi modi per completarla, ma un buon consiglio è farlo nel modo più semplice e facile possibile, soprattutto se sarà più facile da capire quando si riesaminerà questo codice in futuro. Con i dataframe, esiste un'API completa che spesso avrà un modo per fare ciò che serve in modo efficiente.
+
+Trattare le seguenti domande come attività di codifica e provare a rispondere senza guardare la soluzione.
+
+1. Stampare la *forma* del dataframe appena caricato (la forma è il numero di righe e colonne)
+2. Calcolare il conteggio della frequenza per le nazionalità dei recensori:
+   1. Quanti valori distinti ci sono per la colonna `Reviewer_Nationality` e quali sono?
+   2. Quale nazionalità del recensore è la più comune nell'insieme di dati (stampare nazione e numero di recensioni)?
+   3. Quali sono le prossime 10 nazionalità più frequenti e la loro frequenza?
+3. Qual è stato l'hotel più recensito per ciascuna delle 10 nazionalità più recensite?
+4. Quante recensioni ci sono per hotel (conteggio della frequenza dell'hotel) nell'insieme di dati?
+5. Sebbene sia presente una colonna `Average_Score` per ogni hotel nell'insieme di dati, si può anche calcolare un punteggio medio (ottenendo la media di tutti i punteggi dei recensori nell'insieme di dati per ogni hotel). Aggiungere una nuova colonna al dataframe con l'intestazione della colonna `Calc_Average_Score` che contiene quella media calcolata.
+6. Ci sono hotel che hanno lo stesso (arrotondato a 1 decimale) `Average_Score` e `Calc_Average_Score`?
+   1. Provare a scrivere una funzione Python che accetta una serie (riga) come argomento e confronta i valori, stampando un messaggio quando i valori non sono uguali. Quindi usare il metodo `.apply()` per elaborare ogni riga con la funzione.
+7. Calcolare e stampare quante righe contengono valori di "No Negative" nella colonna `Negative_Review` "
+8. Calcolare e stampare quante righe contengono valori di "No Positive" nella colonna `Positive_Review` "
+9. Calcolare e stampare quante righe contengono valori di "No Positive" nella colonna `Positive_Review` " **e** valori di "No Negative" nella colonna `Negative_Review`
+
+### Risposte
+
+1. Stampare la *forma* del dataframei appena caricato (la forma è il numero di righe e colonne)
+
+   ```python
+   print("The shape of the data (rows, cols) is " + str(df.shape))
+   > The shape of the data (rows, cols) is (515738, 17)
+   ```
+
+2. Calcolare il conteggio della frequenza per le nazionalità dei recensori:
+
+   1. Quanti valori distinti ci sono per la colonna `Reviewer_Nationality` e quali sono?
+   2. Quale nazionalità del recensore è la più comune nell'insieme di dati (paese di stampa e numero di recensioni)?
+
+   ```python
+   # value_counts() crea un oggetto Series con indice e valori, in questo caso la nazione 
+   # e la frequenza con la quale si manifestano nella nazionalità del recensore
+   nationality_freq = df["Reviewer_Nationality"].value_counts()
+   print("There are " + str(nationality_freq.size) + " different nationalities")
+   # stampa la prima e ultima riga della Series. Modificare in nationality_freq.to_string() per stampare tutti i dati
+   print(nationality_freq)
+
+   There are 227 different nationalities
+    United Kingdom               245246
+    United States of America      35437
+    Australia                     21686
+    Ireland                       14827
+    United Arab Emirates          10235
+                                  ...  
+    Comoros                           1
+    Palau                             1
+    Northern Mariana Islands          1
+    Cape Verde                        1
+    Guinea                            1
+   Name: Reviewer_Nationality, Length: 227, dtype: int64
+   ```
+
+   3. Quali sono le prossime 10 nazionalità più frequenti e la loro frequenza?
+
+      ```python
+      print("The highest frequency reviewer nationality is " + str(nationality_freq.index[0]).strip() + " with " + str(nationality_freq[0]) + " reviews.")
+      # Notare che c'è uno spazio davanti ai valori, strip() lo rimuove per la stampa
+      # Quale sono le 10 nazionalità più comuni e la loro frequenza?
+      print("The next 10 highest frequency reviewer nationalities are:")
+      print(nationality_freq[1:11].to_string())
+
+      The highest frequency reviewer nationality is United Kingdom with 245246 reviews.
+      The next 10 highest frequency reviewer nationalities are:
+       United States of America     35437
+       Australia                    21686
+       Ireland                      14827
+       United Arab Emirates         10235
+       Saudi Arabia                  8951
+       Netherlands                   8772
+       Switzerland                   8678
+       Germany                       7941
+       Canada                        7894
+       France                        7296
+      ```
+
+3. Qual è stato l'hotel più recensito per ciascuna delle 10 nazionalità più recensite?
+
+   ```python
+   # Qual è stato l'hotel più recensito per ciascuna delle 10 nazionalità più recensite
+   # In genere con pandas si cerca di evitare un ciclo esplicito, ma si vuole mostrare come si crea un
+   # nuovo dataframe usando criteri (non fare questo con un grande volume di dati in quanto potrebbe essere molto lento)
+   for nat in nationality_freq[:10].index:
+      # Per prima cosa estrarre tutte le righe che corrispondono al criterio in un nuovo dataframe
+      nat_df = df[df["Reviewer_Nationality"] == nat]   
+      # Ora ottenere la frequenza per l'hotel
+      freq = nat_df["Hotel_Name"].value_counts()
+      print("The most reviewed hotel for " + str(nat).strip() + " was " + str(freq.index[0]) + " with " + str(freq[0]) + " reviews.")
+
+   The most reviewed hotel for United Kingdom was Britannia International Hotel Canary Wharf with 3833 reviews.
+   The most reviewed hotel for United States of America was Hotel Esther a with 423 reviews.
+   The most reviewed hotel for Australia was Park Plaza Westminster Bridge London with 167 reviews.
+   The most reviewed hotel for Ireland was Copthorne Tara Hotel London Kensington with 239 reviews.
+   The most reviewed hotel for United Arab Emirates was Millennium Hotel London Knightsbridge with 129 reviews.
+   The most reviewed hotel for Saudi Arabia was The Cumberland A Guoman Hotel with 142 reviews.
+   The most reviewed hotel for Netherlands was Jaz Amsterdam with 97 reviews.
+   The most reviewed hotel for Switzerland was Hotel Da Vinci with 97 reviews.
+   The most reviewed hotel for Germany was Hotel Da Vinci with 86 reviews.
+   The most reviewed hotel for Canada was St James Court A Taj Hotel London with 61 reviews.
+   ```
+
+4. Quante recensioni ci sono per hotel (conteggio della frequenza dell'hotel) nell'insieme di dati?
+
+   ```python
+   # Per prima cosa creare un nuovo dataframe in base a quello vecchio, togliendo le colonne non necessarie
+   hotel_freq_df = df.drop(["Hotel_Address", "Additional_Number_of_Scoring", "Review_Date", "Average_Score", "Reviewer_Nationality", "Negative_Review", "Review_Total_Negative_Word_Counts", "Positive_Review", "Review_Total_Positive_Word_Counts", "Total_Number_of_Reviews_Reviewer_Has_Given", "Reviewer_Score", "Tags", "days_since_review", "lat", "lng"], axis = 1)
+
+   # Raggruppre le righe per Hotel_Name, conteggiarle e inserire il risultato in una nuova colonna Total_Reviews_Found
+   hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')
+
+   # Eliminare tutte le righe duplicate
+   hotel_freq_df = hotel_freq_df.drop_duplicates(subset = ["Hotel_Name"])
+   display(hotel_freq_df)
+   ```
+   
+   |          Hotel_Name (Nome Hotel)           | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found |
+   | :----------------------------------------: | :---------------------------------------------------: | :-----------------: |
+   | Britannia International Hotel Canary Wharf |                         9086                          |        4789         |
+   |    Park Plaza Westminster Bridge Londra    |                         12158                         |        4169         |
+   |   Copthorne Tara Hotel London Kensington   |                         7105                          |        3578         |
+   |                    ...                     |                          ...                          |         ...         |
+   |       Mercure Paris Porte d'Orléans        |                          110                          |         10          |
+   |                Hotel Wagner                |                          135                          |         10          |
+   |            Hotel Gallitzinberg             |                          173                          |          8          |
+   
+
+   Si potrebbe notare che il *conteggio nell'insieme di dati* non corrisponde al valore in `Total_Number_of_Reviews`. Non è chiaro se questo valore nell'insieme di dati rappresentasse il numero totale di recensioni che l'hotel aveva, ma non tutte sono state recuperate o qualche altro calcolo. `Total_Number_of_Reviews` non viene utilizzato nel modello a causa di questa non chiarezza.
+
+5. Sebbene sia presente una colonna `Average_Score` per ogni hotel nell'insieme di dati, si può anche calcolare un punteggio medio (ottenendo la media di tutti i punteggi dei recensori nell'insieme di dati per ogni hotel). Aggiungere una nuova colonna al dataframe con l'intestazione della colonna `Calc_Average_Score` che contiene quella media calcolata. Stampare le colonne `Hotel_Name`, `Average_Score` e `Calc_Average_Score`.
+
+   ```python
+   # definisce una funzione che ottiene una riga ed esegue alcuni calcoli su di essa
+   def get_difference_review_avg(row):
+     return row["Average_Score"] - row["Calc_Average_Score"]
+
+   # 'mean' è la definizione matematica per 'average'
+   df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
+
+   # Aggiunge una nuova colonna con la differenza tra le due medie di punteggio
+   df["Average_Score_Difference"] = df.apply(get_difference_review_avg, axis = 1)
+
+   # Crea un df senza tutti i duplicati di Hotel_Name (quindi una sola riga per hotel)
+   review_scores_df = df.drop_duplicates(subset = ["Hotel_Name"])
+
+   # Ordina il dataframe per trovare la differnza più bassa e più alta per il punteggio medio
+   review_scores_df = review_scores_df.sort_values(by=["Average_Score_Difference"])
+
+   display(review_scores_df[["Average_Score_Difference", "Average_Score", "Calc_Average_Score", "Hotel_Name"]])
+   ```
+
+   Ci si potrebbe anche chiedere del valore `Average_Score` e perché a volte è diverso dal punteggio medio calcolato. Poiché non è possibile sapere perché alcuni valori corrispondano, ma altri hanno una differenza, in questo caso è più sicuro utilizzare i punteggi delle recensioni a disposizione per calcolare autonomamente la media. Detto questo, le differenze sono solitamente molto piccole, ecco gli hotel con la maggiore deviazione dalla media dell'insieme di dati e dalla media calcolata:
+
+   | Average_Score_Difference | Average_Score | Calc_Average_Score |                      Hotel_Name (Nome Hotel) |
+   | :----------------------: | :-----------: | :----------------: | -------------------------------------------: |
+   |           -0,8           |      7,7      |        8,5         |                   Best Western Hotel Astoria |
+   |           -0,7           |      8,8      |        9,5         | Hotel Stendhal Place Vend me Parigi MGallery |
+   |           -0,7           |      7,5      |        8.2         |                Mercure Paris Porte d'Orléans |
+   |           -0,7           |      7,9      |        8,6         |              Renaissance Paris Vendome Hotel |
+   |           -0,5           |      7,0      |        7,5         |                          Hotel Royal Elys es |
+   |           ...            |      ...      |        ...         |                                          ... |
+   |           0.7            |      7,5      |        6.8         |      Mercure Paris Op ra Faubourg Montmartre |
+   |           0,8            |      7,1      |        6.3         |       Holiday Inn Paris Montparnasse Pasteur |
+   |           0,9            |      6.8      |        5,9         |                                Villa Eugenia |
+   |           0,9            |      8,6      |        7,7         |   MARCHESE Faubourg St Honor Relais Ch teaux |
+   |           1,3            |      7,2      |        5,9         |                           Kube Hotel Ice Bar |
+
+   Con un solo hotel con una differenza di punteggio maggiore di 1, significa che probabilmente si può ignorare la differenza e utilizzare il punteggio medio calcolato.
+
+6. Calcolare e stampare quante righe hanno la colonna `Negative_Review` valori di "No Negative"
+
+7. Calcolare e stampare quante righe hanno la colonna `Positive_Review` valori di "No Positive"
+
+8. Calcolare e stampare quante righe hanno la colonna `Positive_Review` valori di "No Positive" **e** `Negative_Review` valori di "No Negative"
+
+   ```python
+   # con funzini lambda:
+   start = time.time()
+   no_negative_reviews = df.apply(lambda x: True if x['Negative_Review'] == "No Negative" else False , axis=1)
+   print("Number of No Negative reviews: " + str(len(no_negative_reviews[no_negative_reviews == True].index)))
+
+   no_positive_reviews = df.apply(lambda x: True if x['Positive_Review'] == "No Positive" else False , axis=1)
+   print("Number of No Positive reviews: " + str(len(no_positive_reviews[no_positive_reviews == True].index)))
+
+   both_no_reviews = df.apply(lambda x: True if x['Negative_Review'] == "No Negative" and x['Positive_Review'] == "No Positive" else False , axis=1)
+   print("Number of both No Negative and No Positive reviews: " + str(len(both_no_reviews[both_no_reviews == True].index)))
+   end = time.time()
+   print("Lambdas took " + str(round(end - start, 2)) + " seconds")
+
+   Number of No Negative reviews: 127890
+   Number of No Positive reviews: 35946
+   Number of both No Negative and No Positive reviews: 127
+   Lambdas took 9.64 seconds
+   ```
+
+## Un'altra strada
+
+Un altro modo per contare gli elementi senza Lambda e utilizzare sum per contare le righe:
+
+```python
+# senza funzioni lambda (usando un misto di notazioni per mostrare che si possono usare entrambi)
+start = time.time()
+no_negative_reviews = sum(df.Negative_Review == "No Negative")
+print("Number of No Negative reviews: " + str(no_negative_reviews))
+
+no_positive_reviews = sum(df["Positive_Review"] == "No Positive")
+print("Number of No Positive reviews: " + str(no_positive_reviews))
+
+both_no_reviews = sum((df.Negative_Review == "No Negative") & (df.Positive_Review == "No Positive"))
+print("Number of both No Negative and No Positive reviews: " + str(both_no_reviews))
+
+end = time.time()
+print("Sum took " + str(round(end - start, 2)) + " seconds")
+
+Number of No Negative reviews: 127890
+Number of No Positive reviews: 35946
+Number of both No Negative and No Positive reviews: 127
+Sum took 0.19 seconds
+```
+
+Si potrebbe aver notato che ci sono 127 righe che hanno entrambi i valori "No Negative" e "No Positive" rispettivamente per le colonne `Negative_Review` e `Positive_Review` . Ciò significa che il recensore ha assegnato all'hotel un punteggio numerico, ma ha rifiutato di scrivere una recensione positiva o negativa. Fortunatamente questa è una piccola quantità di righe (127 su 515738, o 0,02%), quindi probabilmente non distorcerà il modello o i risultati in una direzione particolare, ma si potrebbe non aspettarsi che un insieme di dati di recensioni abbia righe con nessuna recensione, quindi vale la pena esplorare i dati per scoprire righe come questa.
+
+Ora che si è esplorato l'insieme di dati, nella prossima lezione si filtreranno i dati e si aggiungerà un'analisi del sentiment.
+
+---
+
+## 🚀 Sfida
+
+Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fondamentale importanza comprendere i dati e le loro debolezze prima di eseguire operazioni su di essi. I dati basati su testo, in particolare, sono oggetto di un attento esame. Esaminare vari insiemi di dati contenenti principalmente testo e vedere se si riesce a scoprire aree che potrebbero introdurre pregiudizi o sentiment distorti in un modello.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Seguire [questo percorso di apprendimento su NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa) per scoprire gli strumenti da provare durante la creazione di modelli vocali e di testo.
+
+## Compito
+
+[NLTK](assignment.it.md)
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
new file mode 100644
index 000000000..59518abdc
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
@@ -0,0 +1,408 @@
+# 호텔 리뷰로 감정 분석하기 - 데이터 처리
+
+이 섹션에서는 큰 데이터셋의 탐색적으로 데이터를 분석하는 이전 강의의 기술을 사용할 예정입니다. 다양한 열의 유용성을 잘 이해하면, 배울 수 있습니다:
+
+- 필요하지 않은 열을 제거하는 방식
+- 이미 존재하는 열을 기반으로 일부 새로운 데이터를 계산하는 방식
+- 최종 도전에서 사용하고자 결과 데이터셋을 저장하는 방식
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
+
+### 소개
+
+지금까지 텍스트 데이터가 숫자 데이터 타입과 꽤 다르다는 것을 배웠습니다.  만약 사람이 텍스트를 쓰거나 읽는다면, 패턴과 빈도, 감정과 의미를 찾기 위해서 분석할 수 있습니다. 이 강의에서 실전인 진짜 데이터로 진행합니다:  **[515K Hotel Reviews Data in Europe](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe)** 이며 [CC0: Public Domain license](https://creativecommons.org/publicdomain/zero/1.0/)를 포함합니다. 퍼블릭 소스의 from Booking.com에서 스크랩했습니다. Jiashen Liu가 데이터셋을 생성했습니다.
+
+### 준비
+
+다음 내용이 필요할 예정입니다:
+
+* Python 3로 .ipynb 노트북을 실행할 능력
+* pandas
+* NLTK, [which you should install locally](https://www.nltk.org/install.html)
+* Kaggle [515K Hotel Reviews Data in Europe](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe)에서 사용할 데이터셋. 압축 풀면 230 MB 입니다. NLP 강의와 관련있는 상단 `/data` 폴더에 내려받습니다.
+
+## 탐색적 데이터 분석
+
+이 도전은 감정 분석과 게스트 리뷰 점수로 호텔 추천 봇을 만든다고 가정합니다. 데이터셋은 6개 도시에 있는 1493개 다른 호텔의 리뷰를 포함해서 사용합니다.
+
+호델 리뷰 데이터셋을 Python으로, NLTK 감정 분석을 확인할 수 있습니다:
+
+* 리뷰에서 가장 빈번하게 사용된 단어와 구문은 무엇인가요?
+* 호텔을 설명하는 공식 *tags*는 리뷰 점수와 관계가 있나요? (예시로, *Solo traveller*보다 *Family with young children*으로 왔을 때 호텔 리뷰가 더 부정적이고, 어쩌면 *Solo travellers*가 더 괜찮나요?)
+* NLTK 감정 점수가 호텔 리뷰어의 숫자로만 이루어진 점수를 '동의'할 수 있나요?
+
+#### 데이터셋
+
+내려받아서 로컬에 저장한 데이터셋을 알아보겠습니다. VS Code나 엑셀 같은 에디터로 파일을 엽니다.
+
+데이터셋의 헤더는 다음과 같습니다:
+
+*Hotel_Address, Additional_Number_of_Scoring, Review_Date, Average_Score, Hotel_Name, Reviewer_Nationality, Negative_Review, Review_Total_Negative_Word_Counts, Total_Number_of_Reviews, Positive_Review, Review_Total_Positive_Word_Counts, Total_Number_of_Reviews_Reviewer_Has_Given, Reviewer_Score, Tags, days_since_review, lat, lng*
+
+여기에 쉽게 풀 수 있는 방식으로 그룹을 묶습니다:
+
+##### Hotel 열
+
+* `Hotel_Name`, `Hotel_Address`, `lat` (latitude), `lng` (longitude)
+  * *lat* 과 *lng*으로 Python에서 호텔 위치를 보일 맵을 plot합니다 (아마 부정적이고 긍정적인 리뷰에 대한 색상)
+  * Hotel_Address는 분명 우리에게 유용하지 않으므로, 쉽게 분류하고 검색할 수 있게 국가로 대체할 예정입니다
+
+**Hotel Meta-review 열**
+
+* `Average_Score`
+  * 데이터셋을 만든 사람에 의하면, 이 열은 *지난 연도에 작성된 최신 코멘트를 기반으로 계산한, 호텔의 평균 점수*입니다. 점수를 계산하는 특이한 방식처럼 보이지만, 현재 액면가로 보일 수 있도록 스크랩한 데이터입니다.
+
+  ✅ 데이터에서 다른 열을 기반으로, 평균 점수를 계산하는 방식을 생각할 수 있나요?
+
+* `Total_Number_of_Reviews`
+  * 호텔이 받은 리뷰의 총 개수 - 만약 데이터셋의 리뷰를 나타내는 것이면 (일부 코드 작성없이) 명확하지 않습니다 
+* `Additional_Number_of_Scoring`
+  * 리뷰 점수가 부여되었지만 리뷰어가 작성한 긍정적이나 부정적인 리뷰가 없다는 점을 의미합니다
+
+**Review columns**
+
+- `Reviewer_Score`
+  - 숫자 값은 최소 2.5 최대 값 10 사이의 최대 소수점 1자리 입니다.
+  - 2.5가 낮은 숫자인 이유는 없습니다
+- `Negative_Review`
+  - 만약 리뷰어가 아무것도 작성하지 않았다면, 필드는 "**No Negative**"로 채워집니다
+  - 리뷰어가 부정적인 리뷰 열에 긍정적인 리뷰를 남길 수 있다는 점을 참고합니다 (e.g. "there is nothing bad about this hotel")
+- `Review_Total_Negative_Word_Counts`
+  - 많은 부정적인 단어들은 점수가 낮습니다 (감정을 확인하지 않습니다)
+- `Positive_Review`
+  - 만약 리뷰어가 아무것도 작성하지 않았다면, 필드는 "**No Positive**"로 채워집니다
+  - 리뷰어가 긍정적인 리뷰 열에 부정적인 리뷰를 남길 수 있다는 점을 참고합니다 (e.g. "there is nothing good about this hotel at all")
+- `Review_Total_Positive_Word_Counts`
+  - 많은 긍정적인 단어들은 점수가 높습니다 (감정을 확인하지 않습니다)
+- `Review_Date` and `days_since_review`
+  - 최근이나 과거에 작성된 리뷰가 적용될 수 있습니다 (과거에 작성된 리뷰는 호텔 관리가 바뀌었거나, 리노베이션이 끝났거나, 수영장이 생기는 등 최근보다 정확하지 않을 수 있습니다.) 
+- `Tags`
+  - 리뷰어가 게스트 타입 (예시. 솔로 혹은 패밀리), 지낸 룸 타입, 지낸 기간처럼 작성한 리뷰 방식을 설명하고자 선택할 수 있는 짧은 설명문입니다. 
+  - 불행하게도, 이 태그를 사용하는 것은 문제가 있으므로, 아래 usefulness를 설명하는 섹션으로 확인합니다
+
+**리뷰어 열**
+
+- `Total_Number_of_Reviews_Reviewer_Has_Given`
+  - 예시로, 수백 개 리뷰를 작성한 다량의 리뷰어가 긍정적보다 부정적으로 리뷰를 남긴다고 판단할 수 있다면, recommendation 모델에서 한 인자가 될 수 있습니다. 그러나, 특정 리뷰를 작성한 리뷰어는 유니크 코드로 식별하지 않으므로, 리뷰 셋에 링크할 수 없습니다. 100개보다 더 작성한 30명 리뷰어가 있지만, recommendation 모델에 어떤 방식으로 도울 수 있는지 보기 힘듭니다.
+  
+- `Reviewer_Nationality`
+  - 일부 사람들은 국가마다 성향이 다르므로 긍정적이나 부정적인 리뷰를 줄 수 있다고 생각합니다. 모델에 사례 증거를 신중히 만듭니다. 국가(인종)에 대한 고정 관념이며, 각 리뷰어는 경험을 기반해서 개인적으로 리뷰를 작성했습니다. 이전에 지낸 호텔, 이동한 거리, 그리고 개인 체질 같은 요인으로 다르게 필터링되었을 수 있습니다. 국적이 리뷰 점수에 대한 이유가 되는 것은 합리적이지 않습니다.
+
+##### 예제
+
+| Average  Score | Total Number   Reviews | Reviewer   Score | Negative <br />Review                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | Positive   Review                 | Tags                                                                                      |
+| -------------- | ---------------------- | ---------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------- |
+| 7.8            | 1945                   | 2.5              | This is  currently not a hotel but a construction site I was terroized from early  morning and all day with unacceptable building noise while resting after a  long trip and working in the room People were working all day i e with  jackhammers in the adjacent rooms I asked for a room change but no silent  room was available To make thinks worse I was overcharged I checked out in  the evening since I had to leave very early flight and received an appropiate  bill A day later the hotel made another charge without my concent in excess  of booked price It s a terrible place Don t punish yourself by booking  here | Nothing  Terrible place Stay away | Business trip                                Couple Standard Double  Room Stayed 2 nights |
+
+본 내용처럼, 게스트는 호텔에 즐겁게 지내지 못했습니다. 호텔은 1945개 리뷰 중 7.8 점으로 좋은 평균이지만, 리뷰어는 2.5점을 주며 지냈던 내용을 115개 단어를 부정적으로 남겼습니다. 만약 Positive_Review 열이 깨끗하면, 긍정이 없다고 추측하겠지만, 7개 경고문을 작성했습니다. 만약 단어 의미나 단어 감정 대신 단어 자체를 센다면, 리뷰어 의도를 왜곡해서 볼 수 있습니다. 이상하게, 2.5 점은 혼란스러우며, 만약 호탤애 지낸 게 불쾌하다고, 모든 포인트를 다 주나요? 데이터셋을 가까이 조사하면, 낮을 수 있는 점수가 0점이 아니라, 2.5점이라고 볼 수 있습니다. 가능한 높은 점수는 10점 입니다.
+
+##### Tags
+
+언급된 것처럼, 한 눈에, `Tags`로 데이터를 분류하는 아이디어는 의미있습니다. 불행히, 주어진 호텔에서는, 옵션이  *Single room*, *Twin room*, 그리고 *Double room* 옵션일 수 있지만, 다음 호텔에서, *Deluxe Single Room*, *Classic Queen Room*, 그리고 *Executive King Room*처럼 태그는 표준화되지 못했습니다. 같은 것을 의미할 수 있지만, 너무 많은 옵션이 있으므로 다음을 선택합니다:
+
+1. 각자 케이스에 맞게 바꿀 방식이 명확하지 않으므로, 모든 단어를 단일 표준으로 변경하려는 시도는 매우 어렵습니다. (예시. *Classic single room* 을 *Single room*으로 맵핑하는 것은 가능하지만 *Superior Queen Room with Courtyard Garden or City View*를 맵핑하려면 매우 어렵습니다)
+
+1. NLP 접근 방식을 가지고 각자 호텔에 적용되는 *Solo*, *Business Traveller*, 또는 *Family with young kids* 같은 특정 용어의 빈도를 측정하며, 추천 모델에 인자로 반영합니다 
+
+태그는 일반적으로 (항상 그렇지 않지만) *Type of trip*, *Type of guests*, *Type of room*, *Number of nights*, 그리고 *Type of device review was submitted on* 에 따라서 5에서 6개 컴마로 구분된 값 리스트를 포함한 단일 필드입니다. 그러나, 일부 리뷰어는 각 필드를 채우지 않기 때문에 (하나정도 공백으로 남길 수 있습니다), 값을 동일한 순서로 항상 유지할 수 없습니다.
+
+예시는, *Type of group*으로 합니다. `Tags` 열에 1025개 필드가 서로 안 겹칠 가능성이 있지만, 불행히도 일부만 그룹으로 (일부 방 타입처럼) 참조합니다. 만약 패밀리를 언급한 것만 필터링하면, 많은 *Family room* 타입 결과를 포함해서 냅니다. 만약 *with* 용어를 포함하면, 즉 *Family with* 값을 센다면, 결과는 더 좋게, "Family with young children" 또는 "Family with older children" 문구가 포함된 515,000개 중 80,000개 넘는 결과가 나옵니다.
+
+태그 열을 쓰기에는 완벽하지 않다는 것을 의미하지만, 약간 작업하면 유용해질 수 있습니다.
+
+##### 평균 호텔 점수
+
+이해할 수 없는 데이터셋으로 이상하거나 모순되는게 많지만, 모델을 만들 때 알 수 있는 예시가 있습니다.  만약 알게 된다면, 토론 섹션에 알려주세요!
+
+데이터셋의 평균 점수와 리뷰 수에 관련된 열은 다음과 같습니다:
+
+1. Hotel_Name
+2. Additional_Number_of_Scoring
+3. Average_Score
+4. Total_Number_of_Reviews
+5. Reviewer_Score  
+
+이 데이터셋에서 가장 리뷰가 많은 싱글 호텔은 515,000개 중 4789개 리뷰를 받은 *Britannia International Hotel Canary Wharf*입니다. 하지만 만약 호텔의 `Total_Number_of_Reviews` 값을 보면, 9086 입니다. 리뷰없는 점수가 많을 것이라고 추정할 수 있어서, `Additional_Number_of_Scoring` 열 값을 추가할 필요가 있습니다. 이 값은 2682이며, `Total_Number_of_Reviews`의 1615가 부족해져서 4789를 더하면 7,471이 됩니다.
+
+만약 `Average_Score` 열로, 데이터셋에서 리뷰의 평균을 추축할 수 있지만, Kaggle 설명은 "*Average Score of the hotel, calculated based on the latest comment in the last year*"라고 합니다. 유용해보이지 않지만, 데이터셋의 리뷰 점수를 기반해서 평균을 계산할 수 있습니다. 예시로 동일한 호텔을 사용해서, 평균 호텔 점수가 7.1로 주어졌지만 계산된 점수는(데이터셋의 평균 리뷰어 점수) 6.8입니다. 비슷하지만, 같은 값은 아니고, `Additional_Number_of_Scoring` 리뷰에서 주어진 점수가 평균을 7.1 올렸다고 추측할 수 있습니다. 불행히 테스트하거나 확인할 방식은 없으므로, 가지고 있지 않는 것을 기반으로 하거나 참조할 때 `Average_Score`, `Additional_Number_of_Scoring` 과 `Total_Number_of_Reviews`를 쓰거나 신뢰하기 어렵습니다.
+
+더 복잡해지면, 두 번째로 리뷰 수가 높은 호텔은 계산된 평균 점수 8.12이며 데이터셋 `Average_Score`는 8.1입니다. 우연히 점수를 맞추었거나 첫 번째 호텔이 모순되었나요?
+
+이 호텔이 아웃라이어일 수 있고, 아마도 대부분 값을 센다는 가정에 (하지만 일부 이유로 안 합니다) 데이터셋 값을 찾고 값을 올바르게 사용하거나 (또는 사용하지 않게) 결정하는 짧은 프로그램을 작성할 예정입니다.
+
+> 🚨 주의할 사항
+>
+> 데이터셋으로 작업할 때 텍스트를 수동으로 읽거나 분석하지 않고 텍스트에서 무언가를 계산하는 코드를 작성하게 됩니다. 사람을 거치지 않고 의미나 감정을 분석하는 것은, NLP의 정수입니다. 그러나, 알부 부정적인 리뷰를 읽을 수 있습니다. 직접 할 필요가 없으므로, 하지말라고 권유하고 싶습니다. 일부는 "The weather wasn't great"와 같이, 멍청하거나, 관련없는 부정적 호텔 리뷰이거나, 호텔에서 어떤 컨트롤도 할 수 없는 내용입니다. 하지만 이것도 일부 어두운 측면의 리뷰입니다. 때로 부정적인 리뷰는 racist, sexist, 또는 ageist적 입니다. 불행하지만 퍼블릭 웹사이트에서 긁어온 데이터에서 예상하게 됩니다. 일부 리뷰어는 싫어하거나, 불편하거나, 화낼 수 있는 리뷰를 남깁니다. 읽고 속상한 것보다 코드로 감정을 측정하는 것이 더 좋습니다. 즉, 소수가 작성하지만, 모두 똑같이 있습니다.
+
+## 연습 -  데이터 탐색
+
+### 데이터 불러오기
+
+데이터를 시각적으로 확인하는 것도 충분하므로, 이제 살짝 코드를 작성하고 답변을 얻겠습니다! 이 섹션에서 pandas 라이브러리를 사용합니다. 첫 작업은 CSV 데이터를 불러오고 읽어올 수 있는지 확인하는 작업입니다. pandas 라이브러리는 빠른 CSV 리더이며, 이전 강의에서 했던, dataframe에 결과를 둡니다. 불러올 CSV는 50만 행이 넘지만, 17 열만 있습니다. Pandas는 모든 행에서 작업할 기능을 포함해서, 데이터프레임과 상호 작용하는 강력한 방식을 많이 제공합니다.
+
+이 강의의 이 곳에서, 코드 스니펫과 일부 코드 설명과 일부 결과에 의미를 둔 토론을 하게 됩니다.  코드에 있는 _notebook.ipynb_ 를 사용합니다.
+
+사용할 데이터 파일을 불러오기 위해서 시작합니다:
+
+```python
+# Load the hotel reviews from CSV
+import pandas as pd
+import time
+# importing time so the start and end time can be used to calculate file loading time
+print("Loading data file now, this could take a while depending on file size")
+start = time.time()
+# df is 'DataFrame' - make sure you downloaded the file to the data folder
+df = pd.read_csv('../../data/Hotel_Reviews.csv')
+end = time.time()
+print("Loading took " + str(round(end - start, 2)) + " seconds")
+```
+
+이제 데이터가 불러와지면, 일부 작업을 할 수 있습니다. 다음 파트를 위해서 프로그램의 상단에 코드를 둡니다.
+
+## 데이터 탐색하기
+
+이 케이스에서, 데이터는 이미 *깨끗하므로*, 작업할 준비가 되었고, 영어만 대상으로한 알고리즘에 걸릴 수 있는 다른 언어가 없다는 점을 의미합니다.
+
+✅ NLP 기술을 적용하기 전 포맷에 일부 초기 처리가 필요한 데이터로 작성할 수 있지만, 이번에는 아닙니다. 만약 한다면, 비-영어권 언어를 핸들링 할 수 있나요?
+
+데이터를 불러오는 순간, 코드로 탐색할 수 있는지 시간을 내어 확인합니다. `Negative_Review` 와 `Positive_Review` 열에 집중하기 윈하면 매우 쉽습니다. NLP 알고리즘이 처리할 자연어 텍스트로 채워졌습니다. 하지만 잠깐 기다려봅니다! NLP와 감정으로 들어가기 전, 다음 코드로 만약 데이터셋에 주어진 값이 pandas로 계산한 값과 매치되는지 확실히 봅시다.
+
+## 데이터프레임 작업
+
+이 강의에서 첫 작업은 데이터프레임을 (바꾸지 않고) 점검하는 코드로 작성해서 다음 가정이 올바른지 체크하는 것입니다.
+
+> 많은 프로그래밍 작업처럼, 마무리할 여러 방식이 있지만, 특히 미래에 다시 코드를 작성할 때 이해하기 쉬우려면, 간단하고, 쉽게 할 수 있도록 좋은 조언을 하게 됩니다. 데이터프레임에는, 효율적인 방식으로 자주 할 수 있는 포괄적인 API가 있습니다.
+
+코딩 작업으로 다음 질문을 설명하며 솔루션에 기대지 않고 답변할 수 있게 시도합니다
+
+1. 직전에 불러온 데이터프레임의 *shape*를 출력합니다 (shape는 행과 열의 개수입니다)
+2. 리뷰어 국적 대상으로 빈도 카운트를 계산합니다:
+   1. `Reviewer_Nationality` 열에 얼마나 많은 별개 값이 있으며 어떤가요?
+   2. 데이터셋에서 가장 일반적인 리뷰어 국적은 어디인가요 (국가와 리뷰 개수 출력)?
+   3. 다음으로 더 자주 발견되는 국적은 어디고, 빈도를 카운트하나요?
+3. 상위 10에 드는 리뷰어 국적에서 가장 자주 리뷰된 호텔은 어디인가요?
+4. 데이터셋에서 호텔 별 (호텔의 빈도 개수) 리뷰가 얼마나 있나요?
+5. 데이터셋에서 각 호텔마다 `Average_Score` 열이 있지만, 평균 점수도 계산할 수 있습니다 (각 호텔의 데이터셋에서 모든 리뷰어 점수의 평균을 얻습니다). 계산된 평균을 포함한 `Calc_Average_Score` 열 헤더로 데이터프레임에 새로운 열을 추가합니다. 
+6. 호텔에서 동일한 (반올림한 소수점 1자리) `Average_Score` 와 `Calc_Average_Score`를 가지고 있나요?
+   1. Series (row)로 값을 비교하며, 값이 같지 않을 때 메시지를 출력하는 Python 함수를 작성합니다. 그러면 `.apply()` 메소드를 사용해서 처리하면 함수로 모든 행을 처리할 수 있습니다.
+7. "No Negative"의 `Negative_Review` 열 값에 있는 행이 얼마나 있는지 계산하고 출력합니다
+8. "No Positive"의 `Positive_Review` 열 값에 있는 행이 얼마나 있는지 계산하고 출력합니다
+9. "No Positive"의 `Negative_Review` 열 값**과** "No Negative"의 `Negative_Review` 열 값에 있는 행이 얼마나 있는지 계산하고 출력합니다
+
+### 코드 답변
+
+1. 직전에 불러온 데이터프레임의 *shape*를 출력합니다 (shape는 행과 열의 개수입니다)
+
+   ```python
+   print("The shape of the data (rows, cols) is " + str(df.shape))
+   > The shape of the data (rows, cols) is (515738, 17)
+   ```
+
+2. 리뷰어 국적 대상으로 빈도 카운트를 계산합니다:
+
+   1. `Reviewer_Nationality` 열에 얼마나 많은 별개 값이 있으며 어떤가요?
+   2. 데이터셋에서 가장 일반적인 리뷰어 국적은 어디인가요 (국가와 리뷰 개수 출력)?
+
+   ```python
+   # value_counts() creates a Series object that has index and values in this case, the country and the frequency they occur in reviewer nationality
+   nationality_freq = df["Reviewer_Nationality"].value_counts()
+   print("There are " + str(nationality_freq.size) + " different nationalities")
+   # print first and last rows of the Series. Change to nationality_freq.to_string() to print all of the data
+   print(nationality_freq) 
+   
+   There are 227 different nationalities
+    United Kingdom               245246
+    United States of America      35437
+    Australia                     21686
+    Ireland                       14827
+    United Arab Emirates          10235
+                                  ...  
+    Comoros                           1
+    Palau                             1
+    Northern Mariana Islands          1
+    Cape Verde                        1
+    Guinea                            1
+   Name: Reviewer_Nationality, Length: 227, dtype: int64
+   ```
+
+   3. 다음으로 더 자주 발견되는 국적은 어디고, 빈도를 카운트하나요?
+
+      ```python
+      print("The highest frequency reviewer nationality is " + str(nationality_freq.index[0]).strip() + " with " + str(nationality_freq[0]) + " reviews.")
+      # Notice there is a leading space on the values, strip() removes that for printing
+      # What is the top 10 most common nationalities and their frequencies?
+      print("The next 10 highest frequency reviewer nationalities are:")
+      print(nationality_freq[1:11].to_string())
+      
+      The highest frequency reviewer nationality is United Kingdom with 245246 reviews.
+      The next 10 highest frequency reviewer nationalities are:
+       United States of America     35437
+       Australia                    21686
+       Ireland                      14827
+       United Arab Emirates         10235
+       Saudi Arabia                  8951
+       Netherlands                   8772
+       Switzerland                   8678
+       Germany                       7941
+       Canada                        7894
+       France                        7296
+      ```
+
+3. 상위 10에 드는 리뷰어 국적에서 가장 자주 리뷰된 호텔은 어디인가요?
+
+   ```python
+   # What was the most frequently reviewed hotel for the top 10 nationalities
+   # Normally with pandas you will avoid an explicit loop, but wanted to show creating a new dataframe using criteria (don't do this with large amounts of data because it could be very slow)
+   for nat in nationality_freq[:10].index:
+      # First, extract all the rows that match the criteria into a new dataframe
+      nat_df = df[df["Reviewer_Nationality"] == nat]   
+      # Now get the hotel freq
+      freq = nat_df["Hotel_Name"].value_counts()
+      print("The most reviewed hotel for " + str(nat).strip() + " was " + str(freq.index[0]) + " with " + str(freq[0]) + " reviews.") 
+      
+   The most reviewed hotel for United Kingdom was Britannia International Hotel Canary Wharf with 3833 reviews.
+   The most reviewed hotel for United States of America was Hotel Esther a with 423 reviews.
+   The most reviewed hotel for Australia was Park Plaza Westminster Bridge London with 167 reviews.
+   The most reviewed hotel for Ireland was Copthorne Tara Hotel London Kensington with 239 reviews.
+   The most reviewed hotel for United Arab Emirates was Millennium Hotel London Knightsbridge with 129 reviews.
+   The most reviewed hotel for Saudi Arabia was The Cumberland A Guoman Hotel with 142 reviews.
+   The most reviewed hotel for Netherlands was Jaz Amsterdam with 97 reviews.
+   The most reviewed hotel for Switzerland was Hotel Da Vinci with 97 reviews.
+   The most reviewed hotel for Germany was Hotel Da Vinci with 86 reviews.
+   The most reviewed hotel for Canada was St James Court A Taj Hotel London with 61 reviews.
+   ```
+
+4. 데이터셋에서 호텔 별 (호텔의 빈도 개수) 리뷰가 얼마나 있나요?
+
+   ```python
+   # First create a new dataframe based on the old one, removing the uneeded columns
+   hotel_freq_df = df.drop(["Hotel_Address", "Additional_Number_of_Scoring", "Review_Date", "Average_Score", "Reviewer_Nationality", "Negative_Review", "Review_Total_Negative_Word_Counts", "Positive_Review", "Review_Total_Positive_Word_Counts", "Total_Number_of_Reviews_Reviewer_Has_Given", "Reviewer_Score", "Tags", "days_since_review", "lat", "lng"], axis = 1)
+   
+   # Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found
+   hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')
+   
+   # Get rid of all the duplicated rows
+   hotel_freq_df = hotel_freq_df.drop_duplicates(subset = ["Hotel_Name"])
+   display(hotel_freq_df) 
+   ```
+   |                 Hotel_Name                 | Total_Number_of_Reviews | Total_Reviews_Found |
+   | :----------------------------------------: | :---------------------: | :-----------------: |
+   | Britannia International Hotel Canary Wharf |          9086           |        4789         |
+   |    Park Plaza Westminster Bridge London    |          12158          |        4169         |
+   |   Copthorne Tara Hotel London Kensington   |          7105           |        3578         |
+   |                    ...                     |           ...           |         ...         |
+   |       Mercure Paris Porte d Orleans        |           110           |         10          |
+   |                Hotel Wagner                |           135           |         10          |
+   |            Hotel Gallitzinberg             |           173           |          8          |
+   
+   *counted in the dataset* 결과로 `Total_Number_of_Reviews`에서 값이 매치되지 않는 점을 알 것입니다. 데이터셋의 값이 호텔의 모든 리뷰 개수를 나타낸다고 하지만, 모두 스크랩하지 못했거나, 계산이 다릅니다. `Total_Number_of_Reviews`는 불명확성으로 인하여 모델에서 사용하지 않습니다.
+
+5. 데이터셋에서 각 호텔마다 `Average_Score` 열이 있지만, 평균 점수를 계산할 수 있습니다 (각 호텔의 데이터셋에서 모든 리뷰어 점수의 평균을 얻습니다). 계산된 평균을 포함한 `Calc_Average_Score` 열 헤더로 데이터프레임에 새로운 열을 추가합니다. `Hotel_Name`, `Average_Score`, 그리고 `Calc_Average_Score` 열을 출력합니다.
+
+   ```python
+   # define a function that takes a row and performs some calculation with it
+   def get_difference_review_avg(row):
+     return row["Average_Score"] - row["Calc_Average_Score"]
+   
+   # 'mean' is mathematical word for 'average'
+   df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
+   
+   # Add a new column with the difference between the two average scores
+   df["Average_Score_Difference"] = df.apply(get_difference_review_avg, axis = 1)
+   
+   # Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)
+   review_scores_df = df.drop_duplicates(subset = ["Hotel_Name"])
+   
+   # Sort the dataframe to find the lowest and highest average score difference
+   review_scores_df = review_scores_df.sort_values(by=["Average_Score_Difference"])
+   
+   display(review_scores_df[["Average_Score_Difference", "Average_Score", "Calc_Average_Score", "Hotel_Name"]])
+   ```
+
+   또는 `Average_Score` 값과 계산된 평균 점수가 가끔 다른 이유를 궁금할 수 있습니다. 값의 일부는 매칭되지만, 다른지 알 수 없으므로, 이 케이스에서 스스로 평균을 계산하는 리뷰 점수로 사용해야 안전합니다. 말하기를, 차이가 일반적으로 매우 작지만, 여기는 데이터셋 평균과 계산된 평균에 가장 큰 차이를 보이는 호텔입니다:
+
+   | Average_Score_Difference | Average_Score | Calc_Average_Score |                                  Hotel_Name |
+   | :----------------------: | :-----------: | :----------------: | ------------------------------------------: |
+   |           -0.8           |      7.7      |        8.5         |                  Best Western Hotel Astoria |
+   |           -0.7           |      8.8      |        9.5         | Hotel Stendhal Place Vend me Paris MGallery |
+   |           -0.7           |      7.5      |        8.2         |               Mercure Paris Porte d Orleans |
+   |           -0.7           |      7.9      |        8.6         |             Renaissance Paris Vendome Hotel |
+   |           -0.5           |      7.0      |        7.5         |                         Hotel Royal Elys es |
+   |           ...            |      ...      |        ...         |                                         ... |
+   |           0.7            |      7.5      |        6.8         |     Mercure Paris Op ra Faubourg Montmartre |
+   |           0.8            |      7.1      |        6.3         |      Holiday Inn Paris Montparnasse Pasteur |
+   |           0.9            |      6.8      |        5.9         |                               Villa Eugenie |
+   |           0.9            |      8.6      |        7.7         |   MARQUIS Faubourg St Honor Relais Ch teaux |
+   |           1.3            |      7.2      |        5.9         |                          Kube Hotel Ice Bar |
+
+   점수 차이가 1보다 큰 호텔이 오직 1개이므로, 아마도 차이를 무시하고 계산된 평균 점수를 사용할 수 있을 것입니다.
+
+6. "No Negative"의 `Negative_Review` 열 값에 있는 행이 얼마나 있는지 계산하고 출력합니다
+
+7. "No Positive"의 `Positive_Review` 열 값에 있는 행이 얼마나 있는지 계산하고 출력합니다
+
+8. "No Positive"의 `Negative_Review` 열 값**과** "No Negative"의 `Negative_Review` 열 값에 있는 행이 얼마나 있는지 계산하고 출력합니다
+
+   ```python
+   # with lambdas:
+   start = time.time()
+   no_negative_reviews = df.apply(lambda x: True if x['Negative_Review'] == "No Negative" else False , axis=1)
+   print("Number of No Negative reviews: " + str(len(no_negative_reviews[no_negative_reviews == True].index)))
+   
+   no_positive_reviews = df.apply(lambda x: True if x['Positive_Review'] == "No Positive" else False , axis=1)
+   print("Number of No Positive reviews: " + str(len(no_positive_reviews[no_positive_reviews == True].index)))
+   
+   both_no_reviews = df.apply(lambda x: True if x['Negative_Review'] == "No Negative" and x['Positive_Review'] == "No Positive" else False , axis=1)
+   print("Number of both No Negative and No Positive reviews: " + str(len(both_no_reviews[both_no_reviews == True].index)))
+   end = time.time()
+   print("Lambdas took " + str(round(end - start, 2)) + " seconds")
+   
+   Number of No Negative reviews: 127890
+   Number of No Positive reviews: 35946
+   Number of both No Negative and No Positive reviews: 127
+   Lambdas took 9.64 seconds
+   ```
+
+## 다른 방식
+
+다른 방식으로 Lambda 없이 아이템을 카운트하고, sum으로 행을 카운트합니다:
+
+   ```python
+   # without lambdas (using a mixture of notations to show you can use both)
+   start = time.time()
+   no_negative_reviews = sum(df.Negative_Review == "No Negative")
+   print("Number of No Negative reviews: " + str(no_negative_reviews))
+   
+   no_positive_reviews = sum(df["Positive_Review"] == "No Positive")
+   print("Number of No Positive reviews: " + str(no_positive_reviews))
+   
+   both_no_reviews = sum((df.Negative_Review == "No Negative") & (df.Positive_Review == "No Positive"))
+   print("Number of both No Negative and No Positive reviews: " + str(both_no_reviews))
+   
+   end = time.time()
+   print("Sum took " + str(round(end - start, 2)) + " seconds")
+   
+   Number of No Negative reviews: 127890
+   Number of No Positive reviews: 35946
+   Number of both No Negative and No Positive reviews: 127
+   Sum took 0.19 seconds
+   ```
+
+   둘 다 `Negative_Review` 와  `Positive_Review` 열에 대하여 각자 "No Negative"와 "No Positive" 값이 127개 행을 가지고 있다는 점을 알 수 있습니다. 리뷰어가 숫자로 이루어진 점수를 호텔에 주었지만, 어느 긍정적이나 부정적인 리뷰도 남기지 않았다는 점을 의미합니다. 운이 좋아서 적은 행 개수로(515738 중 127, 0.02%), 모델 또는 결과가 어느 방향으로 치우치지 않앗지만, 리뷰 데이터셋에 리뷰가 없으므로, 이와 같이 행을 찾기 위해서 데이터를 찾을 보람이 있습니다.
+
+지금 데이터셋을 찾아서, 다음 강의에서 데이터를 필터링하고 감정 분석을 추가해봅니다.
+
+---
+## 🚀 도전
+
+이전 강의에서 본 것처럼, 이 강의에서 작업하기 전 데이터와 약점을 이해하는 것이 얼마나 치명적이게 중요한지 보여줍니다. 특별히, 텍스트-기반 데이터는, 조심히 조사해야 합니다. 다양한 text-heavy 데이터셋을 파보고 모델에서 치우치거나 편향된 감정으로 끼워놓은 영역을 찾을 수 있는지 확인합니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
+
+## 검토 & 자기주도 학습
+
+[this Learning Path on NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa)로 음성과 text-heavy 모델을 만들 때 시도하는 도구를 찾아봅니다.
+
+## 과제 
+
+[NLTK](../assignment.md)
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/assignment.it.md b/6-NLP/4-Hotel-Reviews-1/translations/assignment.it.md
new file mode 100644
index 000000000..5fd90e11e
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/translations/assignment.it.md
@@ -0,0 +1,5 @@
+# NLTK
+
+## Instructions
+
+NLTK is a well-known library for use in computational linguistics and NLP. Take this opportunity to read through the '[NLTK book](https://www.nltk.org/book/)' and try out its exercises. In this ungraded assignment, you will get to know this library more deeply.
diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md
index 7d8a4d031..c764b8e1c 100644
--- a/6-NLP/5-Hotel-Reviews-2/README.md
+++ b/6-NLP/5-Hotel-Reviews-2/README.md
@@ -1,7 +1,7 @@
 # Sentiment analysis with hotel reviews
 
 Now that you have a explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
 
 ### Filtering & Sentiment Analysis Operations
 
@@ -347,20 +347,20 @@ print("Saving results to Hotel_Reviews_NLP.csv")
 df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
 ```
 
-You should run the entire code for [the analysis notebook](solution/notebook-sentiment-analysis.ipynb) (after you've run [your filtering notebook](solution/notebook-filtering.ipynb) to generate the Hotel_Reviews_Filtered.csv file).
+You should run the entire code for [the analysis notebook](solution/3-notebook.ipynb) (after you've run [your filtering notebook](solution/1-notebook.ipynb) to generate the Hotel_Reviews_Filtered.csv file).
 
 To review, the steps are:
 
-1. Original dataset file **Hotel_Reviews.csv** is explored in the previous lesson with [the explorer notebook](../4-Hotel-Reviews-1/solution/notebook-explorer.ipynb)
-2. Hotel_Reviews.csv is filtered by [the filtering notebook](solution/notebook-filtering.ipynb) resulting in **Hotel_Reviews_Filtered.csv**
-3. Hotel_Reviews_Filtered.csv is processed by [the sentiment analysis notebook](solution/notebook-sentiment-analysis.ipynb) resulting in **Hotel_Reviews_NLP.csv**
+1. Original dataset file **Hotel_Reviews.csv** is explored in the previous lesson with [the explorer notebook](../4-Hotel-Reviews-1/solution/notebook.ipynb)
+2. Hotel_Reviews.csv is filtered by [the filtering notebook](solution/1-notebook.ipynb) resulting in **Hotel_Reviews_Filtered.csv**
+3. Hotel_Reviews_Filtered.csv is processed by [the sentiment analysis notebook](solution/3-notebook.ipynb) resulting in **Hotel_Reviews_NLP.csv**
 4. Use Hotel_Reviews_NLP.csv in the NLP Challenge below
 
 ### Conclusion
 
 When you started, you had a dataset with columns and data but not all of it could be verified or used. You've explored the data, filtered out what you don't need, converted tags into something useful, calculated your own averages, added some sentiment columns and hopefully, learned some interesting things about processing natural text.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
 
 ## Challenge
 
diff --git a/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/6-NLP/5-Hotel-Reviews-2/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
new file mode 100644
index 000000000..2e7780003
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
@@ -0,0 +1,376 @@
+# Analisi del sentiment con recensioni di hotel
+
+Ora che si è  esplorato in dettaglio l'insieme di dati, è il momento di filtrare le colonne e quindi utilizzare le tecniche NLP sull'insieme di dati per ottenere nuove informazioni sugli hotel.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/?loc=it)
+
+### Operazioni di Filtraggio e Analisi del Sentiment
+
+Come probabilmente notato, l'insieme di dati presenta alcuni problemi. Alcune colonne sono piene di informazioni inutili, altre sembrano errate. Se sono corrette, non è chiaro come sono state calcolate e le risposte non possono essere verificate in modo indipendente dai propri calcoli.
+
+## Esercizio: un po' più di elaborazione dei dati
+
+Occorre pulire un po' di più i dati. Si aggiungono colonne che saranno utili in seguito, si modificano i valori in altre colonne e si eliminano completamente determinate colonne.
+
+1. Elaborazione iniziale colonne
+
+   1. Scartare `lat` e `lng`
+
+   2. Sostituire i valori `Hotel_Address` con i seguenti valori (se l'indirizzo contiene lo stesso della città e del paese, si cambia solo con la città e la nazione).
+
+      Queste sono le uniche città e nazioni nell'insieme di dati:
+
+      Amsterdam, Netherlands
+
+      Barcelona, Spain
+
+      London, United Kingdom
+
+      Milan, Italy
+
+      Paris, France
+
+      Vienna, Austria
+
+      ```python
+      def replace_address(row):
+          if "Netherlands" in row["Hotel_Address"]:
+              return "Amsterdam, Netherlands"
+          elif "Barcelona" in row["Hotel_Address"]:
+              return "Barcelona, Spain"
+          elif "United Kingdom" in row["Hotel_Address"]:
+              return "London, United Kingdom"
+          elif "Milan" in row["Hotel_Address"]:        
+              return "Milan, Italy"
+          elif "France" in row["Hotel_Address"]:
+              return "Paris, France"
+          elif "Vienna" in row["Hotel_Address"]:
+              return "Vienna, Austria"
+
+      # Sostituisce tutti gli indirizzi con una forma ridotta più utile
+      df["Hotel_Address"] = df.apply(replace_address, axis = 1)
+      # La somma di value_counts() dovrebbe sommarsi al numero totale recensioni
+      print(df["Hotel_Address"].value_counts())
+      ```
+
+      Ora si possono interrogare i dati a livello di nazione:
+
+      ```python
+      display(df.groupby("Hotel_Address").agg({"Hotel_Name": "nunique"}))
+      ```
+
+      | Hotel_Address          | Hotel_Name (Nome Hotel) |
+      | :--------------------- | :---------------------: |
+      | Amsterdam, Paesi Bassi |           105           |
+      | Barcellona, Spagna     |           211           |
+      | Londra, Regno Unito    |           400           |
+      | Milano, Italia         |           162           |
+      | Parigi, Francia        |           458           |
+      | Vienna, Austria        |           158           |
+
+2. Elaborazione colonne di meta-recensione dell'hotel
+
+1. Eliminare `Additional_Number_of_Scoring`
+
+1. Sostituire `Total_Number_of_Reviews` con il numero totale di recensioni per quell'hotel che sono effettivamente nell'insieme di dati
+
+1. Sostituire `Average_Score` con il punteggio calcolato via codice
+
+```python
+# Elimina `Additional_Number_of_Scoring`
+df.drop(["Additional_Number_of_Scoring"], axis = 1, inplace=True)
+# Sostituisce `Total_Number_of_Reviews` e `Average_Score` con i propri valori calcolati
+df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')
+df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
+```
+
+3. Elaborazione delle colonne di recensione
+
+   1. Eliminare `Review_Total_Negative_Word_Counts`, `Review_Total_Positive_Word_Counts`, `Review_Date` e `days_since_review`
+
+   2. Mantenere `Reviewer_Score`, `Negative_Review` e `Positive_Review` così come sono
+
+   3. Conservare i `Tags` per ora
+
+   - Si faranno alcune operazioni di filtraggio aggiuntive sui tag nella prossima sezione, successivamente i tag verranno eliminati
+
+4. Elaborazione delle colonne del recensore
+
+1. Scartare `Total_Number_of_Reviews_Reviewer_Has_Given`
+
+2. Mantenere `Reviewer_Nationality`
+
+### Colonne tag
+
+Le colonne `Tag` sono problematiche in quanto si tratta di un elenco (in formato testo) memorizzato nella colonna. Purtroppo l'ordine e il numero delle sottosezioni in questa colonna non sono sempre gli stessi. È difficile per un essere umano identificare le frasi corrette a cui essere interessato, perché ci sono 515.000 righe e 1427 hotel e ognuno ha opzioni leggermente diverse che un recensore potrebbe scegliere. È qui che la NLP brilla. Si può scansionare il testo, trovare le frasi più comuni e contarle.
+
+Purtroppo non interessano parole singole, ma frasi composte da più parole (es. *Viaggio di lavoro*). L'esecuzione di un algoritmo di distribuzione della frequenza a più parole su così tanti dati (6762646 parole) potrebbe richiedere una quantità straordinaria di tempo, ma senza guardare i dati, sembrerebbe che sia una spesa necessaria. È qui che l'analisi dei dati esplorativi diventa utile, perché si è visto un esempio di tag come `["Business trip", "Solo traveler", "Single Room", "Stayed 5 nights", "Submitted from a mobile device"]` , si può iniziare a chiedersi se è possibile ridurre notevolmente l'elaborazione da fare. Fortunatamente lo è, ma prima occorre seguire alcuni passaggi per accertare i tag di interesse.
+
+### Filtraggio tag
+
+Ricordare che l'obiettivo dell'insieme di dati è aggiungere il sentiment e le colonne che aiuteranno a scegliere l'hotel migliore (per se stessi o forse per un cliente che incarica di creare un bot di raccomandazione dell'hotel). Occorre chiedersi se i tag sono utili o meno nell'insieme di dati finale. Ecco un'interpretazione (se serve l'insieme di dati per altri motivi diversi tag potrebbero rimanere dentro/fuori dalla selezione):
+
+1. Il tipo di viaggio è rilevante e dovrebbe rimanere
+2. Il tipo di gruppo di ospiti è importante e dovrebbe rimanere
+3. Il tipo di camera, suite o monolocale in cui ha soggiornato l'ospite è irrilevante (tutti gli hotel hanno praticamente le stesse stanze)
+4. Il dispositivo su cui è stata inviata la recensione è irrilevante
+5. Il numero di notti in cui il recensore ha soggiornato *potrebbe* essere rilevante se si attribuisce a soggiorni più lunghi un gradimento maggiore per l'hotel, ma è una forzatura e probabilmente irrilevante
+
+In sintesi, si **mantengono 2 tipi di tag e si rimuove il resto**.
+
+Innanzitutto, non si vogliono contare i tag finché non sono in un formato migliore, quindi ciò significa rimuovere le parentesi quadre e le virgolette. Si può fare in diversi modi, ma serve il più veloce in quanto potrebbe richiedere molto tempo per elaborare molti dati. Fortunatamente, pandas ha un modo semplice per eseguire ciascuno di questi passaggi.
+
+```Python
+# Rimuove le parentesi quadre di apertura e chiusura
+df.Tags = df.Tags.str.strip("[']")
+# rimuove anche tutte le virgolette
+df.Tags = df.Tags.str.replace(" ', '", ",", regex = False)
+```
+
+Ogni tag diventa qualcosa come: `Business trip, Solo traveler, Single Room, Stayed 5 nights, Submitted from a mobile device`.
+
+Successivamente si manifesta un problema. Alcune recensioni, o righe, hanno 5 colonne, altre 3, altre 6. Questo è il risultato di come è stato creato l'insieme di dati ed è difficile da risolvere. Si vuole ottenere un conteggio della frequenza di ogni frase, ma sono in ordine diverso in ogni recensione, quindi il conteggio potrebbe essere disattivato e un hotel potrebbe non ricevere un tag assegnato per ciò che meritava.
+
+Si utilizzerà invece l'ordine diverso a proprio vantaggio, perché ogni tag è composto da più parole ma anche separato da una virgola! Il modo più semplice per farlo è creare 6 colonne temporanee con ogni tag inserito nella colonna corrispondente al suo ordine nel tag. Quindi si uniscono le 6 colonne in una grande colonna e si esegue il metodo `value_counts()` sulla colonna risultante. Stampandolo, si vedrà che c'erano 2428 tag univoci. Ecco un piccolo esempio:
+
+| Tag                            | Count  |
+| ------------------------------ | ------ |
+| Leisure trip                   | 417778 |
+| Submitted from a mobile device | 307640 |
+| Couple                         | 252294 |
+| Stayed 1 night                 | 193645 |
+| Stayed 2 nights                | 133937 |
+| Solo traveler                  | 108545 |
+| Stayed 3 nights                | 95821  |
+| Business trip                  | 82939  |
+| Group                          | 65392  |
+| Family with young children     | 61015  |
+| Stayed 4 nights                | 47817  |
+| Double Room                    | 35207  |
+| Standard Double Room           | 32248  |
+| Superior Double Room           | 31393  |
+| Family with older children     | 26349  |
+| Deluxe Double Room             | 24823  |
+| Double or Twin Room            | 22393  |
+| Stayed 5 nights                | 20845  |
+| Standard Double or Twin Room   | 17483  |
+| Classic Double Room            | 16989  |
+| Superior Double or Twin Room   | 13570  |
+| 2 rooms                        | 12393  |
+
+Alcuni dei tag comuni come `Submitted from a mobile device` non sono di alcuna utilità, quindi potrebbe essere una cosa intelligente rimuoverli prima di contare l'occorrenza della frase, ma è un'operazione così veloce che si possono lasciare e ignorare.
+
+### Rimozione della durata dai tag di soggiorno
+
+La rimozione di questi tag è il passaggio 1, riduce leggermente il numero totale di tag da considerare. Notare che non si rimuovono dall'insieme di dati, si sceglie semplicemente di rimuoverli dalla considerazione come valori da contare/mantenere nell'insieme di dati delle recensioni.
+
+| Length of stay   | Count  |
+| ---------------- | ------ |
+| Stayed 1 night   | 193645 |
+| Stayed  2 nights | 133937 |
+| Stayed 3 nights  | 95821  |
+| Stayed  4 nights | 47817  |
+| Stayed 5 nights  | 20845  |
+| Stayed  6 nights | 9776   |
+| Stayed 7 nights  | 7399   |
+| Stayed  8 nights | 2502   |
+| Stayed 9 nights  | 1293   |
+| ...              | ...    |
+
+C'è una grande varietà di camere, suite, monolocali, appartamenti e così via. Significano tutti più o meno la stessa cosa e non sono rilevanti allo scopo, quindi si rimuovono dalla considerazione.
+
+| Type of room                  | Count |
+| ----------------------------- | ----- |
+| Double Room                   | 35207 |
+| Standard  Double Room         | 32248 |
+| Superior Double Room          | 31393 |
+| Deluxe  Double Room           | 24823 |
+| Double or Twin Room           | 22393 |
+| Standard  Double or Twin Room | 17483 |
+| Classic Double Room           | 16989 |
+| Superior  Double or Twin Room | 13570 |
+
+Infine, e questo è delizioso (perché non ha richiesto molta elaborazione), rimarranno i seguenti tag *utili*:
+
+| Tag                                           | Count  |
+| --------------------------------------------- | ------ |
+| Leisure trip                                  | 417778 |
+| Couple                                        | 252294 |
+| Solo  traveler                                | 108545 |
+| Business trip                                 | 82939  |
+| Group (combined with Travellers with friends) | 67535  |
+| Family with young children                    | 61015  |
+| Family  with older children                   | 26349  |
+| With a  pet                                   | 1405   |
+
+Si potrebbe obiettare che `Travellers with friends` (Viaggiatori con amici) è più o meno lo stesso di `Group` (Gruppo), e sarebbe giusto combinare i due come fatto sopra. Il codice per identificare i tag corretti è [il notebook Tags](../solution/1-notebook.ipynb).
+
+Il passaggio finale consiste nel creare nuove colonne per ciascuno di questi tag. Quindi, per ogni riga di recensione, se la colonna `Tag` corrisponde a una delle nuove colonne, aggiungere 1, in caso contrario aggiungere 0. Il risultato finale sarà un conteggio di quanti recensori hanno scelto questo hotel (in aggregato) per, ad esempio, affari o piacere, o per portare un animale domestico, e questa è un'informazione utile quando consiglia un hotel.
+
+```python
+# Elabora Tags in nuove colonne
+# Il file Hotel_Reviews_Tags.py, identifica i tag più importanti
+# Leisure trip, Couple, Solo traveler, Business trip, Group combinato con Travelers with friends,
+# Family with young children, Family with older children, With a pet
+df["Leisure_trip"] = df.Tags.apply(lambda tag: 1 if "Leisure trip" in tag else 0)
+df["Couple"] = df.Tags.apply(lambda tag: 1 if "Couple" in tag else 0)
+df["Solo_traveler"] = df.Tags.apply(lambda tag: 1 if "Solo traveler" in tag else 0)
+df["Business_trip"] = df.Tags.apply(lambda tag: 1 if "Business trip" in tag else 0)
+df["Group"] = df.Tags.apply(lambda tag: 1 if "Group" in tag or "Travelers with friends" in tag else 0)
+df["Family_with_young_children"] = df.Tags.apply(lambda tag: 1 if "Family with young children" in tag else 0)
+df["Family_with_older_children"] = df.Tags.apply(lambda tag: 1 if "Family with older children" in tag else 0)
+df["With_a_pet"] = df.Tags.apply(lambda tag: 1 if "With a pet" in tag else 0)
+
+```
+
+### Salvare il file.
+
+Infine, salvare l'insieme di dati così com'è ora con un nuovo nome.
+
+```python
+df.drop(["Review_Total_Negative_Word_Counts", "Review_Total_Positive_Word_Counts", "days_since_review", "Total_Number_of_Reviews_Reviewer_Has_Given"], axis = 1, inplace=True)
+
+# Salvataggio del nuovo file dati con le colonne calcolate
+print("Saving results to Hotel_Reviews_Filtered.csv")
+df.to_csv(r'../data/Hotel_Reviews_Filtered.csv', index = False)
+```
+
+## Operazioni di Analisi del Sentiment
+
+In questa sezione finale, si applicherà l'analisi del sentiment alle colonne di recensione e si salveranno i risultati in un insieme di dati.
+
+## Esercizio: caricare e salvare i dati filtrati
+
+Tenere presente che ora si sta caricando l'insieme di dati filtrato che è stato salvato nella sezione precedente, **non** quello originale.
+
+```python
+import time
+import pandas as pd
+import nltk as nltk
+from nltk.corpus import stopwords
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+nltk.download('vader_lexicon')
+
+# Carica le recensioni di hotel filtrate dal CSV
+df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')
+
+# Il proprio codice andrà aggiunto qui
+
+
+# Infine ricordarsi di salvare le recensioni di hotel con i nuovi dati NLP aggiunti
+print("Saving results to Hotel_Reviews_NLP.csv")
+df.to_csv(r'../data/Hotel_Reviews_NLP.csv', index = False)
+```
+
+### Rimozione delle stop word
+
+Se si dovesse eseguire l'analisi del sentiment sulle colonne delle recensioni negative e positive, potrebbe volerci molto tempo. Testato su un potente laptop di prova con CPU veloce, ci sono voluti 12 - 14 minuti a seconda della libreria di sentiment utilizzata. È un tempo (relativamente) lungo, quindi vale la pena indagare se può essere accelerato.
+
+Il primo passo è rimuovere le stop word, o parole inglesi comuni che non cambiano il sentiment di una frase. Rimuovendole, l'analisi del sentiment dovrebbe essere eseguita più velocemente, ma non essere meno accurata (poiché le stop word non influiscono sul sentiment, ma rallentano l'analisi).
+
+La recensione negativa più lunga è stata di 395 parole, ma dopo aver rimosso le stop word, è di 195 parole.
+
+Anche la rimozione delle stop word è un'operazione rapida, poiché la rimozione di esse da 2 colonne di recensione su 515.000 righe ha richiesto 3,3 secondi sul dispositivo di test. Potrebbe volerci un po' più o meno tempo a seconda della velocità della CPU del proprio dispositivo, della RAM, del fatto che si abbia o meno un SSD e alcuni altri fattori. La relativa brevità dell'operazione significa che se migliora il tempo di analisi del sentiment, allora vale la pena farlo.
+
+```python
+from nltk.corpus import stopwords
+
+# Carica le recensioni di hotel da CSV
+df = pd.read_csv("../../data/Hotel_Reviews_Filtered.csv")
+
+# Rimuove le stop word - potrebbe essere lento quando c'è molto testo!
+# Ryan Han (ryanxjhan su Kaggle) ha un gran post riguardo al misurare le prestazioni di diversi approcci per la rimozione delle stop word
+# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # si usa l'approccio raccomandato da Ryan
+start = time.time()
+cache = set(stopwords.words("english"))
+def remove_stopwords(review):
+    text = " ".join([word for word in review.split() if word not in cache])
+    return text
+
+# Rimuove le stop word da entrambe le colonne
+df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   
+df.Positive_Review = df.Positive_Review.apply(remove_stopwords)
+```
+
+### Esecuzione dell'analisi del sentiment
+
+Ora si dovrebbe calcolare l'analisi del sentiment per le colonne di recensioni negative e positive e memorizzare il risultato in 2 nuove colonne. Il test del sentiment sarà quello di confrontarlo con il punteggio del recensore per la stessa recensione. Ad esempio, se il sentiment ritiene che la recensione negativa abbia avuto un sentiment pari a 1 (giudizio estremamente positivo) e un sentiment positivo della recensione pari a 1, ma il recensore ha assegnato all'hotel il punteggio più basso possibile, allora il testo della recensione non corrisponde al punteggio, oppure l'analizzatore del sentiment non è stato in grado di riconoscere correttamente il sentiment. Ci si dovrebbe aspettare che alcuni punteggi del sentiment siano completamente sbagliati, e spesso ciò sarà spiegabile, ad esempio la recensione potrebbe essere estremamente sarcastica "Certo che mi è piaciuto dormire in una stanza senza riscaldamento" e l'analizzatore del sentimento pensa che sia un sentimento positivo, anche se un un lettore umano avrebbe rilevato il sarcasmo.
+
+NLTK fornisce diversi analizzatori di sentiment con cui imparare e si possono sostituire e vedere se il sentiment è più o meno accurato. Qui viene utilizzata l'analisi del sentiment di VADER.
+
+> Hutto, CJ & Gilbert, EE (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Ottava Conferenza Internazionale su Weblog e Social Media (ICWSM-14). Ann Arbor, MI, giugno 2014.
+
+```python
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Crea l'analizzatore di sentiment vader (ce ne sono altri in NLTK che si possono provare)
+vader_sentiment = SentimentIntensityAnalyzer()
+# Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
+
+# Ci sono tre possibilità di input per un recensore:
+# Potrebbe essere "No Negative", nel qual caso ritorna 0
+# Potrebbe essere "No Positive", nel qual caso ritorna 0
+# Potrebbe essere una recensione, nel qual caso calcola il sentiment
+def calc_sentiment(review):    
+    if review == "No Negative" or review == "No Positive":
+        return 0
+    return vader_sentiment.polarity_scores(review)["compound"]    
+```
+
+Più avanti nel programma, quando si è pronti per calcolare il sentiment, lo si può applicare a ciascuna recensione come segue:
+
+```python
+# Aggiunge una colonna di sentiment negativa e positiva
+print("Calculating sentiment columns for both positive and negative reviews")
+start = time.time()
+df["Negative_Sentiment"] = df.Negative_Review.apply(calc_sentiment)
+df["Positive_Sentiment"] = df.Positive_Review.apply(calc_sentiment)
+end = time.time()
+print("Calculating sentiment took " + str(round(end - start, 2)) + " seconds")
+```
+
+Questo richiede circa 120 secondi sul computer utilizzato, ma varierà per ciascun computer. Se si vogliono stampare i risultati e vedere se il sentiment corrisponde alla recensione:
+
+```python
+df = df.sort_values(by=["Negative_Sentiment"], ascending=True)
+print(df[["Negative_Review", "Negative_Sentiment"]])
+df = df.sort_values(by=["Positive_Sentiment"], ascending=True)
+print(df[["Positive_Review", "Positive_Sentiment"]])
+```
+
+L'ultima cosa da fare con il file prima di utilizzarlo nella sfida è salvarlo! Si dovrrebbe anche considerare di riordinare tutte le nuove colonne in modo che sia facile lavorarci (per un essere umano, è un cambiamento estetico).
+
+```python
+# Riordina le colonne (E' un estetismo ma facilita l'esplorazione successiva dei dati)
+df = df.reindex(["Hotel_Name", "Hotel_Address", "Total_Number_of_Reviews", "Average_Score", "Reviewer_Score", "Negative_Sentiment", "Positive_Sentiment", "Reviewer_Nationality", "Leisure_trip", "Couple", "Solo_traveler", "Business_trip", "Group", "Family_with_young_children", "Family_with_older_children", "With_a_pet", "Negative_Review", "Positive_Review"], axis=1)
+
+print("Saving results to Hotel_Reviews_NLP.csv")
+df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
+```
+
+Si dovrebbe eseguire l'intero codice per [il notebook di analisi](../solution/3-notebook.ipynb) (dopo aver eseguito [il notebook di filtraggio](../solution/1-notebook.ipynb) per generare il file Hotel_Reviews_Filtered.csv).
+
+Per riepilogare, i passaggi sono:
+
+1. Il file del'insieme di dati originale **Hotel_Reviews.csv** è stato esplorato nella lezione precedente con [il notebook explorer](../../4-Hotel-Reviews-1/solution/notebook.ipynb)
+2. Hotel_Reviews.csv viene filtrato [dal notebook di filtraggio](../solution/1-notebook.ipynb) risultante in **Hotel_Reviews_Filtered.csv**
+3. Hotel_Reviews_Filtered.csv viene elaborato dal [notebook di analisi del sentiment](../solution/3-notebook.ipynb) risultante in **Hotel_Reviews_NLP.csv**
+4. Usare Hotel_Reviews_NLP.csv nella Sfida NLP di seguito
+
+### Conclusione
+
+Quando si è iniziato, si disponeva di un insieme di dati con colonne e dati, ma non tutto poteva essere verificato o utilizzato. Si sono esplorati i dati, filtrato ciò che non serve, convertito i tag in qualcosa di utile, calcolato le proprie medie, aggiunto alcune colonne di sentiment e, si spera, imparato alcune cose interessanti sull'elaborazione del testo naturale.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/?loc=it)
+
+## Sfida
+
+Ora che si è analizzato il proprio insieme di dati per il sentiment, vedere se si possono usare le strategie apprese in questo programma di studi (clustering, forse?) per determinare modelli intorno al sentiment.
+
+## recensione e Auto Apprendimento
+
+Seguire [questo modulo di apprendimento](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa) per saperne di più e utilizzare diversi strumenti per esplorare il sentiment nel testo.
+
+## Compito
+
+[Provare un insieme di dati diverso](assignment.it.md)
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
new file mode 100644
index 000000000..da8920993
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
@@ -0,0 +1,376 @@
+# 호텔 리뷰로 감정 분석하기
+
+지금까지 자세히 데이터셋을 살펴보았으며, 열을 필터링하고 데이터셋으로 NLP 기술을 사용하여 호텔에 대한 새로운 시각을 얻게 될 시간입니다.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
+
+### 필터링 & 감정 분석 작업
+
+알고 있는 것처럼, 데이터셋에 약간의 이슈가 있었습니다. 일부 열은 필요없는 정보로 채워져있으며, 부정확해 보입니다. 만약 맞는 경우, 어떻게 계산되었는지 불투명하고, 답을 스스로 계산해서 독립적으로 확인할 수 없습니다.
+
+## 연습: 조금 더 데이터 처리하기
+
+조금 더 데이터를 정리합니다. 열을 추가하는 것은 나중에 유용하며, 다른 열에서 값을 변경하고, 특정한 열을 완전히 드랍하게 됩니다.
+
+1. 초기 column 처리합니다
+
+   1. `lat` 과 `lng`를 드랍합니다
+
+   2. `Hotel_Address` 값을 다음 값으로 치환합니다 (만약 주소에 도시와 국가가 같다면, 도시와 국가만 변경합니다).
+
+      데이터셋에서 도시와 국가만 있습니다:
+
+      Amsterdam, Netherlands
+
+      Barcelona, Spain
+
+      London, United Kingdom
+
+      Milan, Italy
+
+      Paris, France
+
+      Vienna, Austria 
+
+      ```python
+      def replace_address(row):
+          if "Netherlands" in row["Hotel_Address"]:
+              return "Amsterdam, Netherlands"
+          elif "Barcelona" in row["Hotel_Address"]:
+              return "Barcelona, Spain"
+          elif "United Kingdom" in row["Hotel_Address"]:
+              return "London, United Kingdom"
+          elif "Milan" in row["Hotel_Address"]:        
+              return "Milan, Italy"
+          elif "France" in row["Hotel_Address"]:
+              return "Paris, France"
+          elif "Vienna" in row["Hotel_Address"]:
+              return "Vienna, Austria" 
+      
+      # Replace all the addresses with a shortened, more useful form
+      df["Hotel_Address"] = df.apply(replace_address, axis = 1)
+      # The sum of the value_counts() should add up to the total number of reviews
+      print(df["Hotel_Address"].value_counts())
+      ```
+
+      지금부터 국가 레벨 데이터로 쿼리할 수 있습니다:
+
+      ```python
+      display(df.groupby("Hotel_Address").agg({"Hotel_Name": "nunique"}))
+      ```
+
+      | Hotel_Address          | Hotel_Name |
+      | :--------------------- | :--------: |
+      | Amsterdam, Netherlands |    105     |
+      | Barcelona, Spain       |    211     |
+      | London, United Kingdom |    400     |
+      | Milan, Italy           |    162     |
+      | Paris, France          |    458     |
+      | Vienna, Austria        |    158     |
+
+2. 호텔 Meta-review 열을 처리합니다
+
+  1. `Additional_Number_of_Scoring`을 드랍합니다
+
+  1. `Total_Number_of_Reviews`를 데이터셋에 실제로 있는 총 호텔 리뷰로 치환합니다
+
+  1. `Average_Score`를 계산해둔 점수로 치환합니다
+ 
+  ```python
+  # Drop `Additional_Number_of_Scoring`
+  df.drop(["Additional_Number_of_Scoring"], axis = 1, inplace=True)
+  # Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values
+  df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')
+  df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
+  ```
+
+3. 리뷰 열을 처리합니다
+
+   1. `Review_Total_Negative_Word_Counts`, `Review_Total_Positive_Word_Counts`, `Review_Date` 그리고 `days_since_review`를 드랍합니다
+
+   2. `Reviewer_Score`, `Negative_Review`, 그리고 `Positive_Review` 를 두고,
+     
+   3. 당장 `Tags` 도 둡니다
+
+     - 다음 섹션에서 태그에 추가적인 필터링 작업을 조금 진행하고 태그를 드랍하게 됩니다
+
+4. 리뷰어 열을 처리합니다
+
+  1. `Total_Number_of_Reviews_Reviewer_Has_Given`을 드랍합니다
+  
+  2. `Reviewer_Nationality`를 둡니다
+
+### Tag 열
+
+`Tag`열은 열에 저장된 (텍스트 폼의) 리스트라서 문제가 됩니다. 불행하게 순서와 열의 서브 섹션의 숫자는 항상 같지 않습니다.  515,000 행과 1427개 호텔이고, 각자 리뷰어가 선택할 수 있는 옵션은 조금씩 다르기 때문에, 사람에게 흥미로운 알맞은 문구를 가리기 힘듭니다. NLP가 빛나는 영역입니다. 텍스트를 스캔하고 가장 일반적인 문구를 찾으며, 셀 수 있습니다.
+
+불행히도, 단일 단어는 아니지만, multi-word 구문은 (예시. *Business trip*) 흥미롭습니다. 많은 데이터에 (6762646 단어) multi-word frequency distribution 알고리즘을 실행하는 것은 특별히 오래 걸릴 수 있지만, 데이터를 보지 않아도, 필요한 비용으로 보일 것입니다. `[' Business trip  ', ' Solo traveler ', ' Single Room ', ' Stayed 5 nights ', ' Submitted from  a mobile device ']` 처럼 태그 샘플로 보면, 해야 하는 처리로 많이 줄일 수 있는지 물어볼 수 있기 때문에, 탐색적 데이터 분석은 유용합니다. 운이 좋게도, - 그러나 먼저 관심있는 태그를 확실히 하고자 다음 몇 단계가 필요합니다.
+
+### tags 필터링
+
+데이터셋의 목표는 좋은 호텔을 선택할 때 (호텔 추천 봇을 만들어 달라고 맡기는 클라이언트일 수 있습니다) 도움을 받고자 감정과 열을 추가하는 것이라고 되새깁니다. 태그가 최종 데이터셋에서 유용한지 스스로에게 물어볼 필요가 있습니다. 한 가지 해석이 있습니다 (만약 다른 사유로 데이터셋이 필요한 경우에 선택할 수 있거나 안하기도 합니다):
+
+1. 여행 타입이 적절하고, 유지되어야 합니다
+2. 게스트 그룹 타입은 중요하고, 유지되어야 합니다
+3. 게스트가 지낸 룸 타입, 스위트, 또는 스튜디오 타입은 관련 없습니다 (모든 호텔은 기본적으로 같은 룸이 존재합니다) 
+4. 리뷰를 작성한 디바이스는 관련 없습니다
+5. 만약 리뷰어가 좋아하는 호텔을 더 오래 지낸다면, 리뷰어가 지낸 숙박 기간과 *관련이 있을* 수 있지만, 확대 해석이며, 아마 관련 없습니다
+
+요약하면, **2가지 종류 태그를 유지하고 나머지를 제거합니다**.
+
+먼저, 더 좋은 포맷이 될 때까지 태그를 안 세고 싶으므로, square brackets과 quotes를 제거해야 합니다. 여러 방식으로 할 수 있지만, 많은 데이터를 처리하며 오랜 시간이 걸리므로 빠르게 하길 원합니다. 운이 좋게도, pandas는 각 단계를 쉬운 방식으로 할 수 있습니다.
+
+```Python
+# Remove opening and closing brackets
+df.Tags = df.Tags.str.strip("[']")
+# remove all quotes too
+df.Tags = df.Tags.str.replace(" ', '", ",", regex = False)
+```
+
+각자 태그는 이처럼 이루어집니다: `Business trip, Solo traveler, Single Room, Stayed 5 nights, Submitted from a mobile device`. 
+
+다음으로 문제를 찾았습니다. 리뷰, 또는 행에 5개 열이 있고, 일부는 3개이거나, 6개입니다. 데이터셋이 어떻게 만들어졌는가에 따른 결과이며, 고치기 어렵습니다. 각 구문의 빈도 카운트를 얻고 싶지만, 각 리뷰의 순서가 다르므로, 카운트에 벗어날 수 있고, 호텔이 가치가 있는 태그로 할당받지 못할 수 있습니다.
+
+각 태그는 multi-word 지만 쉼표로 구분되어 있기 때문에, 대신 다른 순서로 사용하며 가산점을 받습니다! 간단한 방식은 태그에서 순서와 일치하는 열에 넣은 각 태그로 6개 임시 열을 만드는 것입니다. 6개 열을 하나의 큰 열로 합치고 결과 열에 `value_counts()` 메소드를 실행할 수 있습니다. 출력하면, 2428개 유니크 태그를 보게 됩니다. 여기 작은 샘플이 있습니다:
+
+| Tag                            | Count  |
+| ------------------------------ | ------ |
+| Leisure trip                   | 417778 |
+| Submitted from a mobile device | 307640 |
+| Couple                         | 252294 |
+| Stayed 1 night                 | 193645 |
+| Stayed 2 nights                | 133937 |
+| Solo traveler                  | 108545 |
+| Stayed 3 nights                | 95821  |
+| Business trip                  | 82939  |
+| Group                          | 65392  |
+| Family with young children     | 61015  |
+| Stayed 4 nights                | 47817  |
+| Double Room                    | 35207  |
+| Standard Double Room           | 32248  |
+| Superior Double Room           | 31393  |
+| Family with older children     | 26349  |
+| Deluxe Double Room             | 24823  |
+| Double or Twin Room            | 22393  |
+| Stayed 5 nights                | 20845  |
+| Standard Double or Twin Room   | 17483  |
+| Classic Double Room            | 16989  |
+| Superior Double or Twin Room   | 13570  |
+| 2 rooms                        | 12393  |
+
+`Submitted from a mobile device` 같은 일부 일반적인 태그는 사용하지 못해서, phrase occurrence를 카운트하기 전에 지우는 게 똑똑할 수 있지만, 빠르게 작업하려면 그냥 두고 무시할 수 있습니다.
+
+### length of stay 태그 지우기
+
+이 태그를 지우는 것은 1단계이며, 고려할 태그의 총 개수를 약간 줄이게 됩니다. 데이터셋에서 지우지 말고, 리뷰 데이터셋에 카운트/유지할 값으로 고려할 대상에서 지우게 선택합니다.
+
+| Length of stay   | Count  |
+| ---------------- | ------ |
+| Stayed 1 night   | 193645 |
+| Stayed  2 nights | 133937 |
+| Stayed 3 nights  | 95821  |
+| Stayed  4 nights | 47817  |
+| Stayed 5 nights  | 20845  |
+| Stayed  6 nights | 9776   |
+| Stayed 7 nights  | 7399   |
+| Stayed  8 nights | 2502   |
+| Stayed 9 nights  | 1293   |
+| ...              | ...    |
+
+룸, 스위트, 스튜디오, 아파트 등 매우 다양합니다. 대부분 같은 것을 의미하고 관련 없으므로, 대상에서 지웁니다.
+
+| Type of room                  | Count |
+| ----------------------------- | ----- |
+| Double Room                   | 35207 |
+| Standard  Double Room         | 32248 |
+| Superior Double Room          | 31393 |
+| Deluxe  Double Room           | 24823 |
+| Double or Twin Room           | 22393 |
+| Standard  Double or Twin Room | 17483 |
+| Classic Double Room           | 16989 |
+| Superior  Double or Twin Room | 13570 |
+
+최종적으로, (많이 처리할 일이 없기 때문에) 즐겁게, 다음 *유용한* 태그만 남길  예정입니다:
+
+| Tag                                           | Count  |
+| --------------------------------------------- | ------ |
+| Leisure trip                                  | 417778 |
+| Couple                                        | 252294 |
+| Solo  traveler                                | 108545 |
+| Business trip                                 | 82939  |
+| Group (combined with Travellers with friends) | 67535  |
+| Family with young children                    | 61015  |
+| Family  with older children                   | 26349  |
+| With a  pet                                   | 1405   |
+
+`Travellers with friends`는 `Group`과 거의 같다고 주장할 수 있어서, 둘을 합치면 공평할 것입니다. 올바른 태그로 식별하기 위한 코드는 [the Tags notebook](../solution/1-notebook.ipynb)에 있습니다.
+
+마지막 단계는 각 태그로 새로운 열을 만드는 것입니다. 그러면, 모든 리뷰 행에서, `Tag` 열이 하나의 새로운 열과 매치되면, 1을 추가하고, 아니면, 0을 추가합니다. 마지막 결과는 비지니스 vs 레저, 또는 애완동물 동반 언급하면서, 호텔 추천할 때 유용한 정보이므로, 얼마나 많은 리뷰어가 (총계) 호텔을 선택했는지 카운트합니다.
+
+```python
+# Process the Tags into new columns
+# The file Hotel_Reviews_Tags.py, identifies the most important tags
+# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, 
+# Family with young children, Family with older children, With a pet
+df["Leisure_trip"] = df.Tags.apply(lambda tag: 1 if "Leisure trip" in tag else 0)
+df["Couple"] = df.Tags.apply(lambda tag: 1 if "Couple" in tag else 0)
+df["Solo_traveler"] = df.Tags.apply(lambda tag: 1 if "Solo traveler" in tag else 0)
+df["Business_trip"] = df.Tags.apply(lambda tag: 1 if "Business trip" in tag else 0)
+df["Group"] = df.Tags.apply(lambda tag: 1 if "Group" in tag or "Travelers with friends" in tag else 0)
+df["Family_with_young_children"] = df.Tags.apply(lambda tag: 1 if "Family with young children" in tag else 0)
+df["Family_with_older_children"] = df.Tags.apply(lambda tag: 1 if "Family with older children" in tag else 0)
+df["With_a_pet"] = df.Tags.apply(lambda tag: 1 if "With a pet" in tag else 0)
+
+```
+
+### 파일 저장하기
+
+마지막으로, 새로운 이름으로 바로 데이터셋을 저장합니다.
+
+```python
+df.drop(["Review_Total_Negative_Word_Counts", "Review_Total_Positive_Word_Counts", "days_since_review", "Total_Number_of_Reviews_Reviewer_Has_Given"], axis = 1, inplace=True)
+
+# Saving new data file with calculated columns
+print("Saving results to Hotel_Reviews_Filtered.csv")
+df.to_csv(r'../data/Hotel_Reviews_Filtered.csv', index = False)
+```
+
+## 감정 분석 작업
+
+마지막 섹션에서, 리뷰 열에 감정 분석을 적용하고 데이터셋에 결과를 저장힙니다.
+
+## 연습: 필터링된 데이터를 불러오고 저장하기
+
+지금 원본 데이터셋 *말고*, 이전 색션에서 저장했던 필터링된 데이터셋을 불러오고 있습니다.
+
+```python
+import time
+import pandas as pd
+import nltk as nltk
+from nltk.corpus import stopwords
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+nltk.download('vader_lexicon')
+
+# Load the filtered hotel reviews from CSV
+df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')
+
+# You code will be added here
+
+
+# Finally remember to save the hotel reviews with new NLP data added
+print("Saving results to Hotel_Reviews_NLP.csv")
+df.to_csv(r'../data/Hotel_Reviews_NLP.csv', index = False)
+```
+
+### stop word 제거하기
+
+만약 긍정적이고 부정적인 리뷰 열에 감정 분석을 하는 경우, 오랜 시간이 걸릴 수 있습니다. 빠른 CPU를 가진 강력한 노트북으로 테스트하면, 사용한 감정 라이브러리에 따라서 12 - 14 분 정도 걸립니다. (상대적)으로 오래 걸려서, 빠르게 할 수 있는지 알아볼 가치가 있습니다.
+
+문장의 감정을 바꾸지 않는 stop word나, 일반적인 영어 단어를 지우는 것은, 첫 단계입니다. 지우게 된다면, 감정 분석이 더 빠르게 되지만, 정확도가 낮아지지 않습니다 (stop word는 감정에 영향없지만, 분석이 느려집니다).
+
+긴 부정적 리뷰는 395 단어로 었지만 , stop word를 지우면, 195 단어만 남습니다.
+
+stop word를 지우는 것은 빨라서, 테스트 디바이스에서 515,000 행이 넘는 2개 리뷰 열에 stop word를 지우면 3.3초 걸립니다. 디바이스의 CPU 속도, 램, SSD 등에 따라 더 오래 걸리거나 빨리 끝날 수 있습니다. 작업이 상대적으로 빨라지고 감정 분석 시간도 향상시킬 수 있다면, 할 가치가 있음을 의미합니다.
+
+```python
+from nltk.corpus import stopwords
+
+# Load the hotel reviews from CSV
+df = pd.read_csv("../../data/Hotel_Reviews_Filtered.csv")
+
+# Remove stop words - can be slow for a lot of text!
+# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches
+# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends
+start = time.time()
+cache = set(stopwords.words("english"))
+def remove_stopwords(review):
+    text = " ".join([word for word in review.split() if word not in cache])
+    return text
+
+# Remove the stop words from both columns
+df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   
+df.Positive_Review = df.Positive_Review.apply(remove_stopwords)
+```
+
+### 감정 분석하기
+
+지금부터 모든 부정적이고 긍정적인 리뷰 열에 대한 감정 분석을 계산하고, 2개 열에 결과를 저장해야 합니다. 감정 테스트는 같은 리뷰로 리뷰어의 점수를 비교할 예정입니다. 예시로, 만약 부정적인 리뷰가 1 (많이 긍정적인 감정) 감정이고 1 긍정적인 리뷰 감정이라고 감정을 내렸지만, 리뷰어가 낮은 점수로 호텔을 리뷰하면, 리뷰 텍스트가 점수와 어느 것도 매치되지 않거나, sentiment analyser가 감정을 똑바로 인식할 수 없습니다. 일부 감정 점수는 다 틀릴 수 있고, 그 이유를 자주 설명할 수 있습니다. 예시로. "Of course I LOVED sleeping in a room with no heating" 리뷰는 극도로 풍자적이고 sentiment analyser는 긍정적인 감정이라고 생각하지만, 사람이 읽으면 풍자라는 것을 알 수 있습니다.
+
+NLTK는 학습하는 다양한 sentiment analyzer를 제공하고, 이를 대신헤서 감정이 얼마나 정확한지 볼 수 있습니다. VADER sentiment analysis를 여기에서 사용했습니다. 
+
+> Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
+
+```python
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Create the vader sentiment analyser (there are others in NLTK you can try too)
+vader_sentiment = SentimentIntensityAnalyzer()
+# Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
+
+# There are 3 possibilities of input for a review:
+# It could be "No Negative", in which case, return 0
+# It could be "No Positive", in which case, return 0
+# It could be a review, in which case calculate the sentiment
+def calc_sentiment(review):    
+    if review == "No Negative" or review == "No Positive":
+        return 0
+    return vader_sentiment.polarity_scores(review)["compound"]    
+```
+
+이후에 프로그램에서 감정을 계산하려 준비할 때, 다음 각 리뷰에서 적용할 수 있습니다:
+
+```python
+# Add a negative sentiment and positive sentiment column
+print("Calculating sentiment columns for both positive and negative reviews")
+start = time.time()
+df["Negative_Sentiment"] = df.Negative_Review.apply(calc_sentiment)
+df["Positive_Sentiment"] = df.Positive_Review.apply(calc_sentiment)
+end = time.time()
+print("Calculating sentiment took " + str(round(end - start, 2)) + " seconds")
+```
+
+이 컴퓨터에서 120초 정도 걸리지만, 각자 컴퓨터마다 다릅니다. 만약 결과를 출력하고 감정이 리뷰와 매치되는지 보려면 아래와 같이 진행합니다:
+
+```python
+df = df.sort_values(by=["Negative_Sentiment"], ascending=True)
+print(df[["Negative_Review", "Negative_Sentiment"]])
+df = df.sort_values(by=["Positive_Sentiment"], ascending=True)
+print(df[["Positive_Review", "Positive_Sentiment"]])
+```
+
+마지막으로 할 일은 도전에서 사용하기 전, 파일을 저장하는 것입니다! 쉽게 작업하도록 모든 새로운 열로 다시 정렬을 (사람인 경우, 외형 변경) 고려해야 합니다.
+
+```python
+# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)
+df = df.reindex(["Hotel_Name", "Hotel_Address", "Total_Number_of_Reviews", "Average_Score", "Reviewer_Score", "Negative_Sentiment", "Positive_Sentiment", "Reviewer_Nationality", "Leisure_trip", "Couple", "Solo_traveler", "Business_trip", "Group", "Family_with_young_children", "Family_with_older_children", "With_a_pet", "Negative_Review", "Positive_Review"], axis=1)
+
+print("Saving results to Hotel_Reviews_NLP.csv")
+df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
+```
+
+(Hotel_Reviews_Filtered.csv 파일 만들어서 [your filtering notebook](../solution/1-notebook.ipynb) 실행한 후에) [the analysis notebook](../solution/3-notebook.ipynb)으로 전체 코드를 실행해야 합니다.
+
+검토하는, 단계는 이렇습니다:
+
+1. 원본 데이터셋 **Hotel_Reviews.csv** 파일은 이전 강의에서 [the explorer notebook](../../4-Hotel-Reviews-1/solution/notebook.ipynb)으로 살펴보았습니다
+2. Hotel_Reviews.csv는 [the filtering notebook](../solution/1-notebook.ipynb)에서 필터링되고 **Hotel_Reviews_Filtered.csv**에 결과로 남습니다
+3. Hotel_Reviews_Filtered.csv는 [the sentiment analysis notebook](../solution/3-notebook.ipynb)에서 처리되어 **Hotel_Reviews_NLP.csv**에 결과로 남습니다
+4. 다음 NLP 도전에서 Hotel_Reviews_NLP.csv를 사용합니다
+
+### 결론
+
+시작했을 때, 열과 데이터로 이루어진 데이터셋이 었었지만 모두 다 확인되거나 사용되지 않았습니다. 데이터를 살펴보았으며, 필요없는 것은 필터링해서 지웠고, 유용하게 태그를 변환했고, 평균을 계산했으며, 일부 감정 열을 추가하고 기대하면서, 자연어 처리에 대한 일부 흥미로운 사실을 학습했습니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
+
+## 도전
+
+이제부터 감정을 분석해둔 데이터셋을 가지고 있으므로, 이 커리큘럼 (clustering, perhaps?)에서 배웠던 전략으로 감정 주변 패턴을 결정해봅니다.
+
+## 검토 & 자기주도 학습
+
+[this Learn module](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa)로 더 배우고 다른 도구도 사용해서 텍스트에서 감정을 찾습니다.
+
+## 과제 
+
+[Try a different dataset](../assignment.md)
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md b/6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md
new file mode 100644
index 000000000..dae727b78
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Provare un insieme di dati diverso
+
+## Istruzioni
+
+Ora che si è imparato a usare NLTK per assegnare sentiment al testo, provare un insieme di dati diverso. Probabilmente si dovranno elaborare dei dati attorno ad esso, quindi creare un notebook e documentare il proprio processo di pensiero. Cosa si è scoperto?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------------------------------------------------- | ----------------------------------------- | ---------------------- |
+|          | Vengono presentati un notebook completo e un insieme di dati con celle ben documentate che spiegano come viene assegnato il sentiment | Il notebook manca di buone spiegazioni | Il notebook è difettoso |
diff --git a/6-NLP/README.md b/6-NLP/README.md
index d875929eb..d7dfab09f 100644
--- a/6-NLP/README.md
+++ b/6-NLP/README.md
@@ -1,6 +1,6 @@
 # Getting started with natural language processing
 
-Natural language processing, NLP, is a subfield of artificial intelligence. The whole field is directed at helping machines understand and process the human language. This can then be used to perform tasks like spell check or machine translation.
+Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written -- referred to as natural language. It is a component of artificial intelligence (AI). NLP has existed for more than 50 years and has roots in the field of linguistics. The whole field is directed at helping machines understand and process the human language. This can then be used to perform tasks like spell check or machine translation. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence.
 
 ## Regional topic: European languages and literature and romantic hotels of Europe ❤️
 
diff --git a/6-NLP/translations/README.it.md b/6-NLP/translations/README.it.md
new file mode 100644
index 000000000..13eb3226e
--- /dev/null
+++ b/6-NLP/translations/README.it.md
@@ -0,0 +1,24 @@
+# Iniziare con l'elaborazione del linguaggio naturale
+
+L'elaborazione del linguaggio naturale, NLP, è un sottocampo dell'intelligenza artificiale. L'intero campo è volto ad aiutare le macchine a comprendere ed elaborare il linguaggio umano. Questo può quindi essere utilizzato per eseguire attività come il controllo ortografico o la traduzione automatica.
+
+## Argomento regionale: lingue e letterature europee e hotel romantici d'Europa ❤️
+
+In questa sezione del programma di studi, verrà presentato uno degli usi più diffusi di machine learning: l'elaborazione del linguaggio naturale (NLP). Derivato dalla linguistica computazionale, questa categoria di intelligenza artificiale è il ponte tra umani e macchine tramite la comunicazione vocale o testuale.
+
+In queste lezioni si impareranno le basi di NLP costruendo piccoli bot conversazionali per imparare come machine learning aiuti a rendere queste conversazioni sempre più "intelligenti". Si viaggerà indietro nel tempo, chiacchierando con Elizabeth Bennett e Mr. Darcy dal romanzo classico di Jane Austen, **Orgoglio e pregiudizio**, pubblicato nel 1813. Quindi, si approfondiranno le proprie conoscenze imparando l'analisi del sentiment tramite le recensioni di hotel in Europa.
+
+![Libro e tè di Orgoglio e Pregiudizio](../images/p&p.jpg)
+> Foto di <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> su <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Lezioni
+
+1. [Introduzione all'elaborazione del linguaggio naturale](../1-Introduction-to-NLP/translations/README.it.md)
+2. [Compiti e tecniche comuni di NLP](../2-Tasks/translations/README.it.md)
+3. [Traduzione e analisi del sentiment con machine learning](../3-Translation-Sentiment/translations/README.it.md)
+4. [Preparazione dei dati](../4-Hotel-Reviews-1/translations/README.it.md)
+5. [NLTK per l'analisi del sentiment](../5-Hotel-Reviews-2/translations/README.it.md)
+
+## Crediti
+
+Queste lezioni sull'elaborazione del linguaggio naturale sono state scritte con ☕ da [Stephen Howell](https://twitter.com/Howell_MSFT)
diff --git a/6-NLP/translations/README.ko.md b/6-NLP/translations/README.ko.md
new file mode 100644
index 000000000..f80c6ab30
--- /dev/null
+++ b/6-NLP/translations/README.ko.md
@@ -0,0 +1,24 @@
+# Natural language processing 시작하기
+
+Natural language processing, 즉 NLP는, 인공지능의 하위 필드입니다. 전체 필드는 기계가 인간의 언어를 이해하고 처리하는 것을 도와줍니다. 맞춤법 검사 또는 기게 번역과 같은 작업을 수행할 때 쓸 수 있습니다.
+
+## 지역 토픽: 유럽 언어와 문학 그리고 유럽의 로맨틱 호텔 ❤️
+
+커리큘럼의 섹션에서, 머신러닝의 가장 널리 퍼진 사용법 중 하나를 소개합니다: natural language processing (NLP). 전산 언어학에서 나온, 인공지능 카테고리는 음성 또는 텍스트 통신으로 인간과 기계 사이를 연결합니다.
+
+이 강의에서 머신러닝이 이 대화를 점차 'smart'하게 만드는 방식을 배우고자 작은 대화 봇을 만들어서 NLP의 기본을 배울 예정입니다. 1813년에 출판한, Jane Austen의 고전 소설, **Pride and Prejudice**에, Elizabeth Bennett과 Mr. Darcy 같이 이야기하며 과거로 시간 여행을 하게 됩니다. 그러면, 유럽의 호텔 리뷰로 감정 분석을 배우면서 지식을 발전시킬 예정입니다.
+
+![Pride and Prejudice book and tea](../images/p&p.jpg)
+> Photo by <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> on <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## 강의
+
+1. [Natural language processing 소개하기](../1-Introduction-to-NLP/translations/README.ko.md)
+2. [일반적 NLP 작업과 기술](../2-Tasks/translations/README.ko.md)
+3. [머신러닝으로 번역과 감정 분석하기](../3-Translation-Sentiment/translations/README.ko.md)
+4. [데이터 준비하기](../4-Hotel-Reviews-1/translations/README.ko.md)
+5. [감정 분석을 위한 NLTK](../5-Hotel-Reviews-2/translations/README.ko.md)
+
+## 크래딧 
+
+These natural language processing lessons were written with ☕ by [Stephen Howell](https://twitter.com/Howell_MSFT)
diff --git a/6-NLP/translations/README.zh-cn.md b/6-NLP/translations/README.zh-cn.md
new file mode 100644
index 000000000..a594b5a07
--- /dev/null
+++ b/6-NLP/translations/README.zh-cn.md
@@ -0,0 +1,24 @@
+# 自然语言处理入门
+
+自然语言处理 (NLP) 是人工智能的一个子领域，主要研究如何让机器理解和处理人类语言，并用它来执行拼写检查或机器翻译等任务。
+
+## 本节主题：欧洲语言文学和欧洲浪漫酒店 ❤️
+
+在这部分课程中，您将了解机器学习最广泛的用途之一：自然语言处理 (NLP)。源自计算语言学，这一类人工智能会通过语音或文本与人类交流，建立连接人与机器的桥梁。
+
+课程中，我们将通过构建小型对话机器人来学习 NLP 的基础知识，以了解机器学习是如何使这个机器人越来越“智能”。您将穿越回 1813 年，与简·奥斯汀的经典小说 **傲慢与偏见** 中的 Elizabeth Bennett 和 Mr. Darcy 聊天（该小说于 1813 年出版）。然后，您将通过欧洲的酒店评论来进一步学习情感分析。
+
+![傲慢与偏见之书，和茶](../images/p&p.jpg)
+> <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">由 Elaine Howlin 拍摄，</a> 来自 <a href="https://unsplash.com/s/照片/傲慢与偏见？utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## 课程
+
+1. [自然语言处理简介](../1-Introduction-to-NLP/translations/README.zh-cn.md)
+2. [NLP 常见任务与技巧](../2-Tasks/README.md)
+3. [机器学习翻译和情感分析](../3-Translation-Sentiment/README.md)
+4. [准备数据](../4-Hotel-Reviews-1/README.md)
+5. [用于情感分析的工具：NLTK](../5-Hotel-Reviews-2/README.md)
+
+## 作者
+
+这些自然语言处理课程由 [Stephen Howell](https://twitter.com/Howell_MSFT) 用 ☕ 编写
diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md
index ae2a69e1c..de17f4547 100644
--- a/7-TimeSeries/1-Introduction/README.md
+++ b/7-TimeSeries/1-Introduction/README.md
@@ -10,7 +10,7 @@ In this lesson and the following one, you will learn a bit about time series for
 
 > 🎥 Click the image above for a video about time series forecasting
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/41/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/)
 
 It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights to better predict future performance, time series forecasting remains a field greatly informed by classic ML techniques.
 
@@ -40,7 +40,7 @@ In mathematics, "a time series is a series of data points indexed (or listed or
 
 Time series analysis, is the analysis of the above mentioned time series data. Time series data can take distinct forms, including 'interrupted time series' which detects patterns in a time series' evolution before and after an interrupting event. The type of analysis needed for the time series, depends on the nature of the data. Time series data itself can take the form of series of numbers or characters.
 
-The analysis to be performed, uses a variety of methods, including frequency-domain and time-domain, linear and nonlinear, and more. [Learn more](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm) about the may ways to analyze this type of data.
+The analysis to be performed, uses a variety of methods, including frequency-domain and time-domain, linear and nonlinear, and more. [Learn more](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm) about the many ways to analyze this type of data.
 
 🎓 **Time series forecasting**
 
@@ -174,7 +174,7 @@ In the next lesson, you will create an ARIMA model to create some forecasts.
 
 Make a list of all the industries and areas of inquiry you can think of that would benefit from time series forecasting. Can you think of an application of these techniques in the arts? In Econometrics? Ecology? Retail? Industry? Finance? Where else?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/42/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/)
 
 ## Review & Self Study
 
diff --git a/7-TimeSeries/1-Introduction/solution/Julia/README.md b/7-TimeSeries/1-Introduction/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/7-TimeSeries/1-Introduction/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/7-TimeSeries/1-Introduction/solution/R/README.md b/7-TimeSeries/1-Introduction/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/7-TimeSeries/1-Introduction/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/7-TimeSeries/1-Introduction/translations/README.it.md b/7-TimeSeries/1-Introduction/translations/README.it.md
new file mode 100644
index 000000000..9c7830d90
--- /dev/null
+++ b/7-TimeSeries/1-Introduction/translations/README.it.md
@@ -0,0 +1,185 @@
+# Introduzione alla previsione delle serie temporali
+
+![Riepilogo delle serie temporali in uno sketchnote](../../../sketchnotes/ml-timeseries.png)
+
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+In questa lezione e nella successiva si imparerà qualcosa sulla previsione delle serie temporali, una parte interessante e preziosa del repertorio di uno scienziato ML che è un po' meno conosciuta rispetto ad altri argomenti. La previsione delle serie temporali è una sorta di "sfera di cristallo": sulla base delle prestazioni passate di una variabile come il prezzo, è possibile prevederne il valore potenziale futuro.
+
+[![Introduzione alla previsione delle serie temporali](https://img.youtube.com/vi/cBojo1hsHiI/0.jpg)](https://youtu.be/cBojo1hsHiI "Introduzione alla previsione delle serie temporali")
+
+> 🎥 Fare clic sull'immagine sopra per un video sulla previsione delle serie temporali
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/?loc=it)
+
+È un campo utile e interessante con un valore reale per il business, data la sua applicazione diretta a problemi di prezzi, inventario e problemi della catena di approvvigionamento. Mentre le tecniche di deep learning hanno iniziato a essere utilizzate per acquisire maggiori informazioni per prevedere meglio le prestazioni future, la previsione delle serie temporali rimane un campo ampiamente informato dalle tecniche classiche di ML.
+
+> Un utile programma di studio delle serie temporali di Penn State può essere trovato [qui](https://online.stat.psu.edu/stat510/lesson/1)
+
+## Introduzione
+
+Si supponga di mantenere una serie di parchimetri intelligenti che forniscono dati su quanto spesso vengono utilizzati e per quanto nel corso del tempo.
+
+> Se si potesse prevedere, in base alle prestazioni passate del contatore, il suo valore futuro secondo le leggi della domanda e dell'offerta?
+
+Prevedere con precisione quando agire per raggiungere il proprio obiettivo è una sfida che potrebbe essere affrontata dalla previsione delle serie temporali. Non renderebbe le persone felici di pagare di più nei periodi di punta quando cercano un parcheggio, ma sarebbe un modo sicuro per generare entrate per pulire le strade!
+
+Si esplorano alcuni dei tipi di algoritmi di serie temporali e si avvia un notebook per pulire e preparare alcuni dati. I dati che saranno analizzati sono tratti dal concorso di previsione GEFCom2014. Consiste in 3 anni di carico orario di elettricità e valori di temperatura tra il 2012 e il 2014. Dati i modelli storici del carico elettrico e della temperatura, è possibile prevedere i valori futuri del carico elettrico.
+
+In questo esempio si imparerà a fare previsioni un passo avanti, utilizzando solo i dati di caricamento storici. Prima di iniziare, però, è utile capire cosa succede dietro le quinte.
+
+## Definizioni
+
+Quando si incontra il termine "serie temporale" è necessario comprenderne l'uso in diversi contesti.
+
+🎓 **Serie temporali**
+
+In matematica, "una serie temporale è una serie di punti dati indicizzati (o elencati o rappresentati graficamente) in ordine temporale". Più comunemente, una serie temporale è una sequenza presa in punti successivi equidistanti nel tempo. Un esempio di una serie temporale è il valore di chiusura giornaliero del [Dow Jones Industrial Average](https://it.wikipedia.org/wiki/Serie_storica). L'uso di grafici di serie temporali e modelli statistici si riscontra frequentemente nell'elaborazione del segnale, nelle previsioni meteorologiche, nella previsione dei terremoti e in altri campi in cui si verificano eventi e i punti dati possono essere tracciati nel tempo.
+
+🎓 **Analisi delle serie temporali**
+
+L'analisi delle serie temporali è l'analisi dei dati delle serie temporali sopra menzionati. I dati delle serie temporali possono assumere forme distinte, comprese le "serie temporali interrotte" che rilevano i modelli nell'evoluzione di una serie temporale prima e dopo un evento di interruzione. Il tipo di analisi necessaria per le serie temporali dipende dalla natura dei dati. I dati delle serie temporali possono assumere la forma di serie di numeri o caratteri.
+
+L'analisi da eseguire utilizza una varietà di metodi, tra cui dominio della frequenza e dominio del tempo, lineare e non lineare e altro ancora. Per [saperne di più](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm) sui molti modi per analizzare questo tipo di dati.
+
+🎓 **Previsione delle serie temporali**
+
+La previsione delle serie temporali è l'uso di un modello per prevedere i valori futuri in base ai modelli visualizzati dai dati raccolti in precedenza così come si sono verificati in passato. Sebbene sia possibile utilizzare modelli di regressione per esplorare i dati delle serie temporali, con indici temporali come x variabili su un grafico, tali dati vengono analizzati al meglio utilizzando tipi speciali di modelli.
+
+I dati delle serie temporali sono un elenco di osservazioni ordinate, a differenza dei dati che possono essere analizzati mediante regressione lineare. Il più comune è ARIMA, acronimo che sta per "Autoregressive Integrated Moving Average" (Modello autoregressivo integrato a media mobile).
+
+[I modelli ARIMA](https://online.stat.psu.edu/stat510/lesson/1/1.1) "mettono in relazione il valore attuale di una serie con i valori passati e gli errori di previsione passati". Sono più appropriati per l'analisi dei dati nel dominio del tempo, in cui i dati sono ordinati nel tempo.
+
+> Esistono diversi tipi di modelli ARIMA, [qui](https://people.duke.edu/~rnau/411arim.htm) si possono trovare ulteriori informazioni al riguardo e di cui si parlerà nella prossima lezione.
+
+Nella prossima lezione, si creerà un modello ARIMA utilizzando [Serie Temporali UYnivariate](https://itl.nist.gov/div898/handbook/pmc/section4/pmc44.htm), che si concentra su una variabile che cambia il suo valore nel tempo. Un esempio di questo tipo di dati è [questo insieme di dati](https://itl.nist.gov/div898/handbook/pmc/section4/pmc4411.htm) che registra la concentrazione mensile di C02 presso l'Osservatorio di Mauna Loa:
+
+| CO2 | YearMonth | Year | Month |
+| :----: | :-------: | :---: | :---: |
+| 330.62 | 1975.04 | 1975 | 1 |
+| 331.40 | 1975.13 | 1975 | 2 |
+| 331.87 | 1975.21 | 1975 | 3 |
+| 333.18 | 1975.29 | 1975 | 4 |
+| 333.92 | 1975.38 | 1975 | 5 |
+| 333.43 | 1975.46 | 1975 | 6 |
+| 331.85 | 1975.54 | 1975 | 7 |
+| 330.01 | 1975.63 | 1975 | 8 |
+| 328.51 | 1975.71 | 1975 | 9 |
+| 328.41 | 1975.79 | 1975 | 10 |
+| 329.25 | 1975.88 | 1975 | 11 |
+| 330.97 | 1975.96 | 1975 | 12 |
+
+✅ Identificare la variabile che cambia nel tempo in questo set di dati
+
+## [Caratteristiche dei dati](https://online.stat.psu.edu/stat510/lesson/1/1.1) delle serie temporali da considerare
+
+Quando si esaminano i dati delle serie temporali, è possibile notare che presentano determinate caratteristiche che è necessario prendere in considerazione e mitigare per comprenderne meglio i modelli. Se si considerano i dati delle serie temporali come potenziali produttori di un "segnale" che si desidera analizzare, queste caratteristiche possono essere considerate "rumore". Spesso sarà necessario ridurre questo "rumore" compensando alcune di queste caratteristiche utilizzando alcune tecniche statistiche.
+
+Ecco alcuni concetti che si dovrebbe conoscere per poter lavorare con le serie temporali:
+
+🎓 **Tendenze**
+
+Le tendenze sono definite come aumenti e diminuzioni misurabili nel tempo. [Per saperne di più](https://machinelearningmastery.com/time-series-trends-in-python). Nel contesto delle serie temporali, si tratta di come utilizzare e, se necessario, rimuovere le tendenze dalle serie temporali.
+
+🎓 **[Stagionalità](https://machinelearningmastery.com/time-series-seasonality-with-python/)**
+
+La stagionalità è definita come fluttuazioni periodiche, come le vacanze estive che potrebbero influire sulle vendite, ad esempio. [Si dia un'occhiata](https://itl.nist.gov/div898/handbook/pmc/section4/pmc443.htm) a come i diversi tipi di grafici mostrano la stagionalità nei dati.
+
+🎓 **Valori anomali**
+
+I valori anomali sono molto lontani dalla varianza dei dati standard.
+
+🎓 **Ciclo di lunga durata**
+
+Indipendentemente dalla stagionalità, i dati potrebbero mostrare un ciclo di lungo periodo come una recessione economica che dura più di un anno.
+
+🎓 **Varianza costante**
+
+Nel tempo, alcuni dati mostrano fluttuazioni costanti, come il consumo energetico giornaliero e notturno.
+
+🎓 **Cambiamenti improvvisi**
+
+I dati potrebbero mostrare un cambiamento improvviso che potrebbe richiedere un'ulteriore analisi. La brusca chiusura delle attività a causa del COVID, ad esempio, ha causato cambiamenti nei dati.
+
+✅ Ecco un [esempio di grafico della serie temporale](https://www.kaggle.com/kashnitsky/topic-9-part-1-time-series-analysis-in-python) che mostra la valuta di gioco giornaliera spesa in alcuni anni. Si riesce a identificare una delle caratteristiche sopra elencate in questi dati?
+
+![Spesa in valuta di gioco](../images/currency.png)
+
+## Esercizio: iniziare con i dati sul consumo energetico
+
+Si inizia a creare un modello di serie temporali per prevedere l'utilizzo futuro di energia dato l'utilizzo passato.
+
+> I dati in questo esempio sono presi dal concorso di previsione GEFCom2014. Consiste in 3 anni di carico orario di elettricità e valori di temperatura tra il 2012 e il 2014.
+>
+> Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli e Rob J. Hyndman, "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond", International Journal of Forecasting, vol.32, no.3, pp 896- 913, luglio-settembre 2016.
+
+1. Nella cartella `working` di questa lezione, aprire il _file_ notebook.ipynb. Iniziare aggiungendo librerie che aiuteranno a caricare e visualizzare i dati
+
+   ```python
+   import os
+   import matplotlib.pyplot as plt
+   from common.utils import load_data
+   %matplotlib inline
+   ```
+
+   Nota, si stanno utilizzando i file dalla cartella `common` inclusa che configura il proprio ambiente e gestisce il download dei dati.
+
+2. Quindi, si esaminano i dati come un dataframe chiamando `load_data()` e `head()`:
+
+   ```python
+   data_dir = './data'
+   energy = load_data(data_dir)[['load']]
+   energy.head()
+   ```
+
+   Si può vedere che ci sono due colonne che rappresentano data e carico:
+
+   |                     | load |
+   | :-----------------: | :----: |
+   | 2012-01-01 00:00:00 | 2698.0 |
+   | 2012-01-01 01:00:00 | 2558.0 |
+   | 2012-01-01 02:00:00 | 2444.0 |
+   | 2012-01-01 03:00:00 | 2402.0 |
+   | 2012-01-01 04:00:00 | 2403.0 |
+
+3. Ora, tracciare i dati chiamando `plot()`:
+
+   ```python
+   energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)
+   plt.xlabel('timestamp', fontsize=12)
+   plt.ylabel('load', fontsize=12)
+   plt.show()
+   ```
+
+   ![grafico dell'energia](../images/energy-plot.png)
+
+4. Ora, tracciare la prima settimana di luglio 2014, fornendola come input per `energy` nella forma `[from date]: [to date]`:
+
+   ```python
+   energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)
+   plt.xlabel('timestamp', fontsize=12)
+   plt.ylabel('load', fontsize=12)
+   plt.show()
+   ```
+
+   ![luglio](../images/july-2014.png)
+
+   Uno stupendo grafico! Dare un'occhiata a questi grafici e vedere se si riesce a determinare una delle caratteristiche sopra elencate. Cosa si può dedurre visualizzando i dati?
+
+Nella prossima lezione, si creerà un modello ARIMA per creare alcune previsioni.
+
+---
+
+## 🚀 Sfida
+
+Fare un elenco di tutti i settori e le aree di indagine che vengono in mente che potrebbero trarre vantaggio dalla previsione delle serie temporali. Si riesce a pensare a un'applicazione di queste tecniche nelle arti? In Econometria? Ecologia? Vendita al Dettaglio? Industria? Finanza? Dove se no?
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Sebbene non si tratteranno qui, le reti neurali vengono talvolta utilizzate per migliorare i metodi classici di previsione delle serie temporali. Si legga di più su di loro [in questo articolo](https://medium.com/microsoftazure/neural-networks-for-forecasting-financial-and-economic-time-series-6aca370ff412)
+
+## Compito
+
+[Visualizzare altre serie temporali](assignment.it.md)
diff --git a/7-TimeSeries/1-Introduction/translations/README.ko.md b/7-TimeSeries/1-Introduction/translations/README.ko.md
new file mode 100644
index 000000000..d8f04e222
--- /dev/null
+++ b/7-TimeSeries/1-Introduction/translations/README.ko.md
@@ -0,0 +1,186 @@
+# Time series forecasting 소개하기
+
+![Summary of time series in a sketchnote](../../../sketchnotes/ml-timeseries.png)
+
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+이 강의와 다음에서, 다른 토픽보다 덜 알려진 ML 사이언티스트의 레파토리 중에 흥미롭고 가치있는 파트인, time series forecasting에 대하여 약간 배우게 됩니다. Time series forecasting은 일종의 'crystal ball'입니다: 가격과 같은 값의 과거 성적에 기반해서, 미래 잠재 값을 예측할 수 있습니다.
+
+[![Introduction to time series forecasting](https://img.youtube.com/vi/cBojo1hsHiI/0.jpg)](https://youtu.be/cBojo1hsHiI "Introduction to time series forecasting")
+
+> 🎥 이미지를 눌러서 time series forecasting에 대한 비디오를 봅니다
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/)
+
+가격, 재고, 그리고 공급과 연관된 이슈에 직접 적용하게 된다면, 비지니스에 실제로 가치있는 유용하고 흥미로운 필드가 됩니다. 딥러닝 기술은 미래의 성능을 잘 예측하기 위해 더 많은 인사이트를 얻고자 사용했지만, time series forecasting은 classic ML 기술에서 지속적으로 많은 정보를 얻는 필드입니다.
+
+> Penn State의 유용한 time series 커리큘럼은 [here](https://online.stat.psu.edu/stat510/lesson/1)에서 찾을 수 있습니다
+
+## 소개
+
+시간에 따라 얼마나 자주 사용하는지 데이터를 제공하는 smart parking meters의 배열을 관리한다고 가정해봅니다.
+
+> 미터기의 과거 퍼포먼스를 기반으로, 공급과 수요의 법칙에 따라서 미래 값을, 예측할 수 있나요?
+
+목표를 이루기 위해 언제 할 지 정확히 예측하는 것은 time series forecasting으로 다룰 수 있는 도전입니다. 주차 공간을 찾을 때 바쁜 시간이면 더 많은 요금을 받아서 기분은 안 좋겠지만, 거리 청소할 수익을 벌 수 있는 확실한 방식이 될 예정입니다!
+
+time series 알고리즘 일부를 알아보면서 노트북에서 정리하고 준비한 일부 데이터로 시작합니다. 분석할 데이터는 GEFCom2014 forecasting competition에서 가져왔습니다. 2012년과 2014년 사이 시간당 전기 부하와 온도 값의 3년치로 이루어져 있습니다. 전기 부하와 온도의 과거에 기록된 패턴이 주어지면, 전기 부하의 미래 값을 예측할 수 있습니다.
+
+이 예시에서, 과거에 기록된 부하 데이터만 사용해서, 한 time step에 앞서 예측하는 방식을 배우게 됩니다. 그러나, 시작하기 전, 무대 뒤에서 어떤 일이 일어나는지 이해하는 것이 유용합니다.
+
+## 일부 정의
+
+'time series' 용어를 만나면 여러 다른 맥락에서 사용하는 것을 이해할 필요가 있습니다.
+
+🎓 **Time series**
+
+수학에서, "time series은 시간 순서로 인덱스된 (또는 리스트되거나 그래픽으로 표기) 데이터 포인트 시리즈입니다. 가장 일반적으로, time series는 연속해서 같은 간격의 포인트로 되어있는 시퀀스입니다." time series 예시로는 [Dow Jones Industrial Average](https://wikipedia.org/wiki/Time_series)의 당일 마감 값입니다. time series plot과 통계 모델링을 사용하면 신호 처리, 닐씨 예측, 지진 경보 등 발생하고 시간이 지나면서 데이터 포인트를 그릴 수 있는 다양한 필드에서 자주 마주하게 됩니다.
+
+🎓 **Time series analysis**
+
+Time series 분석은, 방금 전에 언급했던 time series 데이터의 분석입니다. Time series 데이터는 interrupting 이벤트 이전과 이후에 time series 진화 패턴을 감지하는 'interrupted time series'를 포함한 별개 폼을 가질 수 있습니다. time series에 필요한 분석 타입은, 데이터의 특성에 기반합니다. Time series 데이터 자체는 계열 숫자 또는 문자 폼을 가질 수 있습니다.
+
+분석하면, frequency-domain과 time-domain, 선형과 비선형 등 포함해서, 다양한 방식을 사용합니다. 이 데이터 타입을 분석할 많은 방식에 대해서 [Learn more](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm)합니다.
+
+🎓 **Time series forecasting**
+
+Time series forecasting은 모델로 이전에 수집한 과거 데이터에서 보여준 패턴을 기반으로 미래 값을 예측합니다. regression 모델은 plot에서 x 변수를 사용할 타임 인덱스로, time series 데이터를 살펴보는 것이 가능하지만, 앞서 언급한 데이터는 스페셜 타입 모델을 사용해서 잘 분석하게 됩니다.
+
+Time series 데이터는 linear regression으로 분석할 수 있는 데이터와 다르게, 정렬된 관찰 값의 리스트입니다. 가장 일반적인 하나는 "Autoregressive Integrated Moving Average"의 약어인, ARIMA입니다. 
+
+[ARIMA models](https://online.stat.psu.edu/stat510/lesson/1/1.1)은 "시리즈의 현재 값을 과거 값과 과거 예측 오류로 엮습니다." 데이터가 시간이 지나면서 정렬되는, time-domain 데이터를 분석하는 게 가장 적당합니다.
+
+> [here](https://people.duke.edu/~rnau/411arim.htm)에서 배울 수 있고 다음 강의에서 다룰 예정인, ARIMA 모델에는 여러 타입이 있습니다.
+
+다음 강의에서, 시간이 지나가면서 바뀌는 하나의 변수에 초점을 맞추어진, [Univariate Time Series](https://itl.nist.gov/div898/handbook/pmc/section4/pmc44.htm)로 ARIMA 모델을 만드려고 합니다. 데이터 타입의 예시는 Mauna Loa Observatory에서 월별 C02 concentration을 기록한 [this dataset](https://itl.nist.gov/div898/handbook/pmc/section4/pmc4411.htm)입니다.
+
+|  CO2   | YearMonth | Year  | Month |
+| :----: | :-------: | :---: | :---: |
+| 330.62 |  1975.04  | 1975  |   1   |
+| 331.40 |  1975.13  | 1975  |   2   |
+| 331.87 |  1975.21  | 1975  |   3   |
+| 333.18 |  1975.29  | 1975  |   4   |
+| 333.92 |  1975.38  | 1975  |   5   |
+| 333.43 |  1975.46  | 1975  |   6   |
+| 331.85 |  1975.54  | 1975  |   7   |
+| 330.01 |  1975.63  | 1975  |   8   |
+| 328.51 |  1975.71  | 1975  |   9   |
+| 328.41 |  1975.79  | 1975  |  10   |
+| 329.25 |  1975.88  | 1975  |  11   |
+| 330.97 |  1975.96  | 1975  |  12   |
+
+✅ 이 데이터셋에서 시간이 지나며 변수를 식별합니다
+
+## 고려할 Time Series [data characteristics](https://online.stat.psu.edu/stat510/lesson/1/1.1)
+
+time series 데이터를 봤을 때, 패턴을 더 잘 이해하고자 계산하고 완화해야 할 필요가 있는 특성이라는 점을 알 수 있습니다. 만약 분석하기 원하는 'signal'로 잠재적으로 제공하는 time series 데이터를 고려하면, 이 특성을 'noise'라고 생각할 수 있습니다. 일부 통계 기술로 이 특성 중 일부를 파생해서 이 'noise'를 자주 줄일 필요가 있을 것입니다.
+
+time series 작업하기 위해서 알아야 되는 일부 컨셉은 여기 있습니다:
+
+🎓 **Trends**
+
+트랜드는 시간이 지나면서 측정할 수 있는 증감으로 정의합니다. [Read more](https://machinelearningmastery.com/time-series-trends-in-python). time series의 컨텍스트에서, time series으로 트랜드를 사용하고 필요할 때 지우는 방식입니다.
+
+🎓 **[Seasonality](https://machinelearningmastery.com/time-series-seasonality-with-python/)**
+
+다양한 plot 타입이 데이터에서 seasonality를 어떻게 보여주는지 [Take a look](https://itl.nist.gov/div898/handbook/pmc/section4/pmc443.htm)합니다.
+
+🎓 **Outliers**
+
+아웃라이어는 표준회된 데이터 분산에서 멀어져 있습니다.
+
+🎓 **Long-run cycle**
+
+seasonality의 독립적으로, 1년 보다 긴 경제 침체같은 long-run cycle을 보여줄 수 있습니다. 
+
+🎓 **Constant variance**
+
+시간이 지나면서, 일부 데이터는 낮과 밤 비율의 에너지 사용량처럼, 변하지 않는 파동을 보여줍니다. 
+
+🎓 **Abrupt changes**
+
+데이터는 더 분석할 필요가 있는 갑작스러운 변화를 보여줄 수 있습니다. 예시로, COVID로 인하여 비지니스가 갑자기 끝나면, 데이터에 변경이 가해집니다.
+
+✅ 여기는 몇 년이 넘도록 일일 인-게임 통화를 보여주는 [sample time series plot](https://www.kaggle.com/kashnitsky/topic-9-part-1-time-series-analysis-in-python)입니다. 이 데이터에서 리스트로 되어있는 특성을 모두 식별할 수 있나요?
+
+![In-game currency spend](.././images/currency.png)
+
+## 연습 - 전력 사용량 데이터로 시작하기
+
+주어진 과거 사용량으로 미래 전력 사용량을 예측하기 위한 time series 모델을 만들기 시작합시다.
+
+> 이 예시의 데이터는 GEFCom2014 forecasting competition에서 가져왔습니다. 2012년과 2014년 사이 시간당 전력 부하와 온도 값 3년치로 이루어져 있습니다.
+>
+> Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016.
+
+1. 이 강의의 `working` 폴더에서, _notebook.ipynb_ 파일을 엽니다. 데이터를 불러오고 시각화하기 위해 도움을 받을 수 있는 라이브러리를 추가해서 시작합니다
+
+
+    ```python
+    import os
+    import matplotlib.pyplot as plt
+    from common.utils import load_data
+    %matplotlib inline
+    ```
+
+    참고, 환경을 세팅하고 데이터를 내려받으려 포함된 `common` 폴더에 있는 파일을 사용합니다.
+
+2. 다음으로, `load_data()`와 `head()`를 불러서 데이터프레임으로 데이터를 검사합니다:
+
+    ```python
+    data_dir = './data'
+    energy = load_data(data_dir)[['load']]
+    energy.head()
+    ```
+
+    날짜와 load를 나타내는 두 열을 볼 수 있습니다:
+
+    |                     |  load  |
+    | :-----------------: | :----: |
+    | 2012-01-01 00:00:00 | 2698.0 |
+    | 2012-01-01 01:00:00 | 2558.0 |
+    | 2012-01-01 02:00:00 | 2444.0 |
+    | 2012-01-01 03:00:00 | 2402.0 |
+    | 2012-01-01 04:00:00 | 2403.0 |
+
+3. 지금부터, `plot()` 불러서 데이터를 plot 합니다:
+
+    ```python
+    energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)
+    plt.xlabel('timestamp', fontsize=12)
+    plt.ylabel('load', fontsize=12)
+    plt.show()
+    ```
+
+    ![energy plot](../images/energy-plot.png)
+
+4. 지금부터, `[from date]: [to date]` 패턴에서 `energy`를 넣어서 제공하는, July 2014 첫 주를 plot합니다:
+
+    ```python
+    energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)
+    plt.xlabel('timestamp', fontsize=12)
+    plt.ylabel('load', fontsize=12)
+    plt.show()
+    ```
+
+    ![july](../images/july-2014.png)
+
+    예쁜 plot 입니다! 이 plot을 보고 다음 리스트된 특성을 다 판단할 수 있는지 확인합니다. 데이터를 시각화해서 추측할 내용이 있나요?
+
+다음 강의에서, 일부 예측하는 ARIMA 모델을 만들 예정입니다.
+
+---
+
+## 🚀 도전
+
+time series forecasting에서 얻을 수 있다고 생각할 수 있는 모든 산업과 조사 영역의 리스트를 만듭니다. 예술에 이 기술을 적용할 수 있다고 생각하나요? 경제학에서? 생태학에서? 리테일에서? 산업에서? 금융에서? 또 다른 곳은 어딘가요?
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/)
+
+## 검토 & 자기주도 학습
+
+여기에서 커버되지 않지만, neural network는 가끔 time series forecasting의 classic 방식을 개선할 때 사용합니다. [in this article](https://medium.com/microsoftazure/neural-networks-for-forecasting-financial-and-economic-time-series-6aca370ff412)에서 해당 내용을 더 읽어봅니다.
+
+## 과제
+
+[Visualize some more time series](../assignment.md)
diff --git a/7-TimeSeries/1-Introduction/translations/assignment.it.md b/7-TimeSeries/1-Introduction/translations/assignment.it.md
new file mode 100644
index 000000000..cc192afd4
--- /dev/null
+++ b/7-TimeSeries/1-Introduction/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Visualizzare altre serie temporali
+
+## Istruzioni
+
+Si è iniziato a conoscere la previsione di serie temporali esaminando il tipo di dati richiesti da questa modellazione speciale. Si sono visualizzati alcuni dati sull'energia. Ora, cercare altri dati che potrebbero trarre vantaggio dalla previsione di serie temporali. Trovare tre esempi (provare [Kaggle](https://kaggle.com) e [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa)) e creare un notebook per visualizzarli. Annotare nel notebook tutte le caratteristiche speciali che hanno (stagionalità, cambiamenti improvvisi o altre tendenze).
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------ | ---------------------------------------------------- | ----------------------------------------------------------------------------------------- |
+|          | Tre insiemi di dati sono tracciati e spiegati in un notebook | Due insiemi di dati sono tracciati e spiegati in un notebook | Pochi insiemi di dati sono tracciati o spiegati in un notebook o i dati presentati sono insufficienti |
diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index d54a781be..19da56221 100644
--- a/7-TimeSeries/2-ARIMA/README.md
+++ b/7-TimeSeries/2-ARIMA/README.md
@@ -6,7 +6,7 @@ In the previous lesson, you learned a bit about time series forecasting and load
 
 > 🎥 Click the image above for a video: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/43/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/)
 
 ## Introduction
 
@@ -50,13 +50,13 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.
     import pandas as pd
     import datetime as dt
     import math
-    
+
     from pandas.plotting import autocorrelation_plot
     from statsmodels.tsa.statespace.sarimax import SARIMAX
     from sklearn.preprocessing import MinMaxScaler
     from common.utils import load_data, mape
     from IPython.display import Image
-    
+
     %matplotlib inline
     pd.options.display.float_format = '{:,.2f}'.format
     np.set_printoptions(precision=2)
@@ -83,16 +83,16 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.
 
 ### Create training and testing datasets
 
-Now your data is loaded, so you can separate it into train and test sets. You'll train your model on the train set. As usual, after the model has finished training, you'll evaluate its accuracy using the test set. You need to ensure that the test set covers a later period in time from the training set to ensure that the model does not gain information from future time periods. 
+Now your data is loaded, so you can separate it into train and test sets. You'll train your model on the train set. As usual, after the model has finished training, you'll evaluate its accuracy using the test set. You need to ensure that the test set covers a later period in time from the training set to ensure that the model does not gain information from future time periods.
 
 1. Allocate a two-month period from September 1 to October 31, 2014 to the training set. The test set will include the two-month period of November 1 to December 31, 2014:
 
     ```python
     train_start_dt = '2014-11-01 00:00:00'
-    test_start_dt = '2014-12-30 00:00:00'    
+    test_start_dt = '2014-12-30 00:00:00'
     ```
 
-    Since this data reflects the daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in more recent days. 
+    Since this data reflects the daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in more recent days.
 
 1. Visualize the differences:
 
@@ -120,11 +120,11 @@ Now, you need to prepare the data for training by performing filtering and scali
     ```python
     train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]
     test = energy.copy()[energy.index >= test_start_dt][['load']]
-    
+
     print('Training data shape: ', train.shape)
     print('Test data shape: ', test.shape)
     ```
- 
+
     You can see the shape of the data:
 
     ```output
@@ -189,17 +189,17 @@ Now you need to follow several steps
     print('Forecasting horizon:', HORIZON, 'hours')
     ```
 
-    Selecting the best values for an ARIMA model's parameters can be challenging as it's somewhat subjective and time intensive. You might consider using an `auto_arima()` function from the [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html), 
+    Selecting the best values for an ARIMA model's parameters can be challenging as it's somewhat subjective and time intensive. You might consider using an `auto_arima()` function from the [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html),
 
 1. For now try some manual selections to find a good model.
 
     ```python
     order = (4, 1, 0)
     seasonal_order = (1, 1, 0, 24)
-    
+
     model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)
     results = model.fit()
-    
+
     print(results.summary())
     ```
 
@@ -223,10 +223,10 @@ Walk-forward validation is the gold standard of time series model evaluation and
 
     ```python
     test_shifted = test.copy()
-    
-    for t in range(1, HORIZON):
+
+    for t in range(1, HORIZON+1):
         test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')
-        
+
     test_shifted = test_shifted.dropna(how='any')
     test_shifted.head(5)
     ```
@@ -246,18 +246,18 @@ Walk-forward validation is the gold standard of time series model evaluation and
     ```python
     %%time
     training_window = 720 # dedicate 30 days (720 hours) for training
-    
+
     train_ts = train['load']
     test_ts = test_shifted
-    
+
     history = [x for x in train_ts]
     history = history[(-training_window):]
-    
+
     predictions = list()
-    
+
     order = (2, 1, 0)
     seasonal_order = (1, 1, 0, 24)
-    
+
     for t in range(test_ts.shape[0]):
         model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)
         model_fit = model.fit()
@@ -276,10 +276,10 @@ Walk-forward validation is the gold standard of time series model evaluation and
     ```output
     2014-12-30 00:00:00
     1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]
-    
+
     2014-12-30 01:00:00
     2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]
-    
+
     2014-12-30 02:00:00
     3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]
     ```
@@ -295,7 +295,7 @@ Walk-forward validation is the gold standard of time series model evaluation and
     eval_df.head()
     ```
 
-    ```output
+    Output
     |     |            | timestamp | h   | prediction | actual   |
     | --- | ---------- | --------- | --- | ---------- | -------- |
     | 0   | 2014-12-30 | 00:00:00  | t+1 | 3,008.74   | 3,023.00 |
@@ -303,7 +303,7 @@ Walk-forward validation is the gold standard of time series model evaluation and
     | 2   | 2014-12-30 | 02:00:00  | t+1 | 2,900.17   | 2,899.00 |
     | 3   | 2014-12-30 | 03:00:00  | t+1 | 2,917.69   | 2,886.00 |
     | 4   | 2014-12-30 | 04:00:00  | t+1 | 2,946.99   | 2,963.00 |
-    ```
+
 
     Observe the hourly data's prediction, compared to the actual load. How accurate is this?
 
@@ -311,10 +311,10 @@ Walk-forward validation is the gold standard of time series model evaluation and
 
 Check the accuracy of your model by testing its mean absolute percentage error (MAPE) over all the predictions.
 
-> **🧮 Show me the math** 
+> **🧮 Show me the math**
 >
 > ![MAPE](images/mape.png)
-> 
+>
 >  [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actual<sub>t</sub> and predicted<sub>t</sub> is divided by the actual<sub>t</sub>. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error)
 
 1. Express equation in code:
@@ -351,13 +351,13 @@ Check the accuracy of your model by testing its mean absolute percentage error (
      if(HORIZON == 1):
         ## Plotting single step forecast
         eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))
-    
+
     else:
         ## Plotting multi step forecast
         plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]
         for t in range(1, HORIZON+1):
             plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values
-    
+
         fig = plt.figure(figsize=(15, 8))
         ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)
         ax = fig.add_subplot(111)
@@ -365,9 +365,9 @@ Check the accuracy of your model by testing its mean absolute percentage error (
             x = plot_df['timestamp'][(t-1):]
             y = plot_df['t+'+str(t)][0:len(x)]
             ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))
-        
+
         ax.legend(loc='best')
-        
+
     plt.xlabel('timestamp', fontsize=12)
     plt.ylabel('load', fontsize=12)
     plt.show()
@@ -383,12 +383,12 @@ Check the accuracy of your model by testing its mean absolute percentage error (
 
 Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html)
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/44/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/)
 
 ## Review & Self Study
 
 This lesson touches on only the basics of Time Series Forecasting with ARIMA. Take some time to deepen your knowledge by digging into [this repository](https://microsoft.github.io/forecasting/) and its various model types to learn other ways to build Time Series models.
 
-## Assignment 
+## Assignment
 
 [A new ARIMA model](assignment.md)
diff --git a/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/7-TimeSeries/2-ARIMA/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/7-TimeSeries/2-ARIMA/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/7-TimeSeries/2-ARIMA/solution/R/README.md b/7-TimeSeries/2-ARIMA/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/7-TimeSeries/2-ARIMA/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
index 4914ab705..1a42ab155 100644
--- a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
+++ b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
    "source": [
     "# Time series forecasting with ARIMA\n",
     "\n",
@@ -14,13 +15,26 @@
     "\n",
     "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
    ],
+   "metadata": {}
+  },
+  {
    "cell_type": "markdown",
+   "source": [
+    "## Install Dependencies\n",
+    "Get started by installing some of the required dependencies. These libraries with their corresponding versions are known to work for the solution:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
    "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "source": [
+    "!pip install statsmodels"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -40,15 +54,11 @@
      ]
     }
    ],
-   "source": [
-    "!pip install statsmodels"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "import os\n",
     "import warnings\n",
@@ -68,12 +78,17 @@
     "pd.options.display.float_format = '{:,.2f}'.format\n",
     "np.set_printoptions(precision=2)\n",
     "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -91,116 +106,188 @@
        "2012-01-01 08:00:00 2,916.00\n",
        "2012-01-01 09:00:00 3,105.00"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2,698.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2,558.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2,444.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2,402.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2,403.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 05:00:00</th>\n      <td>2,453.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 06:00:00</th>\n      <td>2,560.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 07:00:00</th>\n      <td>2,719.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 08:00:00</th>\n      <td>2,916.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 09:00:00</th>\n      <td>3,105.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 3
     }
    ],
-   "source": [
-    "energy = load_data('./data')[['load']]\n",
-    "energy.head(10)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Plot all available load data (January 2012 to Dec 2014)"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 1080x576 with 1 Axes>",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 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191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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238.01299 163.592189 \nL 238.076633 163.921378 \nL 238.203919 202.985135 \nL 238.394848 321.273702 \nL 238.458491 289.122914 \nL 238.681241 151.083009 \nL 238.713063 147.791119 \nL 238.808527 148.778686 \nL 238.903991 167.871646 \nL 238.935813 165.786782 \nL 239.158563 329.283967 \nL 239.190385 321.93208 \nL 239.508599 152.948413 \nL 239.604064 156.459762 \nL 239.73135 197.059734 \nL 239.922278 324.236403 \nL 239.9541 319.188839 \nL 240.304136 128.36897 \nL 240.367779 132.428968 \nL 240.495065 176.430559 \nL 240.685993 307.996414 \nL 240.749636 280.89319 \nL 241.004208 127.052215 \nL 241.03603 127.710593 \nL 241.226958 154.594358 \nL 241.449709 319.408298 \nL 241.513352 309.532629 \nL 241.895209 177.088937 \nL 242.022495 208.361888 \nL 242.213424 332.136938 \nL 242.245245 331.039641 \nL 242.34071 290.988318 \nL 242.531638 190.256495 \nL 242.658924 164.799216 \nL 242.754389 172.260832 \nL 242.977139 315.896949 \nL 243.040782 285.062916 \nL 243.295354 120.029517 \nL 243.327175 116.298709 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 251.091613 166.44516 \nL 251.123434 154.923547 \nL 251.155256 164.031108 \nL 251.378006 320.834784 \nL 251.441649 286.379672 \nL 251.632578 152.509494 \nL 251.728042 140.878151 \nL 251.759863 142.304637 \nL 251.823506 155.472195 \nL 251.982614 235.135924 \nL 252.141721 317.433164 \nL 252.205364 301.412635 \nL 252.428114 181.039204 \nL 252.491757 185.318661 \nL 252.5554 187.184065 \nL 252.587221 185.977039 \nL 252.650864 180.819745 \nL 252.746329 254.887262 \nL 252.937258 337.403961 \nL 253.032722 300.863987 \nL 253.191829 239.41538 \nL 253.255472 243.585107 \nL 253.287294 241.939162 \nL 253.41458 213.628912 \nL 253.446401 225.260255 \nL 253.63733 351.229898 \nL 253.669151 351.120168 \nL 253.732794 312.60506 \nL 253.891902 212.202426 \nL 253.923723 209.568914 \nL 253.987366 211.763507 \nL 254.051009 221.419717 \nL 254.114652 216.152694 \nL 254.178295 203.533783 \nL 254.210116 217.35972 \nL 254.432866 352.656383 \nL 254.496509 311.178574 \nL 254.655617 224.711607 \nL 254.751081 216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 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279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 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265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 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298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 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327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 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348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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360.111952 184.660283 \nL 360.175595 198.37649 \nL 360.207417 194.97487 \nL 360.302881 126.942485 \nL 360.334703 142.633825 \nL 360.557453 272.992654 \nL 360.621096 260.044555 \nL 360.780203 149.546794 \nL 360.812025 150.314901 \nL 360.939311 163.921378 \nL 360.971132 160.848948 \nL 361.034775 119.261409 \nL 361.098418 149.107875 \nL 361.35299 288.683995 \nL 361.416633 247.206186 \nL 361.480275 172.91921 \nL 361.543918 174.016507 \nL 361.57574 174.235966 \nL 361.703026 187.184065 \nL 361.734847 185.757579 \nL 361.830312 137.695991 \nL 361.862133 152.180305 \nL 362.116705 306.899118 \nL 362.148526 299.437501 \nL 362.339455 182.136501 \nL 362.371277 183.014338 \nL 362.466741 195.413789 \nL 362.498562 194.865141 \nL 362.562205 153.497061 \nL 362.625848 173.138669 \nL 362.88042 323.907214 \nL 362.912241 314.799653 \nL 363.10317 206.715943 \nL 363.134992 209.568914 \nL 363.230456 219.993232 \nL 363.294099 194.97487 \nL 363.357742 150.534361 \nL 363.389563 159.861381 \nL 363.612314 292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 367.176318 124.199244 \nL 367.239961 155.362465 \nL 367.462711 281.771027 \nL 367.526354 239.63484 \nL 367.621818 165.786782 \nL 367.65364 166.55489 \nL 367.812747 188.83001 \nL 367.844569 188.61055 \nL 367.940033 130.563564 \nL 368.003676 155.691654 \nL 368.194605 271.34671 \nL 368.226426 270.139683 \nL 368.290069 219.993232 \nL 368.385533 144.938148 \nL 368.417355 150.64409 \nL 368.608284 179.722449 \nL 368.703748 123.321406 \nL 368.767391 146.803552 \nL 368.990141 271.785628 \nL 369.053784 232.612142 \nL 369.244713 127.710593 \nL 369.371999 152.399765 \nL 369.40382 143.950581 \nL 369.435642 115.85979 \nL 369.499285 146.254904 \nL 369.753856 282.209945 \nL 369.817499 270.139683 \nL 370.008428 178.625152 \nL 370.04025 181.478123 \nL 370.135714 192.231629 \nL 370.231178 138.244639 \nL 370.263 154.594358 \nL 370.517572 282.209945 \nL 370.549393 278.369407 \nL 370.7085 200.241894 \nL 370.803965 214.397019 \nL 370.867608 230.746738 \nL 370.899429 227.125659 \nL 370.994894 154.155439 \nL 371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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531.725114 315.128842 \nL 531.884222 221.639176 \nL 531.979686 219.334854 \nL 532.043329 225.260255 \nL 532.075151 224.272688 \nL 532.170615 199.802975 \nL 532.202436 213.189993 \nL 532.425187 347.828278 \nL 532.48883 312.056411 \nL 532.647937 202.985135 \nL 532.743401 198.595949 \nL 532.807044 202.546217 \nL 532.838866 197.279193 \nL 532.93433 173.138669 \nL 532.966152 186.196498 \nL 533.188902 320.725054 \nL 533.252545 281.002919 \nL 533.443474 120.797624 \nL 533.602581 79.319815 \nL 533.634402 82.831164 \nL 533.698045 82.063056 \nL 533.729867 103.789528 \nL 533.952617 287.03805 \nL 533.984439 281.880756 \nL 534.207189 129.795456 \nL 534.270832 134.95275 \nL 534.334475 148.230038 \nL 534.398118 141.426799 \nL 534.429939 141.31707 \nL 534.46176 142.633825 \nL 534.589046 263.116986 \nL 534.716332 309.532629 \nL 534.748154 302.290472 \nL 534.970904 168.420294 \nL 535.002725 170.066239 \nL 535.034547 171.163536 \nL 535.32094 269.042387 \nL 535.480047 344.755848 \nL 535.54369 332.466127 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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222.846203 \nL 584.803318 176.320829 \nL 584.866961 205.838106 \nL 585.089712 342.232066 \nL 585.153355 329.503426 \nL 585.344283 212.202426 \nL 585.471569 232.063493 \nL 585.503391 230.746738 \nL 585.567034 173.797047 \nL 585.630677 192.999737 \nL 585.853427 331.259101 \nL 585.91707 316.226138 \nL 586.107999 200.571083 \nL 586.13982 200.022435 \nL 586.171641 202.985135 \nL 586.235284 202.546217 \nL 586.267106 198.15703 \nL 586.330749 137.476532 \nL 586.394392 154.813817 \nL 586.617142 296.365071 \nL 586.680785 283.636431 \nL 586.776249 181.807312 \nL 586.871714 191.463522 \nL 586.999 207.92297 \nL 587.030821 201.887839 \nL 587.094464 141.975448 \nL 587.158107 154.155439 \nL 587.380857 293.182911 \nL 587.4445 276.723463 \nL 587.539965 171.163536 \nL 587.603607 183.892175 \nL 587.762715 213.628912 \nL 587.794536 206.496484 \nL 587.858179 154.045709 \nL 587.921822 167.761916 \nL 588.144572 314.360734 \nL 588.208215 302.400202 \nL 588.399144 188.500821 \nL 588.430966 189.488388 \nL 588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 \nL 592.249541 177.527855 \nL 592.376827 189.159199 \nL 592.44047 163.263 \nL 592.472292 172.480291 \nL 592.599577 261.58077 \nL 592.726863 326.211537 \nL 592.758685 325.443429 \nL 592.822328 302.729391 \nL 592.981435 180.710015 \nL 593.045078 201.33919 \nL 593.140542 239.854299 \nL 593.172364 221.419717 \nL 593.204185 212.092696 \nL 593.29965 215.604045 \nL 593.490578 290.32994 \nL 593.554221 281.551567 \nL 593.74515 201.887839 \nL 593.776972 202.765676 \nL 593.872436 212.312156 \nL 593.936079 168.968942 \nL 593.9679 149.437064 \nL 594.031543 163.043541 \nL 594.158829 247.864564 \nL 594.254294 279.466704 \nL 594.286115 277.162381 \nL 594.349758 261.909959 \nL 594.477044 186.964606 \nL 594.540687 196.401356 \nL 594.636151 208.800807 \nL 594.667973 200.461353 \nL 594.731616 147.57166 \nL 594.795259 172.370562 \nL 594.890723 237.220787 \nL 595.04983 304.923984 \nL 595.081652 304.923984 \nL 595.177116 257.959692 \nL 595.336223 201.668379 \nL 595.399866 206.496484 \nL 595.431688 200.461353 \nL 595.495331 155.252736 \nL 595.558974 179.28353 \nL 595.781724 316.555327 \nL 595.845367 303.05858 \nL 596.036296 183.672716 \nL 596.068117 183.892175 \nL 596.163582 190.256495 \nL 596.195403 185.099202 \nL 596.259046 138.903017 \nL 596.322689 158.105707 \nL 596.577261 316.774786 \nL 596.609082 306.350469 \nL 596.736368 196.840275 \nL 596.800011 199.693246 \nL 596.927297 210.556481 \nL 596.959118 205.947836 \nL 597.022761 145.815985 \nL 597.086404 157.557058 \nL 597.309154 306.899118 \nL 597.372797 294.060748 \nL 597.468262 183.014338 \nL 597.563726 194.426222 \nL 597.595548 194.097033 \nL 597.691012 204.411621 \nL 597.722833 201.668379 \nL 597.786476 142.633825 \nL 597.850119 153.716521 \nL 598.072869 307.008847 \nL 598.168334 263.446175 \nL 598.327441 177.198666 \nL 598.359263 177.527855 \nL 598.454727 184.550553 \nL 598.550191 139.890584 \nL 598.613834 158.215436 \nL 598.836585 319.847217 \nL 598.900228 310.081277 \nL 599.091156 187.842443 \nL 599.122978 189.268928 \nL 599.218442 199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 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182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 683.704432 234.806735 \nL 683.768075 167.761916 \nL 683.831718 175.333262 \nL 684.022647 212.312156 \nL 684.08629 201.668379 \nL 684.149933 174.784614 \nL 684.181754 180.490556 \nL 684.372683 305.582362 \nL 684.404505 305.362902 \nL 684.468148 268.16455 \nL 684.627255 183.014338 \nL 684.754541 204.301891 \nL 684.786362 209.568914 \nL 684.850005 206.496484 \nL 684.881827 206.167295 \nL 684.913648 200.571083 \nL 684.94547 207.154862 \nL 685.16822 314.251004 \nL 685.231863 307.447766 \nL 685.359149 226.467281 \nL 685.422792 232.173223 \nL 685.518256 253.460776 \nL 685.581899 246.876997 \nL 685.677363 216.701342 \nL 685.709185 223.61431 \nL 685.931935 323.468295 \nL 685.963756 321.273702 \nL 686.059221 269.042387 \nL 686.186507 199.034868 \nL 686.218328 200.132164 \nL 686.313793 211.763507 \nL 686.345614 206.496484 \nL 686.441078 186.086768 \nL 686.4729 194.426222 \nL 686.663829 312.056411 \nL 686.69565 309.31317 \nL 686.759293 263.665634 \nL 686.9184 172.91921 \nL 686.950222 172.480291 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 693.537265 325.443429 \nL 693.569087 321.493162 \nL 693.63273 277.71103 \nL 693.760015 206.386754 \nL 693.791837 206.606214 \nL 693.919123 221.858636 \nL 693.950944 226.357551 \nL 693.982766 225.369985 \nL 694.110052 198.26676 \nL 694.30098 312.934248 \nL 694.332802 310.849385 \nL 694.396445 266.628335 \nL 694.460088 201.229461 \nL 694.523731 207.92297 \nL 694.714659 233.819168 \nL 694.778302 226.9062 \nL 694.873767 204.960269 \nL 695.096517 328.625589 \nL 695.16016 286.92832 \nL 695.255624 214.616478 \nL 695.287446 217.798638 \nL 695.351089 223.72404 \nL 695.478375 242.487811 \nL 695.542017 237.549976 \nL 695.60566 226.79647 \nL 695.637482 228.661874 \nL 695.828411 340.476392 \nL 695.892054 335.648287 \nL 696.051161 243.804566 \nL 696.146625 257.081855 \nL 696.24209 271.23698 \nL 696.273911 268.384009 \nL 696.401197 244.024026 \nL 696.592126 346.511523 \nL 696.655769 344.975307 \nL 696.751233 293.182911 \nL 696.878519 221.968365 \nL 696.910341 223.285121 \nL 697.005805 239.964029 \nL 697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 \nL 747.792863 319.298568 \nL 747.856506 292.634263 \nL 748.047435 168.749483 \nL 748.079257 168.968942 \nL 748.111078 165.567323 \nL 748.174721 170.505158 \nL 748.270185 182.35596 \nL 748.397471 249.729968 \nL 748.556579 328.18667 \nL 748.620222 305.692091 \nL 748.81115 197.059734 \nL 749.002079 213.848371 \nL 749.161186 267.506172 \nL 749.320294 345.633685 \nL 749.383937 337.623421 \nL 749.543044 241.609973 \nL 749.606687 245.011593 \nL 749.702151 254.119154 \nL 749.733973 251.046724 \nL 749.797616 245.340782 \nL 749.829437 250.168886 \nL 749.861259 249.07159 \nL 749.89308 254.667802 \nL 750.084009 352.766113 \nL 750.11583 352.327194 \nL 750.147652 350.242331 \nL 750.370402 247.645104 \nL 750.465866 249.729968 \nL 750.561331 228.661874 \nL 750.624974 232.831601 \nL 750.688617 276.613733 \nL 750.847724 343.658552 \nL 750.911367 317.433164 \nL 751.102296 182.46569 \nL 751.165939 173.577588 \nL 751.261403 176.540289 \nL 751.293225 175.333262 \nL 751.42051 209.898103 \nL 751.611439 326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 859.327099 326.979644 \nL 859.518028 214.945667 \nL 859.549849 215.055397 \nL 859.645314 221.748906 \nL 859.740778 178.186233 \nL 859.804421 197.608382 \nL 859.899885 276.284544 \nL 860.027171 324.126673 \nL 860.058993 320.725054 \nL 860.122636 276.284544 \nL 860.2181 209.020266 \nL 860.249922 210.007833 \nL 860.313564 214.28729 \nL 860.345386 219.554313 \nL 860.409029 218.566746 \nL 860.44085 213.080263 \nL 860.504493 163.592189 \nL 860.568136 178.734882 \nL 860.822708 314.031545 \nL 860.854529 302.509931 \nL 860.981815 194.535952 \nL 861.045458 198.815408 \nL 861.172744 208.910536 \nL 861.204565 205.618647 \nL 861.268208 161.507326 \nL 861.331851 181.478123 \nL 861.459137 270.249413 \nL 861.586423 307.008847 \nL 861.618245 300.973716 \nL 861.809173 214.616478 \nL 861.872816 225.479714 \nL 861.904638 231.624575 \nL 861.936459 231.075927 \nL 862.000102 191.90244 \nL 862.031924 171.053806 \nL 862.095567 190.037036 \nL 862.222852 272.114817 \nL 862.350138 302.071013 \nL 862.38196 298.340205 \nL 862.795639 165.567323 \nL 862.82746 176.869478 \nL 862.954746 271.34671 \nL 863.082032 319.737487 \nL 863.113853 318.201272 \nL 863.177496 272.005088 \nL 863.336604 175.99164 \nL 863.368425 175.113803 \nL 863.400247 176.869478 \nL 863.46389 180.380826 \nL 863.495711 176.979207 \nL 863.559354 151.960846 \nL 863.591175 160.739218 \nL 863.845747 316.335868 \nL 863.90939 299.87642 \nL 864.100319 195.0846 \nL 864.163962 200.900272 \nL 864.227605 199.912705 \nL 864.291248 161.287867 \nL 864.323069 135.281939 \nL 864.386712 151.192738 \nL 864.482176 229.210522 \nL 864.609462 280.234812 \nL 864.641284 279.027785 \nL 864.704927 235.794302 \nL 864.76857 166.44516 \nL 864.832213 176.650018 \nL 864.99132 193.767844 \nL 865.023141 186.525687 \nL 865.086784 137.037613 \nL 865.150427 147.242471 \nL 865.245892 229.8689 \nL 865.373177 289.671562 \nL 865.404999 288.025617 \nL 865.468642 244.133755 \nL 865.532285 173.138669 \nL 865.595928 179.832178 \nL 865.755035 192.890007 \nL 865.818678 161.178137 \nL 865.850499 143.511663 \nL 865.914142 156.350032 \nL 866.009607 231.185656 \nL 866.136893 284.843457 \nL 866.168714 283.307242 \nL 866.232357 238.318084 \nL 866.296 168.639753 \nL 866.359643 168.859213 \nL 866.423286 172.480291 \nL 866.51875 181.807312 \nL 866.550572 176.320829 \nL 866.614215 138.244639 \nL 866.677858 157.557058 \nL 866.773322 219.005665 \nL 866.932429 278.149948 \nL 866.996072 260.154285 \nL 867.123358 180.380826 \nL 867.187001 187.622984 \nL 867.282465 208.361888 \nL 867.314287 205.947836 \nL 867.37793 168.968942 \nL 867.409751 180.271097 \nL 867.696144 320.505595 \nL 867.759787 307.557495 \nL 867.918895 230.966197 \nL 867.982538 236.23322 \nL 868.046181 245.340782 \nL 868.078002 236.781869 \nL 868.141645 179.28353 \nL 868.205288 203.533783 \nL 868.428038 326.869915 \nL 868.491681 313.812086 \nL 868.68261 186.635417 \nL 868.714431 191.573251 \nL 868.809896 205.508917 \nL 868.841717 202.985135 \nL 868.90536 170.944076 \nL 868.969003 189.159199 \nL 869.191753 336.965043 \nL 869.255396 326.430996 \nL 869.446325 218.457016 \nL 869.478146 219.554313 \nL 869.573611 227.893767 \nL 869.605432 223.833769 \nL 869.669075 173.577588 \nL 869.732718 186.525687 \nL 869.955468 323.687755 \nL 870.019111 307.228306 \nL 870.273683 167.761916 \nL 870.305505 165.238134 \nL 870.337326 166.225701 \nL 870.369147 169.517591 \nL 870.400969 156.569492 \nL 870.464612 173.577588 \nL 870.751005 346.511523 \nL 870.782827 343.987741 \nL 870.941934 252.582939 \nL 871.005577 232.941331 \nL 871.037398 240.293218 \nL 871.132863 266.518605 \nL 871.164684 253.021857 \nL 871.196506 247.096456 \nL 871.260149 248.193753 \nL 871.323791 263.336445 \nL 871.482899 316.335868 \nL 871.51472 314.909382 \nL 871.578363 292.743992 \nL 871.769292 214.397019 \nL 871.864756 223.50458 \nL 871.896578 215.713775 \nL 871.960221 170.834347 \nL 872.023864 180.819745 \nL 872.15115 260.264015 \nL 872.278435 293.841289 \nL 872.342078 276.833192 \nL 872.501186 199.693246 \nL 872.533007 203.862972 \nL 872.628472 215.494316 \nL 872.660293 207.92297 \nL 872.723936 158.654355 \nL 872.787579 173.906777 \nL 872.883043 234.258087 \nL 873.042151 305.801821 \nL 873.073972 304.375335 \nL 873.169436 262.568337 \nL 873.296722 207.154862 \nL 873.328544 212.092696 \nL 873.392187 220.43215 \nL 873.424008 211.324589 \nL 873.487651 168.639753 \nL 873.551294 191.573251 \nL 873.805866 325.882348 \nL 873.837687 314.470464 \nL 874.028616 201.119731 \nL 874.060437 204.52135 \nL 874.155902 208.252159 \nL 874.187723 204.301891 \nL 874.251366 151.851116 \nL 874.315009 163.811649 \nL 874.442295 267.835361 \nL 874.537759 295.048315 \nL 874.569581 292.305074 \nL 874.633224 246.328349 \nL 874.728688 172.699751 \nL 874.76051 174.455425 \nL 874.855974 184.111635 \nL 874.919617 181.148934 \nL 874.98326 140.878151 \nL 875.015081 124.199244 \nL 875.078724 140.658692 \nL 875.333296 295.267774 \nL 875.396939 257.411044 \nL 875.556046 178.844611 \nL 875.587868 178.295963 \nL 875.683332 187.184065 \nL 875.715154 181.587853 \nL 875.778797 144.49923 \nL 875.84244 162.714352 \nL 876.06519 303.716958 \nL 876.128833 289.671562 \nL 876.319761 182.026771 \nL 876.415226 191.79271 \nL 876.447047 191.244062 \nL 876.542512 133.087346 \nL 876.637976 158.105707 \nL 876.828905 278.588867 \nL 876.860726 276.394274 \nL 876.924369 231.734305 \nL 876.988012 157.776518 \nL 877.051655 163.811649 \nL 877.210763 182.46569 \nL 877.242584 176.320829 \nL 877.306227 140.329503 \nL 877.36987 161.617056 \nL 877.465334 229.100793 \nL 877.624442 306.02128 \nL 877.656263 303.607228 \nL 877.783549 217.908368 \nL 877.879013 190.805144 \nL 877.910835 193.987304 \nL 877.974478 202.655946 \nL 878.006299 198.815408 \nL 878.069942 160.958678 \nL 878.101764 172.480291 \nL 878.356335 312.275871 \nL 878.4518 296.913719 \nL 878.610907 205.728377 \nL 878.67455 214.506749 \nL 878.738193 209.678644 \nL 878.801836 154.484628 \nL 878.833657 126.174377 \nL 878.8973 140.658692 \nL 879.12005 270.139683 \nL 879.183693 254.448343 \nL 879.310979 146.693823 \nL 879.374622 150.424631 \nL 879.470087 158.434896 \nL 879.501908 158.105707 \nL 879.597372 102.143583 \nL 879.661015 119.810057 \nL 879.915587 283.307242 \nL 879.97923 244.133755 \nL 880.138337 160.739218 \nL 880.170159 159.422463 \nL 880.265623 163.48246 \nL 880.361088 119.371139 \nL 880.42473 149.107875 \nL 880.679302 311.617493 \nL 880.711124 301.193176 \nL 880.933874 179.39326 \nL 880.997517 179.1738 \nL 881.029338 181.917042 \nL 881.092981 154.704087 \nL 881.124803 141.755988 \nL 881.156624 148.339767 \nL 881.28391 254.996991 \nL 881.411196 320.066676 \nL 881.443017 317.762353 \nL 881.50666 277.381841 \nL 881.665768 194.97487 \nL 881.697589 194.426222 \nL 881.729411 195.523519 \nL 881.761232 196.181897 \nL 881.793054 196.072167 \nL 881.856696 160.958678 \nL 881.888518 143.840852 \nL 881.952161 158.215436 \nL 882.047625 242.70727 \nL 882.206733 307.667225 \nL 882.270375 265.969957 \nL 882.429483 185.64785 \nL 882.461304 186.635417 \nL 882.524947 193.328926 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137.147343 \nL 885.770737 154.923547 \nL 885.898023 276.942922 \nL 885.993487 315.896949 \nL 886.025308 315.67749 \nL 886.088951 272.224547 \nL 886.248059 182.46569 \nL 886.375344 196.620815 \nL 886.407166 192.560818 \nL 886.470809 145.486796 \nL 886.534452 159.093274 \nL 886.661738 277.272111 \nL 886.757202 316.665057 \nL 886.789024 316.445597 \nL 886.852666 270.359143 \nL 887.011774 186.086768 \nL 887.043595 186.745146 \nL 887.075417 191.90244 \nL 887.13906 191.353792 \nL 887.202703 153.606791 \nL 887.234524 135.281939 \nL 887.298167 149.546794 \nL 887.393631 229.539711 \nL 887.552739 296.69426 \nL 887.616382 254.119154 \nL 887.743667 171.712184 \nL 887.775489 172.80948 \nL 887.902775 192.231629 \nL 887.934596 187.074335 \nL 887.998239 138.244639 \nL 888.061882 153.387332 \nL 888.157347 218.018098 \nL 888.316454 288.683995 \nL 888.380097 270.029954 \nL 888.507383 183.124068 \nL 888.571026 194.206763 \nL 888.66649 209.459185 \nL 888.698311 199.802975 \nL 888.761954 148.559227 \nL 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\n"
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      },
      "metadata": {
       "needs_background": "light"
      }
     }
    ],
-   "source": [
-    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
-    "plt.xlabel('timestamp', fontsize=12)\n",
-    "plt.ylabel('load', fontsize=12)\n",
-    "plt.show()"
-   ]
+   "metadata": {}
   },
   {
+   "cell_type": "markdown",
    "source": [
     "## Create training and testing data sets\n"
    ],
-   "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "train_start_dt = '2014-11-01 00:00:00'\n",
     "test_start_dt = '2014-12-30 00:00:00'    "
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 1080x576 with 1 Axes>",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ],
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403.048897 333.298061 \nL 403.621009 362.497703 \nL 404.193121 377.362976 \nL 404.765233 384.264709 \nL 405.337345 373.823625 \nL 405.909457 357.71958 \nL 406.48157 314.185568 \nL 407.053682 248.530616 \nL 407.625794 203.580864 \nL 408.197906 178.451476 \nL 409.914242 134.740496 \nL 410.486354 132.793854 \nL 411.058466 128.723601 \nL 411.630578 130.316308 \nL 412.202691 135.625334 \nL 412.774803 114.743166 \nL 413.346915 119.344322 \nL 413.919027 142.173133 \nL 415.635363 274.72181 \nL 416.779587 367.098859 \nL 417.351699 395.236696 \nL 417.923812 408.686228 \nL 418.495924 417.357637 \nL 419.068036 421.073955 \nL 419.640148 417.003701 \nL 420.21226 399.483916 \nL 421.356484 340.55373 \nL 422.500708 269.589752 \nL 423.07282 250.477259 \nL 423.644933 237.912564 \nL 424.217045 249.769389 \nL 424.789157 271.89033 \nL 425.361269 285.516829 \nL 425.933381 292.064628 \nL 426.505493 270.297622 \nL 427.077605 260.741375 \nL 427.649717 261.09531 \nL 428.221829 262.511051 \nL 428.793941 267.466141 \nL 429.366053 286.932569 \nL 430.510278 340.730697 \nL 431.08239 358.781385 \nL 431.654502 369.576404 \nL 432.226614 372.407885 \nL 432.798726 370.107307 \nL 433.370838 358.604418 \nL 433.94295 334.359867 \nL 434.515062 293.657335 \nL 436.231399 212.429241 \nL 436.803511 210.836533 \nL 437.375623 208.005052 \nL 437.947735 211.367436 \nL 438.519847 212.075306 \nL 439.091959 222.693357 \nL 439.664071 210.128663 \nL 440.236183 162.170463 \nL 440.808295 137.748944 \nL 441.380408 145.004613 \nL 441.95252 153.852989 \nL 442.524632 171.903677 \nL 443.096744 203.049962 \nL 444.240968 281.977479 \nL 444.81308 313.831633 \nL 445.385192 328.519938 \nL 445.957304 335.598639 \nL 446.529416 336.129542 \nL 447.101529 329.050841 \nL 447.673641 308.699575 \nL 448.817865 235.611987 \nL 449.389977 198.802741 \nL 449.962089 184.645339 \nL 450.534201 184.291404 \nL 451.106313 191.01617 \nL 451.678425 200.749384 \nL 452.250537 206.76628 \nL 452.82265 209.774728 \nL 453.394762 197.563968 \nL 453.966874 144.119775 \nL 454.538986 118.105549 \nL 455.111098 125.18425 \nL 456.255322 159.692918 \nL 456.827434 195.794293 \nL 457.971658 282.685349 \nL 458.543771 315.247374 \nL 459.115883 334.890769 \nL 459.687995 348.517269 \nL 460.260107 355.419002 \nL 460.832219 353.118424 \nL 461.404331 339.668892 \nL 461.976443 320.733367 \nL 462.548555 285.693797 \nL 463.120667 240.036175 \nL 463.692779 208.712923 \nL 464.264891 197.917903 \nL 464.837004 196.325196 \nL 465.409116 204.288734 \nL 465.981228 213.844981 \nL 466.55334 217.738266 \nL 467.125452 203.049962 \nL 467.697564 151.552411 \nL 468.269676 134.209594 \nL 468.841788 146.951256 \nL 469.4139 171.195807 \nL 469.986012 201.457254 \nL 471.130237 304.275387 \nL 471.702349 345.154886 \nL 472.274461 370.461242 \nL 472.846573 383.733806 \nL 473.418685 387.80406 \nL 473.990797 387.80406 \nL 474.562909 369.399437 \nL 475.135021 320.379432 \nL 475.707133 237.381662 \nL 476.279246 200.926351 \nL 476.851358 195.794293 \nL 477.42347 195.617326 \nL 477.995582 186.591982 \nL 479.139806 196.856098 \nL 479.711918 196.148228 \nL 480.28403 198.094871 \nL 480.856142 191.72404 \nL 481.428254 143.588873 \nL 482.000367 107.133562 \nL 482.572479 108.549303 \nL 483.144591 126.423023 \nL 483.716703 149.251834 \nL 485.433039 294.188238 \nL 486.005151 320.910334 \nL 486.577263 334.182899 \nL 487.149375 338.076185 \nL 487.721488 333.651996 \nL 488.2936 315.778276 \nL 488.865712 259.502603 \nL 489.437824 171.72671 \nL 490.009936 141.111327 \nL 490.582048 140.757392 \nL 491.726272 146.597321 \nL 492.298384 155.091762 \nL 492.870496 159.162015 \nL 493.442609 153.499054 \nL 494.014721 154.383892 \nL 494.586833 140.93436 \nL 495.158945 89.43681 \nL 495.731057 62.537746 \nL 496.303169 69.085544 \nL 496.875281 89.082875 \nL 497.447393 116.689809 \nL 498.019505 168.541294 \nL 498.591617 233.134441 \nL 499.163729 280.384771 \nL 499.735842 306.398997 \nL 500.307954 325.334523 \nL 500.880066 336.837412 \nL 501.452178 338.43012 \nL 502.02429 322.149107 \nL 502.596402 277.376323 \nL 503.168514 204.819637 \nL 503.740626 163.409236 \nL 504.312738 152.083314 \nL 504.88485 153.852989 \nL 505.456963 150.667574 \nL 506.029075 149.782736 \nL 506.601187 154.737827 \nL 507.173299 158.10021 \nL 507.745411 164.117106 \nL 508.317523 155.091762 \nL 508.889635 115.981939 \nL 509.461747 95.276738 \nL 510.033859 102.178472 \nL 511.178084 151.021509 \nL 512.89442 304.452355 \nL 513.466532 330.997484 \nL 514.038644 348.340301 \nL 514.610756 352.056619 \nL 515.182868 348.871204 \nL 515.75498 329.404776 \nL 516.327092 277.022388 \nL 516.899205 184.822307 \nL 517.471317 155.9766 \nL 518.615541 158.277178 \nL 519.187653 155.799632 \nL 519.759765 160.754723 \nL 520.331877 170.310969 \nL 520.903989 171.549742 \nL 521.476101 170.664904 \nL 522.048213 155.622665 \nL 522.620326 108.195367 \nL 523.192438 76.872115 \nL 523.76455 80.234498 \nL 524.336662 95.276738 \nL 524.908774 117.220711 \nL 526.62511 261.449246 \nL 527.197222 289.941017 \nL 527.769334 305.337192 \nL 528.341447 311.531056 \nL 528.913559 307.991705 \nL 529.485671 288.879212 \nL 530.057783 235.965922 \nL 530.629895 149.251834 \nL 531.202007 116.689809 \nL 531.774119 118.813419 \nL 532.918343 134.032626 \nL 534.062567 154.914794 \nL 534.63468 156.330535 \nL 535.206792 156.507502 \nL 535.778904 146.597321 \nL 536.351016 101.647569 \nL 536.923128 88.551972 \nL 537.49524 103.771179 \nL 538.639464 143.411905 \nL 539.211576 180.752053 \nL 540.355801 282.685349 \nL 540.927913 318.432789 \nL 541.500025 338.43012 \nL 542.072137 350.286944 \nL 542.644249 355.772937 \nL 543.216361 351.879652 \nL 543.788473 332.767159 \nL 544.932697 256.14022 \nL 545.504809 213.668013 \nL 546.076922 183.052631 \nL 546.649034 170.487937 \nL 547.221146 169.957034 \nL 547.793258 175.089093 \nL 548.36537 184.468371 \nL 548.937482 189.069527 \nL 549.509594 182.875664 \nL 550.081706 139.872555 \nL 550.653818 121.821867 \nL 551.22593 140.403457 \nL 552.370155 190.3083 \nL 553.514379 270.474589 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\n"
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"
      },
      "metadata": {
       "needs_background": "light"
      }
     }
    ],
-   "source": [
-    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
-    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
-    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
-    "plt.xlabel('timestamp', fontsize=12)\n",
-    "plt.ylabel('load', fontsize=12)\n",
-    "plt.show()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "Training data shape:  (1416, 1)\nTest data shape:  (48, 1)\n"
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
      ]
     }
    ],
-   "source": [
-    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
-    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
-    "\n",
-    "print('Training data shape: ', train.shape)\n",
-    "print('Test data shape: ', test.shape)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -218,36 +305,104 @@
        "2014-11-01 08:00:00  0.40\n",
        "2014-11-01 09:00:00  0.48"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-01 00:00:00</th>\n      <td>0.10</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 01:00:00</th>\n      <td>0.07</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 02:00:00</th>\n      <td>0.05</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 03:00:00</th>\n      <td>0.04</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 04:00:00</th>\n      <td>0.06</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 05:00:00</th>\n      <td>0.10</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 06:00:00</th>\n      <td>0.19</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 07:00:00</th>\n      <td>0.31</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 08:00:00</th>\n      <td>0.40</td>\n    </tr>\n    <tr>\n      <th>2014-11-01 09:00:00</th>\n      <td>0.48</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 8
     }
    ],
-   "source": [
-    "scaler = MinMaxScaler()\n",
-    "train['load'] = scaler.fit_transform(train)\n",
-    "train.head(10)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Original vs scaled data:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
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\n"
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bX6WMDpoI/LKwfCHwW+DVtCeSNBIYDawAbgCWtW2LiK2SXgOOB1a2O64ZaAZoaGhIezqrkLwXA/Pefqt9aZPAKGBCRGyBD/7l/quIuDzNwZL6A3OBn0bESkmDSO5ALrYZGNz+2IiYCcwEaGpq8nxFZmZllHbaiJHAzqLlnYV1XZLUB5hTOGZKYXUrMKTdrkOALSnjMTOzMkjbE5gN/FbSgsLyF4CfdnWQJAH3kSSMz0XErsKmFcCVRfsNBI4urDczswpJ1ROIiL8Frgb+VHhdHRF3pjh0BnAscGFEbCtavwAYK2mypDqSm85+56KwmVlllXLD14HAuxExS1K9pCMj4vXOdi48jvKrJA+mfyvpFADw1YiYK2ky8EPgAZL7BC7pVgvMrGo8ZXbtSztE9NskI4TGALOA/iQ/3qd3dkzhxi/tY/si4JhSgjUzs/JKWxi+CPg8sBUgIt6kg5E8ZmZWW9ImgZ0RERSmky4Ucs3MrMalTQI/k3QvMFTSdcAi/IAZM7Oal3buoP9ZeLbwuyR1gW9FxMJMI7Oa5jtpy8+fqWWhyyQgqS+wqDCJnH/4zcx6kS4vB0XEHuB9SQdVIB4zM6ugtPcJtAIvSlpIYYQQQETclElUZmZWEWmTwPzCy8zMepF9JgFJDRHxRkR0OU+Q7R8X/awUvlPXyqWrmsDDbX+Q9M8Zx2JmZhXWVRIonvbhqCwDMTOzyusqCUQnfzYzs16gq8LweEnvkvQIDij8mcJyRET7B8OYmVkN2WcSiIi+lQrEKquzwmJxUTrNPj3Z/hZPSz3exVqrRWnnDjIzs14o0yQgaYqkFkk7JN1ftL5RUkhqLXrdlmUsZma2t1KeLNYdbwJ3AJOAAzrYPjQidmccg5mZdSLTJBAR8wEkNQGjsjyXmZmVLuueQFfWSAqS2UmnRsSG9jtIagaaARoaGiocXnWkKTDWSnHW8qnUO+B9x3z1VKswvAGYCBwBnEzyqMq5He0YETMjoikimurr6ysYoplZ71eVnkBEtAIthcW3JU0B1kkaHBFbqhGTmVke9ZQhom13I/eUeMzMciHTnoCkfoVz9AX6SqoDdpNcAtoEvAp8HLgLeCYiNmcZj5mZfVTWl4NuBb5dtHw58B1gFXAnMILkucULgUszjqVqXPT6kD8La9PZAAgXlSsr6yGi04BpnWx+KMtzm5lZ13wN3swsx5wEzMxyzEnAzCzHqn3HsHVTmqJarXJh0Kxy3BMwM8sxJwEzsxxzEjAzyzEnATOzHHNhuBs6K0T2tqJsLb5nmnPtq5DcG75Ds1K4J2BmlmNOAmZmOeYkYGaWY04CZmY55sLwfnIh8UP781n4c8yHcv0d8Z3h5eOegJlZjmWaBCRNkdQiaYek+9ttO1vSSknvSXpa0hFZxmJmZnvLuifwJnAH8JPilZKGA/OB24CDSR46Py/jWMzMrJ2snyw2H0BSEzCqaNMXgRUR8fPC9mnABknHRMTKLGMyM7MPVaswfDywrG0hIrZKeq2w/iNJQFIz0AzQ0NBQyRith6mVu5mtfPz9ZK9aheFBwOZ26zYDg9vvGBEzI6IpIprq6+srEpyZWV5UKwm0AkParRsCbKlCLGZmuVWtJLACGN+2IGkgcHRhvZmZVUjWQ0T7SaoD+gJ9JdVJ6gcsAMZKmlzY/i3gdy4Km5lVVtaF4VuBbxctXw58JyKmSZoM/BB4APgNcEnGseyXWi9Q1Xr8ZpaNrIeITgOmdbJtEXBMluc3M7N987QRZmY55iRgZpZjTgJmZjnmqaT3wXeoZsufRT70hO+5sxg8JbV7AmZmueYkYGaWY04CZmY55iRgZpZjLgy30xOKWPYhfx/WEf+9KB/3BMzMcsxJwMwsx5wEzMxyzEnAzCzHclsYLi4s+a7B0rgoZz2V/78unXsCZmY5VtUkIOkZSdsltRZeq6oZj5lZ3vSEnsCUiBhUeI2pdjBmZnnSE5KAmZlVSU9IAt+VtEHSryV9ptrBmJnlSbWTwM3AUcAngJnAI5KOLt5BUrOkFkkt69evr0aMZma9VlWTQET8JiK2RMSOiPgp8Gvgc+32mRkRTRHRVF9fX51Azcx6qWr3BNoLQNUOwswsL6qWBCQNlTRJUp2kfpIuA84EHq9WTGZmeVPNO4b7A3cAxwB7gJXAFyLilSrGZGaWK1VLAhGxHphYrfObWe+WZnoTTzPR82oCZmZWQU4CZmY55iRgZpZjTgJmZjmW2+cJmJl1Jk8FY/cEzMxyzEnAzCzHnATMzHLMScDMLMdyVRj2A9LNbH+k/Q2ppWKyewJmZjnmJGBmlmNOAmZmOeYkYGaWY7kqDJuZdaazom93BpSUesdxZ/tX4s5l9wTMzHKsqklA0sGSFkjaKmmNpK9UMx4zs7yp9uWgu4GdwEjgROBXkpZFxIrqhmVmlg/VfND8QGAycFtEtEbE88AvgSuqFZOZWd4oIqpzYukk4NcRcWDRur8GzoqIC4vWNQPNhcUxwKr9OO1wYMN+HF9r8tZecJvzwm0uzRERUd/RhmpeDhoEvNtu3WZgcPGKiJgJzCzHCSW1RERTOd6rFuStveA254XbXD7VLAy3AkParRsCbKlCLGZmuVTNJPAK0E/Sp4rWjQdcFDYzq5CqJYGI2ArMB26XNFDS6cB/BuZkeNqyXFaqIXlrL7jNeeE2l0nVCsOQ3CcA/AT4c2AjcEtEPFi1gMzMcqaqScDMzKrL00aYmeWYk4CZWY71qiSQdi4iJf5e0sbC6+8lqdLxlkMJbZ4qabmkLZJelzS10rGWS6lzTkn6mKSXJa2tVIzlVEp7JU2Q9KykVklvS/qrSsZaLiX8vR4g6UeFtv5R0iOSPlHpeMtB0hRJLZJ2SLq/i33/u6S3JL0r6SeSBnT3vL0qCfDRuYguA2ZIOr6D/ZqBL5AMST0BuBD4aqWCLLO0bRbwX4GPA+cBUyRdUrEoyyttm9tMBdZXIrCMpGqvpOHA48C9wDDgk8ATFYyznNJ+x38FfJrk/+PDgD8B0ysVZJm9CdxBMlimU5ImAbcAZwNHAEcB3+n2WSOiV7yAgSR/aUYXrZsD/F0H+74ANBct/yXwf6vdhizb3MGxdwHTq92GrNsMHAm8DPwFsLba8WfZXuBOYE61Y65wm2cA3ytaPh9YVe027Gf77wDu38f2B4E7i5bPBt7q7vl6U09gNLA7Il4pWrcM6OhfD8cXtnW1X09XSps/ULj0dQa1eWNeqW2eDnwT2JZ1YBkppb1/BvxR0guS3ilcGmmoSJTlVUqb7wNOl3SYpANJeg3/UoEYq6mj36+RkoZ15816UxJINRdR0b6b2+03qAbrAqW0udg0ku9+VgYxZS11myVdBPSNiAWVCCwjpXzHo4ArSS6RNACvAw9lGl02Smnzq8AfgP8oHHMscHum0VVfR79f0PX/9x3qTUmglLmI2u87BGiNQt+qhpQ8/5KkKSS1gfMjYkeGsWUlVZsLU5V/D7ipQnFlpZTveBuwICIWR8R2kuvEp0k6KOMYy62UNt8NDCCpgQwkmYWgt/cEOvr9gm7Ou9abkkApcxGtKGzrar+erqT5lyRdQ6GgFBE1OVKG9G3+FNAIPCfpLZIfh0MLIyoaKxBnuZTyHf8OKP6HTK39o6ZNKW0+keT6+R8L/6iZDpxSKJL3Vh39fr0dERu79W7VLoKUuaDyTyTd34HA6STdpOM72O96kmLhJ0hGFKwArq92/Bm3+TLgLeDYasdciTaTTJN+SNHriySjLw4huURU9XZk8B1/lmR0zIlAf+D7wHPVjj/jNs8C/hk4qNDmbwL/Ue34u9nmfkAd8F2SQngd0K+D/c4r/L98HDAUeIoUg0E6PW+1G17mD/Fg4GFgK/AG8JXC+jNILve07SeSSwV/LLy+R2EKjVp7ldDm14FdJF3JttePqh1/lm1ud8xnqMHRQaW2F7iB5Pr4n4BHgMOrHX+WbSa5DDQXeAfYBDwPnFLt+LvZ5mkkvbfi1zSS+k4r0FC07zeAt0nqILOAAd09r+cOMjPLsd5UEzAzsxI5CZiZ5ZiTgJlZjjkJmJnlmJOAmVmOOQmYmeWYk4CZWY45CZiZ5dj/BywbGaIaCXKXAAAAAElFTkSuQmCC"
      },
      "metadata": {
       "needs_background": "light"
      }
     }
    ],
-   "source": [
-    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
-    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
-    "plt.show()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Let's also scale the test data"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -294,28 +450,75 @@
        "2014-12-30 03:00:00  0.27\n",
        "2014-12-30 04:00:00  0.30"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-12-30 00:00:00</th>\n      <td>0.33</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 01:00:00</th>\n      <td>0.29</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 02:00:00</th>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 03:00:00</th>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 04:00:00</th>\n      <td>0.30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 10
     }
    ],
-   "source": [
-    "test['load'] = scaler.transform(test)\n",
-    "test.head()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Implement ARIMA method"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -325,25 +528,11 @@
      ]
     }
    ],
-   "source": [
-    "# Specify the number of steps to forecast ahead\n",
-    "HORIZON = 3\n",
-    "print('Forecasting horizon:', HORIZON, 'hours')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "                                     SARIMAX Results                                      \n==========================================================================================\nDep. Variable:                               load   No. Observations:                 1416\nModel:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\nDate:                            Fri, 14 May 2021   AIC                          -6942.477\nTime:                                    17:05:41   BIC                          -6911.050\nSample:                                11-01-2014   HQIC                         -6930.725\n                                     - 12-29-2014                                         \nCovariance Type:                              opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\nar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\nar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\nar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\nar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\nsigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n===================================================================================\nLjung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\nProb(Q):                              0.83   Prob(JB):                         0.00\nHeteroskedasticity (H):               0.84   Skew:                             0.14\nProb(H) (two-sided):                  0.07   Kurtosis:                         8.02\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
-     ]
-    }
-   ],
    "source": [
     "order = (4, 1, 0)\n",
     "seasonal_order = (1, 1, 0, 24)\n",
@@ -352,26 +541,70 @@
     "results = model.fit()\n",
     "\n",
     "print(results.summary())\n"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Fri, 14 May 2021   AIC                          -6942.477\n",
+      "Time:                                    17:05:41   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Evaluate the model"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Create a test data point for each HORIZON step."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON+1):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -384,35 +617,110 @@
        "2014-12-30 03:00:00  0.27    0.30    0.41\n",
        "2014-12-30 04:00:00  0.30    0.41    0.57"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n      <th>load+1</th>\n      <th>load+2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-12-30 00:00:00</th>\n      <td>0.33</td>\n      <td>0.29</td>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 01:00:00</th>\n      <td>0.29</td>\n      <td>0.27</td>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 02:00:00</th>\n      <td>0.27</td>\n      <td>0.27</td>\n      <td>0.30</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 03:00:00</th>\n      <td>0.27</td>\n      <td>0.30</td>\n      <td>0.41</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 04:00:00</th>\n      <td>0.30</td>\n      <td>0.41</td>\n      <td>0.57</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 13
     }
    ],
-   "source": [
-    "test_shifted = test.copy()\n",
-    "\n",
-    "for t in range(1, HORIZON):\n",
-    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
-    "    \n",
-    "test_shifted = test_shifted.dropna(how='any')\n",
-    "test_shifted.head(5)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Make predictions on the test data"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -515,46 +823,28 @@
      ]
     }
    ],
-   "source": [
-    "%%time\n",
-    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
-    "\n",
-    "train_ts = train['load']\n",
-    "test_ts = test_shifted\n",
-    "\n",
-    "history = [x for x in train_ts]\n",
-    "history = history[(-training_window):]\n",
-    "\n",
-    "predictions = list()\n",
-    "\n",
-    "# let's user simpler model for demonstration\n",
-    "order = (2, 1, 0)\n",
-    "seasonal_order = (1, 1, 0, 24)\n",
-    "\n",
-    "for t in range(test_ts.shape[0]):\n",
-    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
-    "    model_fit = model.fit()\n",
-    "    yhat = model_fit.forecast(steps = HORIZON)\n",
-    "    predictions.append(yhat)\n",
-    "    obs = list(test_ts.iloc[t])\n",
-    "    # move the training window\n",
-    "    history.append(obs[0])\n",
-    "    history.pop(0)\n",
-    "    print(test_ts.index[t])\n",
-    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
-   ]
+   "metadata": {
+    "scrolled": true
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Compare predictions to actual load"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -567,53 +857,116 @@
        "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
        "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>h</th>\n      <th>prediction</th>\n      <th>actual</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-12-30 00:00:00</td>\n      <td>t+1</td>\n      <td>3,008.74</td>\n      <td>3,023.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-12-30 01:00:00</td>\n      <td>t+1</td>\n      <td>2,955.53</td>\n      <td>2,935.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-12-30 02:00:00</td>\n      <td>t+1</td>\n      <td>2,900.17</td>\n      <td>2,899.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-12-30 03:00:00</td>\n      <td>t+1</td>\n      <td>2,917.69</td>\n      <td>2,886.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-12-30 04:00:00</td>\n      <td>t+1</td>\n      <td>2,946.99</td>\n      <td>2,963.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 15
     }
    ],
-   "source": [
-    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
-    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
-    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
-    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
-    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
-    "eval_df.head()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Compute the **mean absolute percentage error (MAPE)** over all predictions\n",
     "\n",
     "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "h\nt+1   0.01\nt+2   0.01\nt+3   0.02\nName: APE, dtype: float64\n"
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
      ]
     }
    ],
-   "source": [
-    "if(HORIZON > 1):\n",
-    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
-    "    print(eval_df.groupby('h')['APE'].mean())"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -623,14 +976,14 @@
      ]
     }
    ],
-   "source": [
-    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -640,41 +993,18 @@
      ]
     }
    ],
-   "source": [
-    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Plot the predictions vs the actuals for the first week of the test set"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 1080x576 with 1 Axes>",
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-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
    "source": [
     "if(HORIZON == 1):\n",
     "    ## Plotting single step forecast\n",
@@ -699,14 +1029,37 @@
     "plt.xlabel('timestamp', fontsize=12)\n",
     "plt.ylabel('load', fontsize=12)\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ],
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"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "source": [],
    "outputs": [],
-   "source": []
+   "metadata": {}
   }
  ],
  "metadata": {
@@ -714,8 +1067,8 @@
    "name": "python3"
   },
   "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
+   "name": "python3",
+   "display_name": "Python 3.8.10 64-bit ('tmp': conda)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -727,7 +1080,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.8.10"
   },
   "nteract": {
    "version": "nteract-front-end@1.0.0"
@@ -736,6 +1089,9 @@
    "interpreter": {
     "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
    }
+  },
+  "interpreter": {
+   "hash": "205095b8b86ea82a021a8fc2ffba2b3bc9f8d0be25999901963b225939f75778"
   }
  },
  "nbformat": 4,
diff --git a/7-TimeSeries/2-ARIMA/translations/README.it.md b/7-TimeSeries/2-ARIMA/translations/README.it.md
new file mode 100644
index 000000000..0b08ee57b
--- /dev/null
+++ b/7-TimeSeries/2-ARIMA/translations/README.it.md
@@ -0,0 +1,394 @@
+# Previsione delle serie temporali con ARIMA
+
+Nella lezione precedente, si è imparato qualcosa sulla previsione delle serie temporali e si è caricato un insieme di dati che mostra le fluttuazioni del carico elettrico in un periodo di tempo.
+
+[![Introduzione ad ARIMA](https://img.youtube.com/vi/IUSk-YDau10/0.jpg)](https://youtu.be/IUSk-YDau10 " Introduzione ad ARIMA")
+
+> 🎥 Fare clic sull'immagine sopra per un video: Una breve introduzione ai modelli ARIMA. L'esempio è fatto in linguaggio R, ma i concetti sono universali.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/?loc=it)
+
+## Introduzione
+
+In questa lezione si scoprirà un modo specifico per costruire modelli con [ARIMA: *AutoRegressive Integrated Moving Average*](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average) (Media mobile integrata autoregressiva). I modelli ARIMA sono particolarmente indicati per l'adattamento di dati che mostrano [non stazionarietà](https://it.wikipedia.org/wiki/Processo_stazionario).
+
+## Concetti generali
+
+Per poter lavorare con ARIMA, ci sono alcuni concetti da conoscere:
+
+- 🎓 **Stazionarietà**. In un contesto statistico, la stazionarietà si riferisce a dati la cui distribuzione non cambia se spostata nel tempo. I dati non stazionari, poi, mostrano fluttuazioni dovute a andamenti che devono essere trasformati per essere analizzati. La stagionalità, ad esempio, può introdurre fluttuazioni nei dati e può essere eliminata mediante un processo di "differenziazione stagionale".
+
+- 🎓 **[Differenziazione](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing)**. I dati differenzianti, sempre in un contesto statistico, si riferiscono al processo di trasformazione dei dati non stazionari per renderli stazionari rimuovendo il loro andamento non costante. "La differenziazione rimuove le variazioni di livello di una serie temporale, eliminando tendenza e stagionalità e stabilizzando di conseguenza la media delle serie temporali." [Documento di Shixiong e altri](https://arxiv.org/abs/1904.07632)
+
+## ARIMA nel contesto delle serie temporali
+
+Si esaminano le parti di ARIMA per capire meglio come aiuta a modellare le serie temporali e a fare previsioni contro di esso.
+
+- **AR - per AutoRegressivo**. I modelli autoregressivi, come suggerisce il nome, guardano "indietro" nel tempo per analizzare i valori precedenti nei dati e fare ipotesi su di essi. Questi valori precedenti sono chiamati "ritardi". Un esempio potrebbero essere i dati che mostrano le vendite mensili di matite. Il totale delle vendite di ogni mese sarebbe considerato una "variabile in evoluzione" nell'insieme di dati. Questo modello è costruito come "la variabile di interesse in evoluzione è regredita sui propri valori ritardati (cioè precedenti)". [wikipedia](https://it.wikipedia.org/wiki/Modello_autoregressivo_a_media_mobile)
+
+- **I - per integrato**. A differenza dei modelli simili "ARMA", la "I" in ARIMA si riferisce al suo aspetto *[integrato](https://wikipedia.org/wiki/Order_of_integration)* . I dati vengono "integrati" quando vengono applicati i passaggi di differenziazione in modo da eliminare la non stazionarietà.
+
+- **MA - per Media Mobile**. L'aspetto della [media mobile](https://it.wikipedia.org/wiki/Modello_a_media_mobile) di questo modello si riferisce alla variabile di output che è determinata osservando i valori attuali e passati dei ritardi.
+
+In conclusione: ARIMA viene utilizzato per adattare il più possibile un modello alla forma speciale dei dati delle serie temporali.
+
+## Esercizio: costruire un modello ARIMA
+
+Aprire la cartella _/working_ in questa lezione e trovare il file _notebook.ipynb_.
+
+1. Eseguire il notebook per caricare la libreria Python `statsmodels`; servirà per i modelli ARIMA.
+
+1. Caricare le librerie necessarie
+
+1. Ora caricare molte altre librerie utili per tracciare i dati:
+
+   ```python
+   import os
+   import warnings
+   import matplotlib.pyplot as plt
+   import numpy as np
+   import pandas as pd
+   import datetime as dt
+   import math
+
+   from pandas.plotting import autocorrelation_plot
+   from statsmodels.tsa.statespace.sarimax import SARIMAX
+   from sklearn.preprocessing import MinMaxScaler
+   from common.utils import load_data, mape
+   from IPython.display import Image
+
+   %matplotlib inline
+   pd.options.display.float_format = '{:,.2f}'.format
+   np.set_printoptions(precision=2)
+   warnings.filterwarnings("ignore")  # specificare per ignorare messaggi di avvertimento
+   ```
+
+1. Caricare i dati dal file `/data/energy.csv` in un dataframe Pandas e dare un'occhiata:
+
+   ```python
+   energy = load_data('./data')[['load']]
+   energy.head(10)
+   ```
+
+1. Tracciare tutti i dati energetici disponibili da gennaio 2012 a dicembre 2014. Non dovrebbero esserci sorprese poiché questi dati sono stati visti nell'ultima lezione:
+
+   ```python
+   energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)
+   plt.xlabel('timestamp', fontsize=12)
+   plt.ylabel('load', fontsize=12)
+   plt.show()
+   ```
+
+   Ora si costruisce un modello!
+
+### Creare insiemi di dati di addestramento e test
+
+Ora i dati sono stati caricati, quindi si possono separare in insiemi di addestramento e test. Si addestrerà il modello sull'insieme di addestramento. Come al solito, dopo che il modello ha terminato l'addestramento, se ne valuterà l'accuratezza utilizzando l'insieme di test. È necessario assicurarsi che l'insieme di test copra un periodo successivo dall'insieme di addestramento per garantire che il modello non ottenga informazioni da periodi di tempo futuri.
+
+1. Assegnare un periodo di due mesi dal 1 settembre al 31 ottobre 2014 all'insieme di addestramento. L'insieme di test comprenderà il bimestre dal 1 novembre al 31 dicembre 2014:
+
+   ```python
+   train_start_dt = '2014-11-01 00:00:00' 
+   test_start_dt = '2014-12-30 00:00:00'
+   ```
+
+   Poiché questo dato riflette il consumo giornaliero di energia, c'è un forte andamento stagionale, ma il consumo è più simile al consumo nei giorni più recenti.
+
+1. Visualizzare le differenze:
+
+   ```python
+   energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \
+       .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \
+       .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)
+   plt.xlabel('timestamp', fontsize=12)
+   plt.ylabel('load', fontsize=12)
+   plt.show()
+   ```
+
+   ![Addestrare e testare i dati](../images/train-test.png)
+
+   Pertanto, l'utilizzo di una finestra di tempo relativamente piccola per l'addestramento dei dati dovrebbe essere sufficiente.
+
+   > Nota: poiché la funzione utilizzata per adattare il modello ARIMA usa la convalida nel campione durante l'adattamento, si omettono i dati di convalida.
+
+### Preparare i dati per l'addestramento
+
+Ora è necessario preparare i dati per l'addestramento eseguendo il filtraggio e il ridimensionamento dei dati. Filtrare l'insieme di dati per includere solo i periodi di tempo e le colonne che servono e il ridimensionamento per garantire che i dati siano proiettati nell'intervallo 0,1.
+
+1. Filtrare l'insieme di dati originale per includere solo i suddetti periodi di tempo per insieme e includendo solo la colonna necessaria "load" più la data:
+
+   ```python
+   train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]
+   test = energy.copy()[energy.index >= test_start_dt][['load']]
+
+   print('Training data shape: ', train.shape)
+   print('Test data shape: ', test.shape)
+   ```
+
+   Si può vedere la forma dei dati:
+
+   ```output
+   Training data shape:  (1416, 1)
+   Test data shape:  (48, 1)
+   ```
+
+1. Ridimensionare i dati in modo che siano nell'intervallo (0, 1).
+
+   ```python
+   scaler = MinMaxScaler()
+   train['load'] = scaler.fit_transform(train)
+   train.head(10)
+   ```
+
+1. Visualizzare i dati originali rispetto ai dati in scala:
+
+   ```python
+   energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)
+   train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)
+   plt.show()
+   ```
+
+   ![originale](../images/original.png)
+
+   > I dati originali.
+
+   ![scaled](../images/scaled.png)
+
+   > I dati in scala
+
+1. Ora che si è calibrato i dati scalati, si possono scalare i dati del test:
+
+   ```python
+   test['load'] = scaler.transform(test)
+   test.head()
+   ```
+
+### Implementare ARIMA
+
+È tempo di implementare ARIMA! Si utilizzerà ora la libreria `statsmodels`  installata in precedenza.
+
+Ora occorre seguire diversi passaggi
+
+1. Definire il modello chiamando `SARIMAX()` passando i parametri del modello: parametri p, d e q e parametri P, D e Q.
+2. Preparare il modello per i dati di addestramento chiamando la funzione fit().
+3. Effettuare previsioni chiamando la funzione `forecast()` specificando il numero di passaggi (l'orizzonte - `horizon`) da prevedere.
+
+> 🎓 A cosa servono tutti questi parametri? In un modello ARIMA ci sono 3 parametri che vengono utilizzati per aiutare a modellare gli aspetti principali di una serie temporale: stagionalità, tendenza e rumore. Questi parametri sono:
+
+`p`: il parametro associato all'aspetto autoregressivo del modello, che incorpora i valori *passati*.
+`d`: il parametro associato alla parte integrata del modello, che incide sulla quantità di *differenziazione* (🎓 si ricorda la differenziazione 👆?) da applicare a una serie temporale.
+`q`: il parametro associato alla parte a media mobile del modello.
+
+> Nota: se i  dati hanno un aspetto stagionale, come questo, si utilizza un modello ARIMA stagionale (SARIMA). In tal caso è necessario utilizzare un altro insieme di parametri: `P`, `D` e `Q` che descrivono le stesse associazioni di `p`, `d` e `q` , ma corrispondono alle componenti stagionali del modello.
+
+1. Iniziare impostando il valore di orizzonte preferito. Si prova 3 ore:
+
+   ```python
+   # Specificare il numero di passaggi per prevedere in anticipo
+   HORIZON = 3
+   print('Forecasting horizon:', HORIZON, 'hours')
+   ```
+
+   La selezione dei valori migliori per i parametri di un modello ARIMA può essere difficile in quanto è in qualche modo soggettiva e richiede molto tempo. Si potrebbe prendere in considerazione l'utilizzo di una funzione `auto_arima()` dalla [libreria `pyramid`](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html),
+
+1. Per ora provare alcune selezioni manuali per trovare un buon modello.
+
+   ```python
+   order = (4, 1, 0)
+   seasonal_order = (1, 1, 0, 24)
+
+   model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)
+   results = model.fit()
+
+   print(results.summary())
+   ```
+
+   Viene stampata una tabella dei risultati.
+
+Si è costruito il primo modello! Ora occorre trovare un modo per valutarlo.
+
+### Valutare il modello
+
+Per valutare il  modello, si può eseguire la cosiddetta convalida `walk forward` . In pratica, i modelli di serie temporali vengono riaddestrati ogni volta che diventano disponibili nuovi dati. Ciò consente al modello di effettuare la migliore previsione in ogni fase temporale.
+
+A partire dall'inizio della serie temporale utilizzando questa tecnica, addestrare il modello sull'insieme di dati di addestramento Quindi fare una previsione sul passaggio temporale successivo. La previsione viene valutata rispetto al valore noto. L'insieme di addestramento viene quindi ampliato per includere il valore noto e il processo viene ripetuto.
+
+> Nota: è necessario mantenere fissa la finestra dell'insieme di addestramento per un addestramento più efficiente in modo che ogni volta che si aggiunge una nuova osservazione all'insieme di addestramento, si rimuove l'osservazione dall'inizio dell'insieme.
+
+Questo processo fornisce una stima più solida di come il modello si comporterà in pratica. Tuttavia, ciò comporta il costo computazionale della creazione di così tanti modelli. Questo è accettabile se i dati sono piccoli o se il modello è semplice, ma potrebbe essere un problema su larga scala.
+
+La convalida walk-forward è lo standard di riferimento per valutazione del modello di serie temporali ed è consigliata per i propri progetti.
+
+1. Innanzitutto, creare un punto dati di prova per ogni passaggio HORIZON.
+
+   ```python
+   test_shifted = test.copy()
+
+   for t in range(1, HORIZON):
+       test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')
+
+   test_shifted = test_shifted.dropna(how='any')
+   test_shifted.head(5)
+   ```
+
+   |            |          | load | load 1 | load 2 |
+   | ---------- | -------- | ---- | ------ | ------ |
+   | 2014/12/30 | 00:00:00 | 0,33 | 0.29 | 0,27 |
+   | 2014/12/30 | 00:01:00:00 | 0.29 | 0,27 | 0,27 |
+   | 2014/12/30 | 02:00:00 | 0,27 | 0,27 | 0,30 |
+   | 2014/12/30 | 03:00:00 | 0,27 | 0,30 | 0.41 |
+   | 2014/12/30 | 04:00 | 0,30 | 0.41 | 0,57 |
+
+   I dati vengono spostati orizzontalmente in base al loro punto horizon.
+
+1. Fare previsioni sui dati di test utilizzando questo approccio a finestra scorrevole in un ciclo della dimensione della lunghezza dei dati del test:
+
+   ```python
+   %%time
+   training_window = 720 # dedicare 30 giorni (720 ore) for l'addestramento
+
+   train_ts = train['load']
+   test_ts = test_shifted
+
+   history = [x for x in train_ts]
+   history = history[(-training_window):]
+
+   predictions = list()
+
+   order = (2, 1, 0)
+   seasonal_order = (1, 1, 0, 24)
+
+   for t in range(test_ts.shape[0]):
+       model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)
+       model_fit = model.fit()
+       yhat = model_fit.forecast(steps = HORIZON)
+       predictions.append(yhat)
+       obs = list(test_ts.iloc[t])
+       # move the training window
+       history.append(obs[0])
+       history.pop(0)
+       print(test_ts.index[t])
+       print(t+1, ': predicted =', yhat, 'expected =', obs)
+   ```
+
+   Si può guardare l'addestramento in corso:
+
+   ```output
+   2014-12-30 00:00:00
+   1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]
+
+   2014-12-30 01:00:00
+   2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]
+
+   2014-12-30 02:00:00
+   3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]
+   ```
+
+1. Confrontare le previsioni con il carico effettivo:
+
+   ```python
+   eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])
+   eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]
+   eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')
+   eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()
+   eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])
+   eval_df.head()
+   ```
+
+   ```output
+   |     |            | timestamp | h   | prediction | actual   |
+   | --- | ---------- | --------- | --- | ---------- | -------- |
+   | 0   | 2014-12-30 | 00:00:00  | t+1 | 3,008.74   | 3,023.00 |
+   | 1   | 2014-12-30 | 01:00:00  | t+1 | 2,955.53   | 2,935.00 |
+   | 2   | 2014-12-30 | 02:00:00  | t+1 | 2,900.17   | 2,899.00 |
+   | 3   | 2014-12-30 | 03:00:00  | t+1 | 2,917.69   | 2,886.00 |
+   | 4   | 2014-12-30 | 04:00:00  | t+1 | 2,946.99   | 2,963.00 |
+   ```
+
+   Osservare la previsione dei dati orari, rispetto al carico effettivo. Quanto è accurato questo?
+
+### Controllare la precisione del modello
+
+Controllare l'accuratezza del modello testando il suo errore percentuale medio assoluto (MAPE) su tutte le previsioni.
+
+> **🧮 Mostrami la matematica!**
+>
+> ![MAPE (%)](../images/mape.png)
+>
+> [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) viene utilizzato per mostrare l'accuratezza della previsione come un rapporto definito dalla formula qui sopra. La differenza tra actual<sub>t</sub> e predicted<sub>t</sub> viene divisa per actual<sub>t</sub>. "Il valore assoluto in questo calcolo viene sommato per ogni punto nel tempo previsto e diviso per il numero di punti adattati n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error)
+
+1. Equazione espressa in codice:
+
+   ```python
+   if(HORIZON > 1):
+       eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']
+       print(eval_df.groupby('h')['APE'].mean())
+   ```
+
+1. Calcolare il MAPE di un passo:
+
+   ```python
+   print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')
+   ```
+
+   Previsione a un passo MAPE: 0,5570581332313952 %
+
+1. Stampare la previsione a più fasi MAPE:
+
+   ```python
+   print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')
+   ```
+
+   ```output
+   Multi-step forecast MAPE:  1.1460048657704118 %
+   ```
+
+   Un bel numero basso è il migliore: si consideri che una previsione che ha un MAPE di 10 è fuori dal 10%.
+
+1. Ma come sempre, è più facile vedere visivamente questo tipo di misurazione dell'accuratezza, quindi si traccia:
+
+   ```python
+    if(HORIZON == 1):
+       ## Tracciamento previsione passo singolo
+       eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))
+
+   else:
+       ## Tracciamento posizione passo multiplo
+       plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]
+       for t in range(1, HORIZON+1):
+           plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values
+
+       fig = plt.figure(figsize=(15, 8))
+       ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)
+       ax = fig.add_subplot(111)
+       for t in range(1, HORIZON+1):
+           x = plot_df['timestamp'][(t-1):]
+           y = plot_df['t+'+str(t)][0:len(x)]
+           ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))
+
+       ax.legend(loc='best')
+
+   plt.xlabel('timestamp', fontsize=12)
+   plt.ylabel('load', fontsize=12)
+   plt.show()
+   ```
+
+   ![un modello di serie temporale](../images/accuracy.png)
+
+🏆 Un grafico molto bello, che mostra un modello con una buona precisione. Ottimo lavoro!
+
+---
+
+## 🚀 Sfida
+
+Scoprire i modi per testare l'accuratezza di un modello di serie temporali. Si esamina MAPE in questa lezione, ma ci sono altri metodi che si potrebbero usare? Ricercarli e annotarli. Un documento utile può essere trovato [qui](https://otexts.com/fpp2/accuracy.html)
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Questa lezione tratta solo le basi della previsione delle serie temporali con ARIMA. SI prenda del tempo per approfondire le proprie conoscenze esaminando [questo repository](https://microsoft.github.io/forecasting/) e i suoi vari tipi di modelli per imparare altri modi per costruire modelli di serie temporali.
+
+## Compito
+
+[Un nuovo modello ARIMA](assignment.it.md)
diff --git a/7-TimeSeries/2-ARIMA/translations/README.ko.md b/7-TimeSeries/2-ARIMA/translations/README.ko.md
new file mode 100644
index 000000000..7ed625539
--- /dev/null
+++ b/7-TimeSeries/2-ARIMA/translations/README.ko.md
@@ -0,0 +1,394 @@
+# ARIMA로 Time series forecasting 하기
+
+이전 강의에서, time series forecasting에 대해 약간 배웠고 시간대 간격으로 전력 부하의 파동을 보여주는 데이터셋도 불러왔습니다.
+
+[![Introduction to ARIMA](https://img.youtube.com/vi/IUSk-YDau10/0.jpg)](https://youtu.be/IUSk-YDau10 "Introduction to ARIMA")
+
+> 🎥 영상을 보려면 이미지 클릭: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/)
+
+## 소개
+
+이 강의에서, [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average)로 모델을 만드는 상세한 방식을 살펴볼 예정입니다. ARIMA 모델은 [non-stationarity](https://wikipedia.org/wiki/Stationary_process)를 보여주는 데이터에 특히 알맞습니다.
+
+## 일반적인 컨셉
+
+ARIMA로 작업하려고 한다면, 일부 컨셉을 알 필요가 있습니다:
+
+- 🎓 **Stationarity**. 통계 컨텍스트에서, stationarity는 시간이 지나면서 분포가 변경되지 않는 데이터를 나타냅니다. Non-stationary 데이터라면, 분석하기 위해서 변환이 필요한 트랜드로 파동을 보여줍니다. 예시로, Seasonality는, 데이터에 파동을 나타나게 할 수 있고 'seasonal-differencing' 처리로 뺄 수 있습니다.
+
+- 🎓 **[Differencing](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing)**. Differencing 데이터는, 통계 컨텍스트에서 다시 언급하자면, non-stationary 데이터를 non-constant 트랜드로 지워서 움직이지 않게 변형시키는 프로세스를 나타냅니다. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series." [Paper by Shixiong et al](https://arxiv.org/abs/1904.07632)
+
+## Time series의 컨텍스트에서 ARIMA
+
+ARIMA의 파트를 언팩해서 어떻게 time series 모델을 만들고 예측하는 데에 도움을 주는지 더 이해합니다.
+
+- **AR - for AutoRegressive**. 이름에서 추측하듯, Autoregressive 모델은, 데이터에서 이전 값을 분석하고 가정하기 위해서 시간을 'back' 합니다. 이전 값은 'lags'이라고 불립니다. 예시로 연필의 월별 판매를 보여주는 데이터가 존재합니다. 각 월별 판매 총액은 데이터셋에서 'evolving variable'으로 생각됩니다. 이 모델은 "evolving variable of interest is regressed on its own lagged (i.e., prior) values."로 만들어졌습니다. [wikipedia](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average)
+
+- **I - for Integrated**. 비슷한 'ARMA' 모델과 다르게, ARIMA의 'I'는 *[integrated](https://wikipedia.org/wiki/Order_of_integration)* 측면을 나타냅니다. non-stationarity를 제거하기 위해서 differencing 단계가 적용될 때 데이터는 'integrated'됩니다.
+
+- **MA -  for Moving Average**. 이 모델의 [moving-average](https://wikipedia.org/wiki/Moving-average_model) 측면에서 lags의 현재와 과거 값을 지켜봐서 결정하는 출력 변수를 나타냅니다.
+
+결론: ARIMA는 가능한 근접하게 time series 데이터의 스페셜 폼에 맞는 모델을 만들기 위해서 사용합니다.
+
+## 연습 - ARIMA 모델 만들기
+
+이 강의의 _/working_ 폴더를 열고 _notebook.ipynb_ 파일을 찾습니다.
+
+1. 노트북을 실행해서 `statsmodels` Python 라이브러리를 불러옵니다; ARIMA 모델이 필요할 예정입니다.
+
+1. 필요한 라이브러리를 불러옵니다
+
+1. 지금부터, 데이터를 plot할 때 유용한 여러 라이브러리를 불러옵니다:
+
+    ```python
+    import os
+    import warnings
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import pandas as pd
+    import datetime as dt
+    import math
+    
+    from pandas.plotting import autocorrelation_plot
+    from statsmodels.tsa.statespace.sarimax import SARIMAX
+    from sklearn.preprocessing import MinMaxScaler
+    from common.utils import load_data, mape
+    from IPython.display import Image
+    
+    %matplotlib inline
+    pd.options.display.float_format = '{:,.2f}'.format
+    np.set_printoptions(precision=2)
+    warnings.filterwarnings("ignore") # specify to ignore warning messages
+    ```
+
+1. `/data/energy.csv` 파일의 데이터를 Pandas 데이터프레임으로 불러오고 찾아봅니다:
+
+    ```python
+    energy = load_data('./data')[['load']]
+    energy.head(10)
+    ```
+
+1. January 2012부터 December 2014까지 유효한 에너지 데이터를 모두 plot합니다. 지난 강의에서 보았던 데이터라서 놀랍지 않습니다:
+
+    ```python
+    energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)
+    plt.xlabel('timestamp', fontsize=12)
+    plt.ylabel('load', fontsize=12)
+    plt.show()
+    ```
+
+    지금부터, 모델을 만들어봅시다!
+
+### 훈련과 테스트 데이터셋 만들기
+
+이제 데이터를 불러왔으면, 훈련과 테스트 셋으로 나눌 수 있습니다. 훈련 셋으로 모델을 훈련할 수 있습니다. 평소처럼, 모델 훈련이 끝나면, 데이터셋으로 정확도를 평가합니다. 모델이 미래에서 정보를 못 얻도록 테스트셋이 훈련 셋의 이후 기간을 커버하는지 확인할 필요가 있습니다.
+
+1. 2014년 September 1 부터 October 31 까지 2개월간 훈련 셋에 할당합니다. 테스트셋은 2014년 November 1 부터 December 31 까지 2개월간 포함합니다:
+
+    ```python
+    train_start_dt = '2014-11-01 00:00:00'
+    test_start_dt = '2014-12-30 00:00:00'    
+    ```
+
+    이 데이터는 에너지의 일일 소비 수량을 반영하므로, 강한 계절적 패턴이 있지만, 소비 수량은 최근 날짜와 매우 비슷합니다.
+
+1. 다른 점을 시각화합니다:
+
+    ```python
+    energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \
+        .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \
+        .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)
+    plt.xlabel('timestamp', fontsize=12)
+    plt.ylabel('load', fontsize=12)
+    plt.show()
+    ```
+
+    ![training and testing data](../images/train-test.png)
+
+    그래서, 데이터를 훈련하면 상대적으로 적은 시간대로도 충분해야 합니다.
+
+    > 노트: ARIMA 모델을 fit할 때 사용하는 함수는 fitting하는 동안 in-sample 검증하므로, 검증 데이터를 생략할 예정입니다.
+
+### 훈련을 위한 데이터 준비하기
+
+지금부터, 데이터 필터링하고 스케일링한 훈련 데이터를 준비할 필요가 있습니다. 필요한 시간대와 열만 포함된 데이터셋을 필터링하고, 0,1 간격으로 데이터를 예측하도록 확장합니다.
+
+1. 세트 별로 앞서 말해둔 기간만 포함하고 날짜가 추가된 'load' 열만 포함해서 원본 데이터셋을 필터링합니다:
+
+    ```python
+    train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]
+    test = energy.copy()[energy.index >= test_start_dt][['load']]
+    
+    print('Training data shape: ', train.shape)
+    print('Test data shape: ', test.shape)
+    ```
+ 
+    데이터의 모양을 볼 수 있습니다:
+
+    ```output
+    Training data shape:  (1416, 1)
+    Test data shape:  (48, 1)
+    ```
+
+1. (0, 1) 범위로 데이터를 스케일링합니다:
+
+    ```python
+    scaler = MinMaxScaler()
+    train['load'] = scaler.fit_transform(train)
+    train.head(10)
+    ```
+
+1. 원본 vs. 스케일된 데이터를 시각화합니다:
+
+    ```python
+    energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)
+    train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)
+    plt.show()
+    ```
+
+    ![original](../images/original.png)
+
+    > 원본 데이터
+
+    ![scaled](../images/scaled.png)
+
+    > 스케일된 데이터
+
+1. 지금부터 스케일된 데이터를 보정했으므로, 테스트 데이터로 스케일할 수 있습니다:
+
+    ```python
+    test['load'] = scaler.transform(test)
+    test.head()
+    ```
+
+### ARIMA 구현하기
+
+ARIMA를 구현할 시간입니다! 미리 설치해둔 `statsmodels` 라이브러리를 지금 사용하겠습니다.
+
+이제 다음 몇 단계가 필요합니다
+
+   1. `SARIMAX()`을 불러서 데이터를 정의하고 모델 파라미터를 전달합니다: p, d, 그리고 q 파라미터와, P, D, 그리고 Q 파라미터.
+   2. fit() 함수를 불러서 훈련 데이터을 위한 모델을 준비합니다.
+   3. `forecast()` 함수를 부르고 예측할 단계 숫자를 (`horizon`) 지정해서 예측합니다.
+
+> 🎓 모든 파라미터는 무엇을 위해서 있나요? ARIMA 모델에 time series의 주요 측면을 모델링 도울 때 사용하는 3개 파라미터가 있습니다: seasonality, trend, 그리고 noise. 파라미터는 이렇습니다:
+
+`p`: *past* 값을 합치는, 모델의 auto-regressive 측면과 관련있는 파라미터입니다.
+`d`: time series에 적용할 *differencing* (🎓 differencing을 기억하나요 👆?) 결과에 영향받는, 모델의 통합 파트와 관련있는 파라미터입니다.
+`q`: 모델의 moving-average 파트와 관련있는 파라미터입니다.
+
+> 노트: 데이터에 - 이러한 것처럼 - 계절적 측면이 있다면, seasonal ARIMA 모델 (SARIMA)을 사용합니다. 이러한 케이스에는 다른 파라미터 셋을 사용할 필요가 있습니다: `P`, `D`와, `Q`는  `p`, `d`와, `q`처럼 같은 집단이라는 점을 설명하지만, 모델의 계절적 컴포넌트에 대응합니다.
+
+1. 선호하는 horizon 값을 세팅하며 시작합니다. 3시간 동안 시도해봅시다:
+
+    ```python
+    # Specify the number of steps to forecast ahead
+    HORIZON = 3
+    print('Forecasting horizon:', HORIZON, 'hours')
+    ```
+
+    ARIMA 파라미터의 최적 값을 선택하는 것은 다소 주관적이고 시간이 많이 지나므로 어려울 수 있습니다. [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html)에서 `auto_arima()` 함수로 사용하는 것을 고려할 수 있습니다.
+
+1. 지금 당장 좋은 모델을 찾고자 약간 수동으로 선택하려고 합니다.
+
+    ```python
+    order = (4, 1, 0)
+    seasonal_order = (1, 1, 0, 24)
+    
+    model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)
+    results = model.fit()
+    
+    print(results.summary())
+    ```
+
+    결과 테이블이 출력되었습니다.
+
+첫 모델을 만들었습니다! 지금부터 평가하는 방식을 찾을 필요가 있습니다.
+
+### 모델 평가하기
+
+모델을 평가하려면, `walk forward` 검증이라 불리는 것을 할 수 있습니다. 연습에서, time series 모델은 새로운 데이터를 사용할 수 있는 순간마다 다시-훈련하고 있습니다. 모델은 각 time step마다 최적 예측을 하게 됩니다.
+
+이 기술로 time series의 초반부터 시작해서, 훈련 데이터셋으로 모델을 훈련합니다. 다음 time step에서 예측하게 됩니다. 예측은 알려진 값을 기반으로 평가하게 됩니다. 훈련 셋은 알려진 값을 포함해서 확장하고 프로세스가 반복하게 됩니다.
+
+> 노트: 세트의 초반부터 관측치를 지울 수 있는, 훈련 셋에서 새로운 관측치를 추가할 때마다 효과적인 훈련을 위해 훈련 셋 window를 고정해서 유지해야 합니다.
+
+이 프로세스는 실전에서 모델이 어떻게 할 지에 대해서 강하게 추정하도록 제공합니다. 그러나, 많은 모델을 만들면 계산 비용이 생깁니다. 이는 데이터가 작거나 모델이 간단하지만, 스케일에 이슈가 있을 때 받아들일 수 있습니다.
+
+Walk-forward 검사는 time series 모델 평가의 최적 표준이고 이 프로젝트에 추천됩니다.
+
+1. 먼저, 각자 HORIZON 단계에 테스트 데이터 포인트를 만듭니다.
+
+    ```python
+    test_shifted = test.copy()
+    
+    for t in range(1, HORIZON + 1):
+        test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')
+        
+    test_shifted = test_shifted.dropna(how='any')
+    test_shifted.head(5)
+    ```
+
+    |            |          | load | load+1 | load+2 |
+    | ---------- | -------- | ---- | ------ | ------ |
+    | 2014-12-30 | 00:00:00 | 0.33 | 0.29   | 0.27   |
+    | 2014-12-30 | 01:00:00 | 0.29 | 0.27   | 0.27   |
+    | 2014-12-30 | 02:00:00 | 0.27 | 0.27   | 0.30   |
+    | 2014-12-30 | 03:00:00 | 0.27 | 0.30   | 0.41   |
+    | 2014-12-30 | 04:00:00 | 0.30 | 0.41   | 0.57   |
+
+    데이터는 horizon 포인트에 따라서 수평으로 이동합니다.
+
+1. 테스트 데이터 길이의 크기로 반복해서 sliding window 방식으로 테스트 데이터를 예측합니다:
+
+    ```python
+    %%time
+    training_window = 720 # dedicate 30 days (720 hours) for training
+    
+    train_ts = train['load']
+    test_ts = test_shifted
+    
+    history = [x for x in train_ts]
+    history = history[(-training_window):]
+    
+    predictions = list()
+    
+    order = (2, 1, 0)
+    seasonal_order = (1, 1, 0, 24)
+    
+    for t in range(test_ts.shape[0]):
+        model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)
+        model_fit = model.fit()
+        yhat = model_fit.forecast(steps = HORIZON)
+        predictions.append(yhat)
+        obs = list(test_ts.iloc[t])
+        # move the training window
+        history.append(obs[0])
+        history.pop(0)
+        print(test_ts.index[t])
+        print(t+1, ': predicted =', yhat, 'expected =', obs)
+    ```
+
+    진행하고 있는 훈련을 볼 수 있습니다:
+
+    ```output
+    2014-12-30 00:00:00
+    1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]
+    
+    2014-12-30 01:00:00
+    2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]
+    
+    2014-12-30 02:00:00
+    3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]
+    ```
+
+1. 예측과 실제 부하를 비교합니다:
+
+    ```python
+    eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])
+    eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]
+    eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')
+    eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()
+    eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])
+    eval_df.head()
+    ```
+
+    output
+    |     |            | timestamp | h   | prediction | actual   |
+    | --- | ---------- | --------- | --- | ---------- | -------- |
+    | 0   | 2014-12-30 | 00:00:00  | t+1 | 3,008.74   | 3,023.00 |
+    | 1   | 2014-12-30 | 01:00:00  | t+1 | 2,955.53   | 2,935.00 |
+    | 2   | 2014-12-30 | 02:00:00  | t+1 | 2,900.17   | 2,899.00 |
+    | 3   | 2014-12-30 | 03:00:00  | t+1 | 2,917.69   | 2,886.00 |
+    | 4   | 2014-12-30 | 04:00:00  | t+1 | 2,946.99   | 2,963.00 |
+
+    실제 부하와 비교해서, 시간당 데이터의 예측을 관찰해봅니다. 어느정도 정확한가요?
+
+### 모델 정확도 확인하기
+
+모든 예측에서 mean absolute percentage error (MAPE)으로 테스트해서 모델의 정확도를 확인해봅니다.
+
+> **🧮 Show me the math** 
+>
+> ![MAPE](../images/mape.png)
+> 
+>  [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/)은 다음 공식에서 정의된 비율로 정확도를 예측해서 보여주도록 사용됩니다. actual<sub>t</sub> 과 predicted<sub>t</sub> 사이의 차이점을 actual<sub>t</sub>로 나누게 됩니다. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error)
+
+1. 코드로 방정식을 표현합니다:
+
+    ```python
+    if(HORIZON > 1):
+        eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']
+        print(eval_df.groupby('h')['APE'].mean())
+    ```
+
+1. one step MAPE을 계산합니다:
+
+
+    ```python
+    print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')
+    ```
+
+    One step forecast MAPE:  0.5570581332313952 %
+
+1. multi-step forecast MAPE을 출력합니다:
+
+    ```python
+    print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')
+    ```
+
+    ```output
+    Multi-step forecast MAPE:  1.1460048657704118 %
+    ```
+
+    최적으로 낮은 숫자가 가장 좋습니다: 10 MAPE이 10% 내려져서 예측되었다고 생각해봅니다.
+
+1. 하지만 항상, 이 정확도 측정 종류를 시각적으로 보는 것이 더 쉬우므로, plot 해봅니다:
+
+    ```python
+     if(HORIZON == 1):
+        ## Plotting single step forecast
+        eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))
+    
+    else:
+        ## Plotting multi step forecast
+        plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]
+        for t in range(1, HORIZON+1):
+            plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values
+    
+        fig = plt.figure(figsize=(15, 8))
+        ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)
+        ax = fig.add_subplot(111)
+        for t in range(1, HORIZON+1):
+            x = plot_df['timestamp'][(t-1):]
+            y = plot_df['t+'+str(t)][0:len(x)]
+            ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))
+        
+        ax.legend(loc='best')
+        
+    plt.xlabel('timestamp', fontsize=12)
+    plt.ylabel('load', fontsize=12)
+    plt.show()
+    ```
+
+    ![a time series model](../images/accuracy.png)
+
+🏆 괜찮은 정확도로 모델을 보여주는, 매우 좋은 plot 입니다. 잘 마쳤습니다!
+
+---
+
+## 🚀 도전
+
+Time Series 모델의 정확도를 테스트할 방식을 파봅니다. 이 강의에서 MAPE을 다루지만, 사용할 다른 방식이 있나요? 조사해보고 첨언해봅니다. 도움을 받을 수 있는 문서는 [here](https://otexts.com/fpp2/accuracy.html)에서 찾을 수 있습니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/)
+
+## 검토 & 자기주도 학습
+
+이 강의에서 ARIMA로 Time Series Forecasting의 기초만 다룹니다. 시간을 내서 [this repository](https://microsoft.github.io/forecasting/)를 파보고 Time Series 모델 만드는 다양한 방식을 배우기 위한 모델 타입도 깊게 알아봅니다.
+
+## 과제     
+
+[A new ARIMA model](../assignment.md)
diff --git a/7-TimeSeries/2-ARIMA/translations/assignment.it.md b/7-TimeSeries/2-ARIMA/translations/assignment.it.md
new file mode 100644
index 000000000..120865b9b
--- /dev/null
+++ b/7-TimeSeries/2-ARIMA/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Un nuovo modello ARIMA
+
+## Istruzioni
+
+Ora che si è creato un modello ARIMA, crearne uno nuovo con dati aggiornati (provare uno di [questi set di dati da Duke](http://www2.stat.duke.edu/~mw/ts_data_sets.html)). Annotare il lavoro in un notebook, visualizzare i dati e il modello e verificarne l'accuratezza utilizzando MAPE.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | ----------------------------------- |
+|          | Viene presentato un notebook con un nuovo modello ARIMA costruito, testato e spiegato con visualizzazioni e accuratezza dichiarate. | Il notebook presentato non è annotato o contiene bug | Viene presentato un notebook incompleto |
diff --git a/7-TimeSeries/translations/README.it.md b/7-TimeSeries/translations/README.it.md
new file mode 100644
index 000000000..d1f0deaa9
--- /dev/null
+++ b/7-TimeSeries/translations/README.it.md
@@ -0,0 +1,22 @@
+# Introduzione alla previsione delle serie temporali
+
+Che cos'è la previsione delle serie temporali? Si tratta di prevedere eventi futuri analizzando le tendenze del passato.
+
+## Argomento regionale: consumo di elettricità in tutto il mondo ✨
+
+In queste due lezioni, si verrà introdotti alla previsione delle serie temporali, un'area un po' meno conosciuta di machine learning che è tuttavia estremamente preziosa per l'industria e le applicazioni aziendali, tra gli altri campi. Sebbene le reti neurali possano essere utilizzate per migliorare l'utilità di questi modelli, verranno studiate nel contesto di machine learning classico come modelli che aiutano a prevedere le prestazioni future basandosi sul passato.
+
+L'obiettivo regionale è l'utilizzo elettrico nel mondo, un interessante insieme di dati per conoscere la previsione del consumo energetico futuro in base ai modelli di carico passato. Si può vedere come questo tipo di previsione può essere estremamente utile in un ambiente aziendale.
+
+![rete elettrica](../images/electric-grid.jpg)
+
+Foto di <a href="https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Peddi Sai hrithik</a> di torri elettriche su una strada in Rajasthan su <a href="https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Lezioni
+
+1. [Introduzione alla previsione delle serie temporali](../1-Introduction/translations/README.it.md)
+2. [Costruire modelli di serie temporali ARIMA](../2-ARIMA/translations/README.it.md)
+
+## Crediti
+
+"Introduzione alla previsione delle serie temporali" è stato scritto con ⚡️ da [Francesca Lazzeri](https://twitter.com/frlazzeri) e [Jen Looper](https://twitter.com/jenlooper)
diff --git a/7-TimeSeries/translations/README.ko.md b/7-TimeSeries/translations/README.ko.md
new file mode 100644
index 000000000..57c1d4262
--- /dev/null
+++ b/7-TimeSeries/translations/README.ko.md
@@ -0,0 +1,22 @@
+# Time series forecasting 소개하기
+
+time series forecasting은 무엇인가요? 과거의 트렌드로 분석해서 미래 이벤트를 예측합니다.
+
+## 지역 토픽: 전세계 전기 사용량 ✨
+
+2개의 강의에서, 타 필드의 산업과 비지니스 애플리케이션에서도 매우 쓸모있는 머신러닝의 비교적 덜 알려진 영역인, time series forecasting을 소개할 예정입니다. neural network로 모델의 유틸리티를 개선할 수 있지만, 모델은 과거 기반으로 미래 성능을 예측할수 있게 도와줄 수 있으므로 classical 머신러닝의 컨텍스트로 공부해볼 예정입니다.
+
+여기에서 핵심은 과거 부하 패턴 기반으로 향후 전력 사용량의 예측에 대해 배울 수 있는 흥미로운 데이터셋인, 전세계의 전기 사용량입니다. 이 예측 종류가 비지니스 환경에서 많이 돕고 있는지 볼 수 있습니다.
+
+![electric grid](../images/electric-grid.jpg)
+
+Photo by <a href="https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Peddi Sai hrithik</a> of electrical towers on a road in Rajasthan on <a href="https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## 강의
+
+1. [Time series forecasting 소개하기](../1-Introduction/translations/README.ko.md)
+2. [ARIMA time series 모델 만들기](../2-ARIMA/translations/README.ko.md)
+
+## 크레딧
+
+"Introduction to time series forecasting" was written with ⚡️ by [Francesca Lazzeri](https://twitter.com/frlazzeri) and [Jen Looper](https://twitter.com/jenlooper)
diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index bfa07ffe3..fbba83523 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -11,7 +11,7 @@ By using reinforcement learning and a simulator (the game), you can learn how to
 
 > 🎥 Click the image above to hear Dmitry discuss Reinforcement Learning
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/45/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/)
 
 ## Prerequisites and Setup
 
@@ -229,8 +229,7 @@ We are now ready to implement the learning algorithm. Before we do that, we also
     We add a few `eps` to the original vector in order to avoid division by 0 in the initial case, when all components of the vector are identical.
 
 Run them learning algorithm through 5000 experiments, also called **epochs**: (code block 8)
-
-    ```python
+```python
     for epoch in range(5000):
     
         # Pick initial point
@@ -255,11 +254,11 @@ Run them learning algorithm through 5000 experiments, also called **epochs**: (c
             ai = action_idx[a]
             Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())
             n+=1
-    ```
+```
 
-    After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize the Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw a small circle instead of an arrow head.
+After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize the Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw a small circle instead of an arrow head.
 
-    <img src="images/learned.png"/>
+<img src="images/learned.png"/>
 
 ## Checking the policy
 
@@ -315,6 +314,6 @@ The learnings can be summarized as:
 
 Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as learning rate, learning rate decay, and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters**, which we optimize during training (for example, Q-Table coefficients). The process of finding the best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/46/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
 
 ## Assignment [A More Realistic World](assignment.md)
diff --git a/8-Reinforcement/1-QLearning/solution/Julia/README.md b/8-Reinforcement/1-QLearning/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/8-Reinforcement/1-QLearning/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/8-Reinforcement/1-QLearning/solution/R/README.md b/8-Reinforcement/1-QLearning/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/8-Reinforcement/1-QLearning/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/8-Reinforcement/1-QLearning/translations/README.it.md b/8-Reinforcement/1-QLearning/translations/README.it.md
new file mode 100644
index 000000000..9d91cdece
--- /dev/null
+++ b/8-Reinforcement/1-QLearning/translations/README.it.md
@@ -0,0 +1,320 @@
+# Introduzione a Reinforcement Learning e  Q-Learning
+
+![Riepilogo di reinforcement in machine learning in uno sketchnote](../../../sketchnotes/ml-reinforcement.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+Il reinforcement learning (apprendimento per rinforzo) coinvolge tre concetti importanti: l'agente, alcuni stati e un insieme di azioni per stato. Eseguendo un'azione in uno stato specifico, l'agente riceve una ricompensa. Si immagini di nuovo il gioco per computer Super Mario. Si è Mario, ci si trova in un livello di gioco, in piedi accanto a un dirupo. Sopra a Mario c'è una moneta. L'essere Mario, in un livello di gioco, in una posizione specifica... questo è il proprio stato. Spostarsi di un passo a destra (un'azione) porterebbe Mario oltre il limite e questo darebbe un punteggio numerico basso. Tuttavia, premendo il pulsante di salto si farà segnare un punto e si rimarrà vivi. Questo è un risultato positivo e dovrebbe assegnare un punteggio numerico positivo.
+
+Usando reinforcement learning e un simulatore (il gioco), si può imparare a giocare per massimizzare la ricompensa che consiste nel rimanere in vita e segnare più punti possibile.
+
+[![Introduzione al Reinforcement Learning](https://img.youtube.com/vi/lDq_en8RNOo/0.jpg)](https://www.youtube.com/watch?v=lDq_en8RNOo)
+
+> 🎥 Fare clic sull'immagine sopra per ascoltare Dmitry discutere sul reinforcement learning
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/?loc=it)
+
+## Prerequisiti e Configurazione
+
+In questa lezione si sperimenterà del codice in Python. Si dovrebbe essere in grado di eseguire il codice di Jupyter Notebook da questa lezione, sul proprio computer o da qualche parte nel cloud.
+
+Si può aprire [il notebook della lezione](notebook.ipynb) e seguire questa lezione per sviluppare.
+
+> **Nota:** Se si sta aprendo questo codice dal cloud, occorre anche recuperare il file [`rlboard.py`](../rlboard.py) , che viene utilizzato nel codice del notebook. Aggiungerlo alla stessa directory del notebook.
+
+## Introduzione
+
+In questa lezione si esplorerà il mondo di **[Pierino e il lupo](https://it.wikipedia.org/wiki/Pierino_e_il_lupo)**, ispirato a una fiaba musicale di un compositore russo, [Sergei Prokofiev](https://it.wikipedia.org/wiki/Sergei_Prokofiev). Si userà **Reinforcement Learning** per permettere a Pierino di esplorare il suo ambiente, raccogliere gustose mele ed evitare di incontrare il lupo.
+
+**Reinforcement Learning** (RL) è una tecnica di apprendimento che  permette di apprendere un comportamento ottimale di un **agente** in un certo **ambiente** eseguendo molti esperimenti. Un agente in questo ambiente dovrebbe avere un **obiettivo**, definito da una **funzione di ricompensa**.
+
+## L’ambiente
+
+Per semplicità, si considera il mondo di Pierino come una tavola di gioco quadrata di dimensioni `width` X `height`, (larghezza X altezza), in questo modo:
+
+![L'ambiente di Pierino](../images/environment.png)
+
+Ogni cella in questa tavola può essere:
+
+* **terra**, sulla quale possono camminare Pierino e le altre creature.
+* **acqua**, sulla quale ovviamente non è possibile camminare.
+* un **albero** o un **prato**, un luogo dove riposarsi.
+* una **mela**, che rappresenta qualcosa che Pierino sarebbe felice di trovare per nutrirsi.
+* un **lupo**, che è pericoloso e dovrebbe essere evitato.
+
+C'è un modulo Python separato, [`rlboard.py`](../rlboard.py), che contiene il codice per lavorare con questo ambiente. Poiché questo codice non è importante per comprendere i  concetti esposti, si importerà il modulo e lo si utilizzerà per creare la tavola di gioco di esempio (blocco di codice 1):
+
+```python
+from rlboard import *
+
+width, height = 8,8
+m = Board(width,height)
+m.randomize(seed=13)
+m.plot()
+```
+
+Questo codice dovrebbe stampare un'immagine dell'ambiente simile a quella sopra.
+
+## Azioni e policy
+
+In questo esempio, l'obiettivo di Pierino sarebbe quello di trovare una mela, evitando il lupo e altri ostacoli. Per fare ciò, può essenzialmente camminare finché non trova una mela.
+
+Pertanto, in qualsiasi posizione, può scegliere tra una delle seguenti azioni: su, giù, sinistra e destra.
+
+Si definiranno queste azioni come un dizionario e si mapperanno su coppie di corrispondenti cambiamenti di coordinate. Ad esempio, lo spostamento a destra (`R`) corrisponderebbe a una coppia `(1,0)`. (blocco di codice 2):
+
+```python
+actions = { "U" : (0,-1), "D" : (0,1), "L" : (-1,0), "R" : (1,0) }
+action_idx = { a : i for i,a in enumerate(actions.keys()) }
+```
+
+Per riassumere, la strategia e l'obiettivo di questo scenario sono i seguenti:
+
+- **La strategia** del nostro agente (Pierino) è definita da una cosiddetta **policy**. Una policy è una funzione che restituisce l'azione ad ogni dato stato. In questo caso, lo stato del problema è rappresentato dalla tavola di gioco, inclusa la posizione attuale del giocatore.
+
+- **L'obiettivo** del reinforcement learning è alla fine imparare una buona policy che consentirà di risolvere il problema in modo efficiente. Tuttavia, come linea di base, si considera la policy più semplice chiamata **random walk**.
+
+## Random walk (passeggiata aleatoria)
+
+Prima si risolve il problema implementando una strategia di random walk. Tramite random walk, si sceglierà casualmente l'azione successiva tra quelle consentite, fino a raggiungere la mela (blocco di codice 3).
+
+1. Implementare random walk con il codice seguente:
+
+   ```python
+   def random_policy(m):
+       return random.choice(list(actions))
+
+   def walk(m,policy,start_position=None):
+       n = 0 # numero di passi
+       # imposta posizione iniziale
+       if start_position:
+           m.human = start_position
+       else:
+           m.random_start()
+       while True:
+           if m.at() == Board.Cell.apple:
+               return n # successo!
+           if m.at() in [Board.Cell.wolf, Board.Cell.water]:
+               return -1 # mangiato dal lupo o annegato
+           while True:
+               a = actions[policy(m)]
+               new_pos = m.move_pos(m.human,a)
+               if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:
+                   m.move(a) # esegue la mossa effettiva
+                   break
+           n+=1
+
+   walk(m,random_policy)
+   ```
+
+   La chiamata a `walk` dovrebbe restituire la lunghezza del percorso corrispondente, che può variare da una esecuzione all'altra.
+
+1. Eseguire l'esperimento di walk un certo numero di volte (100 ad esempio) e stampare le statistiche risultanti (blocco di codice 4):
+
+   ```python
+   def print_statistics(policy):
+       s,w,n = 0,0,0
+       for _ in range(100):
+           z = walk(m,policy)
+           if z<0:
+               w+=1
+           else:
+               s += z
+               n += 1
+       print(f"Average path length = {s/n}, eaten by wolf: {w} times")
+
+   print_statistics(random_policy)
+   ```
+
+   Notare che la lunghezza media di un percorso è di circa 30-40 passi, che è parecchio, dato che la distanza media dalla mela più vicina è di circa 5-6 passi.
+
+   Si può anche vedere come appare il movimento di Pierino durante la passeggiata aleatoria:
+
+   ![La passeggiata aleatoria di Pierino](../images/random_walk.gif)
+
+## Funzione di ricompensa
+
+Per rendere la policy più intelligente, occorre capire quali mosse sono "migliori" di altre. Per fare questo, si deve definire l'obiettivo.
+
+L'obiettivo può essere definito in termini di una **funzione di ricompensa**, che restituirà un valore di punteggio per ogni stato. Più alto è il numero, migliore è la funzione di ricompensa. (blocco di codice 5)
+
+```python
+move_reward = -0.1
+goal_reward = 10
+end_reward = -10
+
+def reward(m,pos=None):
+    pos = pos or m.human
+    if not m.is_valid(pos):
+        return end_reward
+    x = m.at(pos)
+    if x==Board.Cell.water or x == Board.Cell.wolf:
+        return end_reward
+    if x==Board.Cell.apple:
+        return goal_reward
+    return move_reward
+```
+
+Una cosa interessante delle funzioni di ricompensa è che nella maggior parte dei casi viene *data una ricompensa sostanziale solo alla fine del gioco*. Ciò significa che l'algoritmo dovrebbe in qualche modo ricordare i passaggi "buoni" che portano a una ricompensa positiva alla fine e aumentare la loro importanza. Allo stesso modo, tutte le mosse che portano a cattivi risultati dovrebbero essere scoraggiate.
+
+## Q-Learning
+
+Un algoritmo che verrà trattato qui si chiama **Q-Learning**. In questo algoritmo, la policy è definita da una funzione (o una struttura dati) chiamata **Q-Table**. Registra la "bontà" di ciascuna delle azioni in un dato stato.
+
+Viene chiamata Q-Table perché spesso è conveniente rappresentarla come una tabella o un array multidimensionale. Poiché la tavola di gioco ha dimensioni `width` x `height`, si può rappresentare la tabella Q usando un array numpy con forma `width` x `height` x `len(actions)`: (blocco di codice 6)
+
+```python
+Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)
+```
+
+Notare che si inizializzano tutti i valori della Q-Table con un valore uguale, in questo caso - 0.25. Ciò corrisponde alla policy di "random walk", perché tutte le mosse in ogni stato sono ugualmente buone. Si può passare la Q-Table alla funzione `plot` per visualizzare la tabella sulla tavola di gioco: `m.plot (Q)`.
+
+![L'ambiente di Pierino](../images/env_init.png)
+
+Al centro di ogni cella è presente una "freccia" che indica la direzione di spostamento preferita. Poiché tutte le direzioni sono uguali, viene visualizzato un punto.
+
+Ora si deve eseguire la simulazione, esplorare l'ambiente e apprendere una migliore distribuzione dei valori della Q-Table, che  consentirà di trovare il percorso verso la mela molto più velocemente.
+
+## Essenza di Q-Learning: Equazione di Bellman
+
+Una volta che si inizia il movimento, ogni azione avrà una ricompensa corrispondente, vale a dire che si può teoricamente selezionare l'azione successiva in base alla ricompensa immediata più alta. Tuttavia, nella maggior parte degli stati, la mossa non raggiungerà l'obiettivo di raggiungere la mela, e quindi non è possibile decidere immediatamente quale direzione sia migliore.
+
+> Si ricordi che non è il risultato immediato che conta, ma piuttosto il risultato finale, che sarà ottenuto alla fine della simulazione.
+
+Per tenere conto di questa ricompensa ritardata, occorre utilizzare i principi della **[programmazione dinamica](https://it.wikipedia.org/wiki/Programmazione_dinamica)**, che consentono di pensare al problema in modo ricorsivo.
+
+Si supponga di essere ora nello stato *s*, e di voler passare allo stato *s* successivo. In tal modo, si riceverà la ricompensa immediata *r(s,a)*, definita dalla funzione di ricompensa, più qualche ricompensa futura. Se si suppone che la Q-Table rifletta correttamente l'"attrattiva" di ogni azione, allora allo stato *s'* si sceglierà *un'azione a* che corrisponde al valore massimo di *Q(s',a')*. Pertanto, la migliore ricompensa futura possibile che si potrebbe ottenere allo stato *s* sarà definita come `max`<sub>a'</sub>*Q(s',a')* (il massimo qui è calcolato su tutte le possibili azioni *a'* allo stato *s'*).
+
+Questo dà la **formula** di Bellman per calcolare il valore della Q-Table allo stato *s*, data l'azione *a*:
+
+<img src="../images/bellmaneq.gif"/>
+
+Qui y è il cosiddetto **fattore di sconto** che determina fino a che punto si dovrebbe preferire il premio attuale a quello futuro e viceversa.
+
+## Algoritmo di Apprendimento
+
+Data l'equazione di cui sopra, ora si può scrivere pseudo-codice per l'algoritmo di apprendimento:
+
+* Inizializzare Q-Table Q con numeri uguali per tutti gli stati e le azioni
+* Impostare la velocità di apprendimento α ← 1
+* Ripetere la simulazione molte volte
+   1. Iniziare in una posizione casuale
+   1. Ripetere
+      1. Selezionare un'azione *a* nello stato *s*
+      2. Eseguire l'azione trasferendosi in un nuovo stato *s'*
+      3. Se si incontra una condizione di fine gioco o la ricompensa totale è troppo piccola, uscire dalla simulazione
+      4. Calcolare la ricompensa *r* nel nuovo stato
+      5. Aggiornare Q-Function secondo l'equazione di Bellman: *Q(s,a)* ← *(1-α)Q(s,a)+α(r+γ max<sub>a'</sub>Q(s',a'))*
+      6. *s* ← *s'*
+      7. Aggiornare la ricompensa totale e diminuire α.
+
+## Sfruttamento contro esplorazione
+
+Nell'algoritmo sopra, non è stato specificato come esattamente si dovrebbe scegliere un'azione al passaggio 2.1. Se si sceglie l'azione in modo casuale, si **esplorerà** casualmente l'ambiente e molto probabilmente si morirà spesso e si esploreranno aree in cui normalmente non si andrebbe. Un approccio alternativo sarebbe **sfruttare** i valori della Q-Table già noti, e quindi scegliere l'azione migliore (con un valore Q-Table più alto) allo stato *s*. Questo, tuttavia, impedirà di esplorare altri stati ed è probabile che non si potrebbe trovare la soluzione ottimale.
+
+Pertanto, l'approccio migliore è trovare un equilibrio tra esplorazione e sfruttamento. Questo può essere fatto scegliendo l'azione allo stato *s* con probabilità proporzionali ai valori nella Q-Table. All'inizio, quando i valori della Q-Table sono tutti uguali, corrisponderebbe a una selezione casuale, ma man mano che si impara di più sull'ambiente, si sarà più propensi a seguire il percorso ottimale consentendo all'agente di scegliere il percorso inesplorato una volta ogni tanto.
+
+## Implementazione Python
+
+Ora si è pronti per implementare l'algoritmo di apprendimento. Prima di farlo, serve anche una funzione che converta i numeri arbitrari nella Q-Table in un vettore di probabilità per le azioni corrispondenti.
+
+1. Creare una funzione `probs()`:
+
+   ```python
+   def probs(v,eps=1e-4):
+       v = v-v.min()+eps
+       v = v/v.sum()
+       return v
+   ```
+
+   Aggiungere alcuni `eps` al vettore originale per evitare la divisione per 0 nel caso iniziale, quando tutte le componenti del vettore sono identiche.
+
+Esegure l'algoritmo di apprendimento attraverso 5000 esperimenti, chiamati anche **epoche**: (blocco di codice 8)
+
+    ```python
+    for epoch in range(5000):
+
+        # Sceglie il punto iniziale
+        m.random_start()
+
+        # Inizia a viaggiare
+        n=0
+        cum_reward = 0
+        while True:
+            x,y = m.human
+            v = probs(Q[x,y])
+            a = random.choices(list(actions),weights=v)[0]
+            dpos = actions[a]
+            m.move(dpos,check_correctness=False) # si consente al giocatore di spostarsi oltre la tavola di gioco, il che fa terminare l'episodioepisode
+            r = reward(m)
+            cum_reward += r
+            if r==end_reward or cum_reward < -1000:
+                lpath.append(n)
+                break
+            alpha = np.exp(-n / 10e5)
+            gamma = 0.5
+            ai = action_idx[a]
+            Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())
+            n+=1
+    ```
+
+Dopo aver eseguito questo algoritmo, la Q-Table dovrebbe essere aggiornata con valori che definiscono l'attrattiva delle diverse azioni in ogni fase. Si può provare a visualizzare la Q-Table tracciando un vettore in ogni cella che punti nella direzione di movimento desiderata. Per semplicità, si disegna un piccolo cerchio invece di una punta di freccia.
+
+<img src="../images/learned.png"/>
+
+## Controllo della policy
+
+Poiché la Q-Table elenca l'"attrattiva" di ogni azione in ogni stato, è abbastanza facile usarla per definire la navigazione efficiente in questo mondo. Nel caso più semplice, si può selezionare l'azione corrispondente al valore Q-Table più alto: (blocco di codice 9)
+
+```python
+def qpolicy_strict(m):
+        x,y = m.human
+        v = probs(Q[x,y])
+        a = list(actions)[np.argmax(v)]
+        return a
+
+walk(m,qpolicy_strict)
+```
+
+> Se si prova più volte il codice sopra, si potrebbe notare che a volte "si blocca" e occorre premere il pulsante STOP nel notebook per interromperlo. Ciò accade perché potrebbero esserci situazioni in cui due stati "puntano" l'uno all'altro in termini di Q-Value ottimale, nel qual caso gli agenti finiscono per spostarsi tra quegli stati indefinitamente.
+
+## 🚀 Sfida
+
+> **Attività 1:** modificare la funzione `walk` per limitare la lunghezza massima del percorso di un certo numero di passaggi (ad esempio 100) e osservare il codice sopra restituire questo valore di volta in volta.
+
+> **Attività 2:** Modificare la funzione di `walk` in modo che non torni nei luoghi in cui è già stata in precedenza. Ciò impedirà la ricorsione di `walk`, tuttavia, l'agente può comunque finire per essere "intrappolato" in una posizione da cui non è in grado di fuggire.
+
+## Navigazione
+
+Una policy di navigazione migliore sarebbe quella usata durante l'addestramento, che combina sfruttamento ed esplorazione. In questa policy, si selezionerà ogni azione con una certa probabilità, proporzionale ai valori nella Q-Table. Questa strategia può comunque portare l'agente a tornare in una posizione già esplorata, ma, come si può vedere dal codice sottostante, risulta in un percorso medio molto breve verso la posizione desiderata (ricordare che `print_statistics` esegue la simulazione 100 volte ): (blocco di codice 10)
+
+```python
+def qpolicy(m):
+        x,y = m.human
+        v = probs(Q[x,y])
+        a = random.choices(list(actions),weights=v)[0]
+        return a
+
+print_statistics(qpolicy)
+```
+
+Dopo aver eseguito questo codice, si dovrebbe ottenere una lunghezza media del percorso molto più piccola rispetto a prima, nell'intervallo 3-6.
+
+## Indagare il processo di apprendimento
+
+Come accennato, il processo di apprendimento è un equilibrio tra esplorazione e sfruttamento delle conoscenze acquisite sulla struttura del problema spazio. Si è visto che i risultati dell'apprendimento (la capacità di aiutare un agente a trovare un percorso breve verso l'obiettivo) sono migliorati, ma è anche interessante osservare come si comporta la lunghezza media del percorso durante il processo di apprendimento:
+
+<img src="../images/lpathlen1.png"/>
+
+Gli apprendimenti possono essere riassunti come:
+
+- **La lunghezza media del percorso aumenta**. Quello che si vede qui è che all'inizio la lunghezza media del percorso aumenta. Ciò è probabilmente dovuto al fatto che quando non si sa nulla dell'ambiente, è probabile rimanere intrappolati in cattive condizioni, acqua o lupo. Man mano che si impara di più e si inizia a utilizzare questa conoscenza, è possibile esplorare l'ambiente più a lungo, ma non si conosce ancora molto bene dove si trovano le mele.
+
+- **La lunghezza del percorso diminuisce, man mano che si impara di più**. Una volta imparato abbastanza, diventa più facile per l'agente raggiungere l'obiettivo e la lunghezza del percorso inizia a diminuire. Tuttavia, si è ancora aperti all'esplorazione, quindi spesso ci si allontana dal percorso migliore e si esplorano nuove opzioni, rendendo il percorso più lungo che ottimale.
+
+- **La lunghezza aumenta bruscamente**. Quello che si osserva anche su questo grafico è che ad un certo punto la lunghezza è aumentata bruscamente. Questo indica la natura stocastica del processo e che a un certo punto si possono "rovinare" i coefficienti della Q-Table sovrascrivendoli con nuovi valori. Idealmente, questo dovrebbe essere ridotto al minimo diminuendo il tasso di apprendimento (ad esempio, verso la fine dell'allenamento, si regolano i valori di Q-Table solo di un piccolo valore).
+
+Nel complesso, è importante ricordare che il successo e la qualità del processo di apprendimento dipendono in modo significativo da parametri come il tasso di apprendimento, il decadimento del tasso di apprendimento e il fattore di sconto. Questi sono spesso chiamati **iperparametri**, per distinguerli dai **parametri**, che si ottimizzano durante l'allenamento (ad esempio, i coefficienti della Q-Table). Il processo per trovare i valori migliori degli iperparametri è chiamato **ottimizzazione degli iperparametri** e merita un argomento a parte.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/?loc=fr)
+
+## Incarico: [Un mondo più realistico](assignment.it.md)
diff --git a/8-Reinforcement/1-QLearning/translations/README.ko.md b/8-Reinforcement/1-QLearning/translations/README.ko.md
new file mode 100644
index 000000000..b91313b67
--- /dev/null
+++ b/8-Reinforcement/1-QLearning/translations/README.ko.md
@@ -0,0 +1,322 @@
+# Reinforcement Learning과 Q-Learning 소개하기
+
+![Summary of reinforcement in machine learning in a sketchnote](../../../sketchnotes/ml-reinforcement.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+Reinforcement learning에는 3가지 중요한 컨셉이 섞여있습니다: agent, 일부 states, 그리고 state 당 actions 세트. 특정 상황에서 행동하면 agent에게 보상이 주어집니다. Super Mario 게임을 다시 상상해봅니다. 마리오가 되어서, 게임 레벨에 진입하고, 구덩이 옆에 있습니다. 그 위에 동전이 있습니다. 게임 레벨에서, 특정 위치에 있는, 마리오가 됩니다 ... 이게 state 입니다. 한 단계 오른쪽으로 이동하면 (action) 구덩이를 빠져서, 낮은 점수를 받습니다. 그러나, 점프 버튼을 누르면 점수를 얻고 살아남을 수 있습니다. 긍정적인 결과이며, 긍정적인 점수를 줘야 합니다.
+
+reinforcement learning과 (게임) 시뮬레이터로, 살아남고 가능한 많은 점수로 최대 보상을 받는 게임의 플레이 방식을 배울 수 있습니다.
+
+[![Intro to Reinforcement Learning](https://img.youtube.com/vi/lDq_en8RNOo/0.jpg)](https://www.youtube.com/watch?v=lDq_en8RNOo)
+
+> 🎥 Dmitry discuss Reinforcement Learning 들으려면 이미지 클릭
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/)
+
+## 전제조건 및 설정
+
+이 강의에서, Python으로 약간의 코드를 실험할 예정입니다. 이 강의에서 Jupyter Notebook을, 컴퓨터나 클라우드 어디에서나 실행해야 합니다.
+
+[the lesson notebook](../notebook.ipynb)을 열고 이 강의로 만들 수 있습니다.
+
+> **노트:** 만약 클라우드에서 코드를 연다면, 노트북 코드에서 사용하는 [`rlboard.py`](../rlboard.py) 파일도 가져올 필요가 있습니다. 노트북과 같은 디렉토리에 추가합니다.
+
+## 소개
+
+이 강의에서, 러시아 작곡가 [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev)의 동화 곡에 영감을 받은, **[Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf)** 의 월드를 탐험해볼 예정입니다. **Reinforcement Learning**으로 Peter가 환경을 탐험하고, 맛있는 사과를 모으고 늑대는 피할 것입니다.
+
+**Reinforcement Learning** (RL)은 많은 실험을 돌려서 일부 **environment**에서 **agent**의 최적화된 동작을 배울 수 있는 훈련 기술입니다. 이 환경의 agent는 **reward function**으로 정의된, 약간의 **goal**을 가지고 있어야 합니다.
+
+## 환경
+
+단순하고자, Peter의 월드를 이렇게 `width` x `height` 크기의 정사각형 보드로 고려해보려 합니다:
+
+![Peter's Environment](../images/environment.png)
+
+이 보드에서 각 셀은 이 중 하나 입니다:
+
+* **ground**, Peter와 다른 생명체가 걸을 수 있습니다.
+* **water**, 무조건 걸을 수 없습니다.
+* a **tree** or **grass**, 휴식할 수 있는 장소입니다.
+* an **apple**, Peter가 스스로 먹으려고 찾을 수 있는 물건입니다.
+* a **wolf**, 위험하고 피해야 합니다.
+
+이 환경에서 작성하는 코드가 포함되어 있는, [`rlboard.py`](../rlboard.py) 별도 Python 모듈이 있습니다. 그러므로 이 코드는 컨셉을 이해하는 게 중요하지 않으므로, 모듈을 가져오고 샘플 보드를 (code block 1) 만들고자 사용할 예정입니다:
+
+```python
+from rlboard import *
+
+width, height = 8,8
+m = Board(width,height)
+m.randomize(seed=13)
+m.plot()
+```
+
+이 코드는 위와 비슷하게 그림을 출력하게 됩니다.
+
+## 액션과 정책
+
+예시에서, Peter의 목표는 늑대와 장애물을 피하고, 사과를 찾는 것입니다. 그러기 위해서, 사과를 찾기 전까지 걷게 됩니다.
+
+그러므로, 어느 위치에서, 다음 액션 중 하나를 선택할 수 있습니다: up, down, left 그리고 right.
+
+이 액션을 dictionary로 정의하고, 좌표 변경의 쌍을 맵핑합니다. 오른쪽 이동(`R`)은 `(1,0)` 쌍에 대응합니다. (code block 2):
+
+```python
+actions = { "U" : (0,-1), "D" : (0,1), "L" : (-1,0), "R" : (1,0) }
+action_idx = { a : i for i,a in enumerate(actions.keys()) }
+```
+
+요약해보면, 이 시나리오의 전략과 목표는 다음과 같습니다:
+
+- **agent (Peter)의 전략**은, **policy**로 불리며 정의됩니다. 정책은 주어진 state에서 action을 반환하는 함수입니다. 이 케이스에서, 문제의 state는 플레이어의 현재 위치를 포함해서, 보드로 표현합니다.
+
+- **reinforcement learning의 목표**는, 결국 문제를 효율적으로 풀수 있게 좋은 정책을 학습하는 것입니다. 그러나, 기본적으로, **random walk**라고 불리는 단순한 정책을 고려해보겠습니다.
+
+## 랜덤 워킹
+
+랜덤 워킹 전략으로 첫 문제를 풀어봅시다. 랜덤 워킹으로, 사과에 닿을 때까지, 허용된 액션에서 다음 액션을 무작위로 선택합니다 (code block 3).
+
+1. 아래 코드로 랜덤 워킹을 구현합니다:
+
+    ```python
+    def random_policy(m):
+        return random.choice(list(actions))
+    
+    def walk(m,policy,start_position=None):
+        n = 0 # number of steps
+        # set initial position
+        if start_position:
+            m.human = start_position 
+        else:
+            m.random_start()
+        while True:
+            if m.at() == Board.Cell.apple:
+                return n # success!
+            if m.at() in [Board.Cell.wolf, Board.Cell.water]:
+                return -1 # eaten by wolf or drowned
+            while True:
+                a = actions[policy(m)]
+                new_pos = m.move_pos(m.human,a)
+                if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:
+                    m.move(a) # do the actual move
+                    break
+            n+=1
+    
+    walk(m,random_policy)
+    ```
+
+    `walk`를 부르면 실행할 때마다 다르게, 일치하는 경로의 길이를 반환합니다.
+
+1. 워킹 실험을 여러 번 (say, 100) 실행하고, 통계 결과를 출력합니다 (code block 4):
+
+    ```python
+    def print_statistics(policy):
+        s,w,n = 0,0,0
+        for _ in range(100):
+            z = walk(m,policy)
+            if z<0:
+                w+=1
+            else:
+                s += z
+                n += 1
+        print(f"Average path length = {s/n}, eaten by wolf: {w} times")
+    
+    print_statistics(random_policy)
+    ```
+
+    가까운 사과으로 가는 평균 걸음이 대략 5-6 걸음이라는 사실로 주어졌을 때, 경로의 평균 길이가 대략 30-40 걸음이므로, 꽤 오래 걸립니다.
+
+    또 랜덤 워킹을 하는 동안 Peter의 행동이 어떻게 되는지 확인할 수 있습니다:
+
+    ![Peter's Random Walk](../images/random_walk.gif)
+
+## 보상 함수
+
+정책을 더 지능적으로 만드려면, 행동을 다른 것보다 "더" 좋은지 이해할 필요가 있습니다. 이렇게, 목표를 정의할 필요가 있습니다. 
+
+목표는 각 state에서 일부 점수 값을 반환할 예정인, **reward function**로 정의할 수 있습니다. 높은 점수일수록, 더 좋은 보상 함수입니다. (code block 5)
+
+```python
+move_reward = -0.1
+goal_reward = 10
+end_reward = -10
+
+def reward(m,pos=None):
+    pos = pos or m.human
+    if not m.is_valid(pos):
+        return end_reward
+    x = m.at(pos)
+    if x==Board.Cell.water or x == Board.Cell.wolf:
+        return end_reward
+    if x==Board.Cell.apple:
+        return goal_reward
+    return move_reward
+```
+
+보상 함수에 대해서 흥미로운 것은 많은 경우에, *게임이 끝날 때만 중요한 보상을 줍니다*. 알고리즘이 끝날 때 긍정적인 보상으로 이어지는 "good" 단계를 어떻게든지 기억하고, 중요도를 올려야 된다는 점을 의미합니다. 비슷하게, 나쁜 결과를 이끄는 모든 동작을 하지 말아야 합니다.
+
+## Q-Learning
+
+여기에서 논의할 알고리즘은 **Q-Learning**이라고 불립니다. 이 알고리즘에서, 정책은 **Q-Table** 불리는 함수 (데이터 구조)로 정의합니다. 주어진 state에서 각 액션의 "goodness"를 기록합니다.
+
+테이블, 또는 multi-dimensional 배열로 자주 표현하는 게 편리하므로 Q-Table이라고 부릅니다. 보드는 `width` x `height` 크기라서, `width` x `height` x `len(actions)` 형태의 numpy 배열로 Q-Table을 표현할 수 있습니다 : (code block 6)
+
+```python
+Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)
+```
+
+Q-Table의 모든 값이 같다면, 이 케이스에서 - 0.25 으로 초기화합니다. 각자 state는 모두가 충분히 동일하게 움직이므로, "random walk" 정책에 대응됩니다. 보드에서 테이블을 시각화하기 위해서 Q-Table을 `plot` 함수에 전달할 수 있습니다: `m.plot(Q)`.
+
+![Peter's Environment](../images/env_init.png)
+
+각자 셀의 중심에 이동하는 방향이 표시되는 "arrow"가 있습니다. 모든 방향은 같으므로, 점이 출력됩니다.
+
+사과로 가는 길을 더 빨리 찾을 수 있으므로, 지금부터 시물레이션을 돌리고, 환경을 찾고, 그리고 더 좋은 Q-Table 분포 값을 배울 필요가 있습니다.
+
+## Q-Learning의 핵심: Bellman 방정식
+
+움직이기 시작하면, 각 액션은 알맞은 보상을 가집니다, 예시로. 이론적으로 가장 높은 보상을 바로 주면서 다음 액션을 선택할 수 있습니다. 그러나, 대부분 state 에서, 행동은 사과에 가려는 목표가 성취감에 없으므로, 어떤 방향이 더 좋은지 바로 결정하지 못합니다.
+
+> 즉시 발생되는 결과가 아니라, 시뮬레이션의 끝에 도달했을 때 생기는, 최종 결과라는 것을 기억합니다.
+
+딜레이된 보상에 대해 설명하려면, 문제를 재귀적으로 생각할 수 있는, **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)** 의 원칙을 사용할 필요가 있습니다.
+
+지금 state *s*에 있고, state *s'* 다음으로 움직이길 원한다고 가정합니다. 그렇게 하면, 보상 함수로 정의된 즉시 보상 *r(s,a)* 를 일부 미래 보상과 함께 받게 될 예정입니다. Q-Table이 각 액션의 "attractiveness"를 올바르게 가져온다고 가정하면, *s'* state에서 *Q(s',a')* 의 최대 값에 대응하는 *a* 액션을 선택하게 됩니다.  그래서, state *s*에서 얻을 수 있는 가능한 좋은 미래 보상은 `max`<sub>a'</sub>*Q(s',a')*로 정의될 예정입니다. (여기의 최대는 state *s'*에서 가능한 모든 *a'* 액션으로 계산합니다.)
+
+주어진 *a* 액션에서, state *s*의 Q-Table 값을 계산하는 **Bellman formula**가 주어집니다:
+
+<img src="../images/bellmaneq.gif"/>
+
+여기 y는 미래 보상보다 현재 보상을 얼마나 선호하는지 판단하는 **discount factor**로 불리고 있고 반대의 경우도 그렇습니다.
+
+## 알고리즘 학습
+
+다음 방정식이 주어지면, 학습 알고리즘의 의사 코드를 작성할 수 있습니다:
+
+* 모든 state와 액션에 대하여 같은 숫자로 Q-Table Q 초기화
+* 학습률 α ← 1 설정
+* 수차례 시뮬레이션 반복
+   1. 랜덤 위치에서 시작
+   1. 반복
+        1. state *s* 에서  *a* 액션 선택
+        2. 새로운 state *s'* 로 이동해서 액션 수행
+        3. 게임이 끝나는 조건이 생기거나, 또는 총 보상이 너무 작은 경우 - 시뮬레이션 종료 
+        4. 새로운 state에서 *r* 보상 계산
+        5. Bellman 방정식 따라서 Q-Function 업데이트 : *Q(s,a)* ← *(1-α)Q(s,a)+α(r+γ max<sub>a'</sub>Q(s',a'))*
+        6. *s* ← *s'*
+        7. 총 보상을 업데이트하고 α. 감소
+
+## Exploit vs. explore
+
+다음 알고리즘에서, 2.1 단계에 액션을 어떻게 선택하는지 명확하지 않습니다. 만약 랜덤으로 액션을 선택하면, 환경을 램덤으로 **explore**하게 되고, 일반적으로 가지 않는 영역도 탐험하게 되면서 꽤 자주 죽을 것 같습니다. 다른 방식은 이미 알고 있는 Q-Table 값을 **exploit**하고, state *s*에서 최고의 액션 (높은 Q-Table 값)을 선택하는 방식입니다. 그러나, 다른 state를 막을 수 있고, 최적의 솔루션을 찾지 못할 수 있습니다.
+
+따라서, 최적의 방식은 exploration과 exploitation 사이 밸런스를 잘 조절하는 것입니다. 이는 Q-Table 값에 확률로 비례한 state *s*에서 액션을 선택할 수 있습니다. 초반에, Q-Table 값이 모두 같을 때, 랜덤 선택에 해당하겠지만, 환경을 더 배운다면, agent가 탐험하지 않은 경로를 선택하면서, 최적 경로를 따라갈 가능성이 커집니다.
+
+## Python 구현
+
+지금 학습 알고리즘을 구현할 준비가 되었습니다. 그 전에, Q-Table에서 임의 숫자를 액션과 대응되는 확률 백터로 변환하는 일부 함수가 필요합니다.
+
+1. `probs()` 함수를 만듭니다:
+
+    ```python
+    def probs(v,eps=1e-4):
+        v = v-v.min()+eps
+        v = v/v.sum()
+        return v
+    ```
+
+    백터의 모든 컴포넌트가 똑같다면, 초기 케이스에서 0으로 나누는 것을 피하기 위해서 원본 백터에 약간의 `eps`를 추가합니다.
+
+**epochs**라고 불리는, 5000번 실험으로 학습 알고리즘을 실행합니다: (code block 8)
+
+```python
+    for epoch in range(5000):
+    
+        # Pick initial point
+        m.random_start()
+        
+        # Start travelling
+        n=0
+        cum_reward = 0
+        while True:
+            x,y = m.human
+            v = probs(Q[x,y])
+            a = random.choices(list(actions),weights=v)[0]
+            dpos = actions[a]
+            m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode
+            r = reward(m)
+            cum_reward += r
+            if r==end_reward or cum_reward < -1000:
+                lpath.append(n)
+                break
+            alpha = np.exp(-n / 10e5)
+            gamma = 0.5
+            ai = action_idx[a]
+            Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())
+            n+=1
+```
+
+이 알고리즘이 실행된 후, Q-Table은 각 단계에 다른 액션의 attractiveness를 정의하는 값으로 업데이트해야 합니다. 움직이고 싶은 방향의 방향으로 각 셀에 백터를 plot해서 Q-Table을 시각화할 수 있습니다. 단순하게, 화살표 머리 대신 작은 원을 그립니다.
+
+<img src="../images/learned.png"/>
+
+## 정책 확인
+
+Q-Table은 각 state에서 각 액션의 "attractiveness"를 리스트로 가지고 있으므로, 사용해서 세계에서 실력있는 네비게이션으로 정의하는 것은 상당히 쉽습니다. 간편한 케이스는, 가장 높은 Q-Table 값에 상응하는 액션을 선택할 수 있는 경우입니다: (code block 9)
+
+```python
+def qpolicy_strict(m):
+        x,y = m.human
+        v = probs(Q[x,y])
+        a = list(actions)[np.argmax(v)]
+        return a
+
+walk(m,qpolicy_strict)
+```
+
+> 만약 코드를 여러 번 시도하면, 가끔 "hangs"을 일으키고, 노트북에 있는 STOP 버튼을 눌러서 중단할 필요가 있다는 점을 알게 됩니다. 최적 Q-Value의 측면에서 두 state가 "point"하는 상황이 있을 수 있기 때문에, 이러한 케이스에는 agent가 그 state 사이를 무한으로 이동하는 현상이 발생됩니다.
+
+## 🚀 도전
+
+> **Task 1:** 특정 걸음 (say, 100) 숫자로 경로의 최대 길이를 제한하는 `walk` 함수를 수정하고, 가끔 코드가 이 값을 반환하는지 지켜봅니다.
+
+> **Task 2:** 이미 이전에 갔던 곳으로 돌아가지 않도록 `walk` 함수를 수정합니다. `walk`를 반복하는 것을 막을 수 있지만, agent가 탈출할 수 없는 곳은 여전히 "trapped" 될 수 있습니다.
+
+## Navigation
+
+더 좋은 네비게이션 정책으로 exploitation과 exploration을 합쳐서, 훈련 중에 사용했습니다. 이 정책에서, Q-Table 값에 비례해서, 특정 확률로 각 액션을 선택할 예정입니다. 이 전략은 이미 탐험한 위치로 agent가 돌아가는 결과를 도출할 수 있지만, 해당 코드에서 볼 수 있는 것처럼, 원하는 위치로 가는 평균 경로가 매우 짧게 결과가 나옵니다. (`print_statistics`가 100번 시뮬레이션 돌렸다는 점을 기억합니다): (code block 10)
+
+```python
+def qpolicy(m):
+        x,y = m.human
+        v = probs(Q[x,y])
+        a = random.choices(list(actions),weights=v)[0]
+        return a
+
+print_statistics(qpolicy)
+```
+
+이 코드가 실행된 후, 3-6의 범위에서, 이전보다 매우 작은 평균 경로 길이를 얻어야 합니다.
+
+## 학습 프로세스 조사
+
+언급했던 것처럼, 학습 프로세스는 문제 공간의 구조에 대해 얻은 지식에 exploration과 exploration 사이 밸런스를 잘 맞춥니다. 학습 결과가 (agent가 목표로 가는 깗은 경로를 찾을 수 있게 도와주는 능력) 개선되었던 것을 볼 수 있지만, 학습 프로세스를 하는 동안에 평균 경로 길이가 어떻게 작동하는지 보는 것도 흥미롭습니다:
+
+<img src="../images/lpathlen1.png"/>
+
+배운 내용을 오약하면 이렇습니다:
+
+- **평균 경로의 길이 증가**. 처음 여기에서 볼 수 있는 것은, 평균 경로 길이가 증가하는 것입니다. 아마도 환경에 대해 잘 모를 때, 물 또는 늑대, 나쁜 state에 걸릴 수 있다는 사실입니다. 더 배우고 지식으로 시작하면, 환경을 오래 탐험할 수 있지만, 여전히 사과가 어디에 자라고 있는지 잘 모릅니다.
+
+- **배울수록, 경로 길이 감소**. 충분히 배웠으면, agent가 목표를 달성하는 것은 더 쉽고, 경로 길이가 줄어들기 시작합니다. 그러나 여전히 탐색하게 되므로, 최적의 경로를 자주 빗겨나가고, 새로운 옵션을 탐험해서, 경로를 최적 경로보다 길게 만듭니다.
+
+- **급격한 길이 증가**. 이 그래프에서 관찰할 수 있는 약간의 포인트는, 갑자기 길이가 길어졌다는 것입니다. 프로세스의 추계학 특성을 나타내고, 일부 포인트에서 새로운 값을 덮어쓰는 Q-Table 계수로 "spoil"될 수 있습니다. 관성적으로 학습률을 줄여서 최소화해야 합니다 (예시로, 훈련이 끝나기 직전에, 작은 값으로만 Q-Table 값을 맞춥니다).
+
+전체적으로, 학습 프로세스의 성공과 퀄리티는 학습률, 학습률 감소, 그리고 감가율처럼 파라미터에 기반하는게 상당히 중요하다는 점을 기억합니다. 훈련하면서 최적화하면 (예시로, Q-Table coefficients), **parameters**와 구별해서, 가끔 **hyperparameters**라고 불립니다. 최고의 hyperparameter 값을 찾는 프로세스는 **hyperparameter optimization**이라고 불리며, 별도의 토픽이 있을 만합니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
+
+## 과제 
+
+[A More Realistic World](../assignment.md)
diff --git a/8-Reinforcement/1-QLearning/translations/assignment.it.md b/8-Reinforcement/1-QLearning/translations/assignment.it.md
new file mode 100644
index 000000000..37b251953
--- /dev/null
+++ b/8-Reinforcement/1-QLearning/translations/assignment.it.md
@@ -0,0 +1,27 @@
+# Un mondo più realistico
+
+Nella situazione citata, Pierino riusciva a muoversi quasi senza stancarsi o avere fame. In un mondo più realistico, ci si deve sedere e riposare di tanto in tanto, e anche nutrirsi. Si rende questo mondo più realistico, implementando le seguenti regole:
+
+1. Spostandosi da un luogo all'altro, Pierino perde **energia** e accumula un po' di **fatica**.
+2. Pierino può guadagnare più energia mangiando mele.
+3. Pierino può recuperare energie riposando sotto l'albero o sull'erba (cioè camminando in una posizione nella tabola di gioco con un un albero o un prato - campo verde)
+4. Pierino ha bisogno di trovare e uccidere il lupo
+5. Per uccidere il lupo, Pierino deve avere determinati livelli di energia e fatica, altrimenti perde la battaglia.
+
+## Istruzioni
+
+Usare il notebook originale [notebook.ipynb](../notebook.ipynb) come punto di partenza per la propria soluzione.
+
+Modificare la funzione di ricompensa in base alle regole del gioco, eseguire l'algoritmo di reinforcement learning per apprendere la migliore strategia per vincere la partita e confrontare i risultati della passeggiata aleatoria con il proprio algoritmo in termini di numero di partite vinte e perse.
+
+> **Nota**: in questo nuovo mondo, lo stato è più complesso e oltre alla posizione umana include anche la fatica e i livelli di energia. Si può scegliere di rappresentare lo stato come una tupla (Board,energy,fatigue) - (Tavola, Energia, Fatica), o definire una classe per lo stato (si potrebbe anche volerla derivare da `Board`), o anche modificare la classe `Board` originale all'interno di [rlboard.py](../rlboard.py).
+
+Nella propria soluzione, mantenere il codice responsabile della strategia di passeggiata aleatoria e confrontare i risultati del proprio algoritmo con la passeggiata aleatoria alla fine.
+
+> **Nota**: potrebbe essere necessario regolare gli iperparametri per farlo funzionare, in particolare il numero di epoche. Poiché il successo del gioco (lotta contro il lupo) è un evento raro, ci si può aspettare un tempo di allenamento molto più lungo.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+|          | Viene presentato un notebook con la definizione delle nuove regole del mondo, l'algoritmo di Q-Learning e alcune spiegazioni testuali. Q-Learning è in grado di migliorare significativamente i risultati rispetto a random walk. | Viene presentato il notebook, viene implementato Q-Learning e migliora i risultati rispetto a random walk, ma non in modo significativo; o il notebook è scarsamente documentato e il codice non è ben strutturato | Vengono fatti alcuni tentativi di ridefinire le regole del mondo, ma l'algoritmo Q-Learning non funziona o la funzione di ricompensa non è completamente definita |
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index b1a2e2a22..7f8df688f 100644
--- a/8-Reinforcement/2-Gym/README.md
+++ b/8-Reinforcement/2-Gym/README.md
@@ -1,8 +1,8 @@
 # CartPole Skating
 
 The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**.
-
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/)
+https://white-water-09ec41f0f.azurestaticapps.net/
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/)
 
 ## Introduction
 
@@ -329,7 +329,7 @@ You should see something like this:
 
 > **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with the highest Q-Table value? This can be done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/48/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
 
 ## Assignment: [Train a Mountain Car](assignment.md)
 
diff --git a/8-Reinforcement/2-Gym/solution/Julia/README.md b/8-Reinforcement/2-Gym/solution/Julia/README.md
new file mode 100644
index 000000000..43447e1b8
--- /dev/null
+++ b/8-Reinforcement/2-Gym/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
\ No newline at end of file
diff --git a/8-Reinforcement/2-Gym/solution/R/README.md b/8-Reinforcement/2-Gym/solution/R/README.md
new file mode 100644
index 000000000..f59c07cc0
--- /dev/null
+++ b/8-Reinforcement/2-Gym/solution/R/README.md
@@ -0,0 +1 @@
+this is a temporary placeholder
\ No newline at end of file
diff --git a/8-Reinforcement/2-Gym/translations/README.it.md b/8-Reinforcement/2-Gym/translations/README.it.md
new file mode 100644
index 000000000..07152028e
--- /dev/null
+++ b/8-Reinforcement/2-Gym/translations/README.it.md
@@ -0,0 +1,340 @@
+# CartPole Skating
+
+Il problema risolto nella lezione precedente potrebbe sembrare un problema giocattolo, non propriamente applicabile a scenari di vita reale. Questo non è il caso, perché anche molti problemi del mondo reale condividono questo scenario, incluso Scacchi o Go. Sono simili, perché anche in quei casi si ha una tavolo di gioco con regole date e uno **stato discreto**.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/?loc=it)
+
+## Introduzione
+
+In questa lezione si applicheranno gli stessi principi di Q-Learning ad un problema con **stato continuo**, cioè uno stato dato da uno o più numeri reali. Ci si occuperà del seguente problema:
+
+> **Problema**: se Pierino vuole scappare dal lupo, deve essere in grado di muoversi più velocemente. Si vedrà come Pierino può imparare a pattinare, in particolare, a mantenere l'equilibrio, utilizzando Q-Learning.
+
+![La grande fuga!](../images/escape.png)
+
+> Pierino e i suoi amici diventano creativi per sfuggire al lupo! Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+Si userà una versione semplificata del bilanciamento noto come **problema** CartPole. Nel mondo cartpole, c'è un cursore orizzontale che può spostarsi a sinistra o a destra, e l'obiettivo è bilanciare un palo verticale sopra il cursore.
+
+<img alt="un cartpole" src="../images/cartpole.png" width="200"/>
+
+## Prerequisiti
+
+In questa lezione si utilizzerà una libreria chiamata **OpenAI Gym per** simulare **ambienti** diversi. Si può eseguire il codice di questa lezione localmente (es. da Visual Studio Code), nel qual caso la simulazione si aprirà in una nuova finestra. Quando si esegue il codice online, potrebbe essere necessario apportare alcune modifiche, come descritto [qui](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7).
+
+## OpenAI Gym
+
+Nella lezione precedente, le regole del gioco e lo stato sono state date dalla classe `Board` sviluppata nel codice. Qui si utilizzerà uno speciale **ambiente di simulazione**, che simulerà la fisica dietro il palo di bilanciamento. Uno degli ambienti di simulazione più popolari per addestrare gli algoritmi di reinforcement learning è chiamato a [Gym](https://gym.openai.com/), mantenuto da [OpenAI](https://openai.com/). Con questo gym è possibile creare **ambienti** diversi da una simulazione cartpole a giochi Atari.
+
+> **Nota**: si possono vedere altri ambienti disponibili da OpenAI Gym [qui](https://gym.openai.com/envs/#classic_control).
+
+Innanzitutto, si installa gym e si importano le librerie richieste (blocco di codice 1):
+
+```python
+import sys
+!{sys.executable} -m pip install gym
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import random
+```
+
+## Esercizio: inizializzare un ambiente cartpole
+
+Per lavorare con un problema di bilanciamento del cartpole, è necessario inizializzare l'ambiente corrispondente. Ad ogni ambiente è associato uno:
+
+- **Spazio di osservazione** che definisce la struttura delle informazioni ricevute dall'ambiente. Per il problema del cartpole, si riceve la posizione del palo, la velocità e alcuni altri valori.
+
+- **Spazio di azione** che definisce le possibili azioni. In questo caso lo spazio delle azioni è discreto e consiste di due azioni: **sinistra** e **destra**. (blocco di codice 2)
+
+1. Per inizializzare, digitare il seguente codice:
+
+   ```python
+   env = gym.make("CartPole-v1")
+   print(env.action_space)
+   print(env.observation_space)
+   print(env.action_space.sample())
+   ```
+
+Per vedere come funziona l'ambiente, si esegue una breve simulazione di 100 passaggi. Ad ogni passaggio, si fornisce una delle azioni da intraprendere: in questa simulazione si seleziona casualmente un'azione da `action_space`.
+
+1. Eseguire il codice qui sotto e guardare a cosa porta.
+
+   ✅ Ricordare che è preferibile eseguire questo codice sull'installazione locale di Python! (blocco di codice 3)
+
+   ```python
+   env.reset()
+
+   for i in range(100):
+      env.render()
+      env.step(env.action_space.sample())
+   env.close()
+   ```
+
+   Si dovrebbe vedere qualcosa di simile a questa immagine:
+
+   ![carrello non in equilibrio](../images/cartpole-nobalance.gif)
+
+1. Durante la simulazione, sono necessarie osservazioni per decidere come agire. Infatti, la funzione step restituisce le osservazioni correnti, una funzione di ricompensa e il flag done che indica se ha senso continuare o meno la simulazione: (blocco di codice 4)
+
+   ```python
+   env.reset()
+
+   done = False
+   while not done:
+      env.render()
+      obs, rew, done, info = env.step(env.action_space.sample())
+      print(f"{obs} -> {rew}")
+   env.close()
+   ```
+
+   Si finirà per vedere qualcosa di simile nell'output del notebook:
+
+   ```text
+   [ 0.03403272 -0.24301182  0.02669811  0.2895829 ] -> 1.0
+   [ 0.02917248 -0.04828055  0.03248977  0.00543839] -> 1.0
+   [ 0.02820687  0.14636075  0.03259854 -0.27681916] -> 1.0
+   [ 0.03113408  0.34100283  0.02706215 -0.55904489] -> 1.0
+   [ 0.03795414  0.53573468  0.01588125 -0.84308041] -> 1.0
+   ...
+   [ 0.17299878  0.15868546 -0.20754175 -0.55975453] -> 1.0
+   [ 0.17617249  0.35602306 -0.21873684 -0.90998894] -> 1.0
+   ```
+
+   Il vettore di osservazione restituito ad ogni passo della simulazione contiene i seguenti valori:
+   - Posizione del carrello
+   - Velocità del carrello
+   - Angolo del palo
+   - Tasso di rotazione del palo
+
+1. Ottenere il valore minimo e massimo di quei numeri: (blocco di codice 5)
+
+   ```python
+   print(env.observation_space.low)
+   print(env.observation_space.high)
+   ```
+
+   Si potrebbe anche notare che il valore della ricompensa in ogni fase della simulazione è sempre 1. Questo perché l'obiettivo è sopravvivere il più a lungo possibile, ovvero mantenere il palo in una posizione ragionevolmente verticale per il periodo di tempo più lungo.
+
+   ✅ Infatti la simulazione CartPole si considera risolta se si riesce a ottenere la ricompensa media di 195 su 100 prove consecutive.
+
+## Discretizzazione dello stato
+
+In Q-Learning, occorre costruire una Q-Table che definisce cosa fare in ogni stato. Per poterlo fare, è necessario che lo stato sia **discreto**, più precisamente, dovrebbe contenere un numero finito di valori discreti. Quindi, serve un qualche modo per **discretizzare** le osservazioni, mappandole su un insieme finito di stati.
+
+Ci sono alcuni modi in cui si può fare:
+
+- **Dividere in contenitori**. Se è noto l'intervallo di un certo valore, si può dividere questo intervallo in un numero di **bin** (contenitori) e quindi sostituire il valore con il numero di contenitore a cui appartiene. Questo può essere fatto usando il metodo di numpy [`digitize`](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html). In questo caso, si conoscerà con precisione la dimensione dello stato, perché dipenderà dal numero di contenitori selezionati per la digitalizzazione.
+
+✅ Si può usare l'interpolazione lineare per portare i valori a qualche intervallo finito (ad esempio, da -20 a 20), e poi convertire i numeri in interi arrotondandoli. Questo dà un po' meno controllo sulla dimensione dello stato, specialmente se non sono noti gli intervalli esatti dei valori di input. Ad esempio, in questo caso 2 valori su 4 non hanno limiti superiore/inferiore sui loro valori, il che può comportare un numero infinito di stati.
+
+In questo esempio, si andrà con il secondo approccio. Come si potrà notare in seguito, nonostante i limiti superiore/inferiore non definiti, quei valori raramente assumono valori al di fuori di determinati intervalli finiti, quindi quegli stati con valori estremi saranno molto rari.
+
+1. Ecco la funzione che prenderà l'osservazione dal modello e produrrà una tupla di 4 valori interi: (blocco di codice 6)
+
+   ```python
+   def discretize(x):
+       return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))
+   ```
+
+1. Si esplora anche un altro metodo di discretizzazione utilizzando i contenitori: (blocco di codice 7)
+
+   ```python
+   def create_bins(i,num):
+       return np.arange(num+1)*(i[1]-i[0])/num+i[0]
+
+   print("Sample bins for interval (-5,5) with 10 bins\n",create_bins((-5,5),10))
+
+   ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # Intervallo di valori per ogni parametro
+   nbins = [20,20,10,10] # numero di contenitori per ogni parametro
+   bins = [create_bins(ints[i],nbins[i]) for i in range(4)]
+
+   def discretize_bins(x):
+       return tuple(np.digitize(x[i],bins[i]) for i in range(4))
+   ```
+
+1. Si esegue ora una breve simulazione e si osservano quei valori discreti dell'ambiente. Si può provare `discretize` e `discretize_bins` e vedere se c'è una differenza.
+
+   ✅ discretize_bins restituisce il numero del contenitore, che è in base 0. Quindi per i valori della variabile di input intorno a 0 restituisce il numero dalla metà dell'intervallo (10). In discretize, non interessava l'intervallo dei valori di uscita, consentendo loro di essere negativi, quindi i valori di stato non vengono spostati e 0 corrisponde a 0. (blocco di codice 8)
+
+   ```python
+   env.reset()
+
+   done = False
+   while not done:
+      #env.render()
+      obs, rew, done, info = env.step(env.action_space.sample())
+      #print(discretize_bins(obs))
+      print(discretize(obs))
+   env.close()
+   ```
+
+   ✅ Decommentare la riga che inizia con env.render se si vuole vedere come viene eseguito l'ambiente. Altrimenti si può eseguirlo in background, che è più veloce. Si userà questa esecuzione "invisibile" durante il processo di Q-Learning.
+
+## La struttura di Q-Table
+
+Nella lezione precedente, lo stato era una semplice coppia di numeri da 0 a 8, e quindi era conveniente rappresentare Q-Table con un tensore numpy con una forma di 8x8x2. Se si usa la discretizzazione dei contenitori, è nota anche la dimensione del vettore di stato, quindi si può usare lo stesso approccio e rappresentare lo stato con un array di forma 20x20x10x10x2 (qui 2 è la dimensione dello spazio delle azioni e le prime dimensioni corrispondono al numero di contenitori che si è scelto di utilizzare per ciascuno dei parametri nello spazio di osservazione).
+
+Tuttavia, a volte non sono note dimensioni precise dello spazio di osservazione. Nel caso della funzione `discretize`, si potrebbe non essere mai sicuri che lo stato rimanga entro certi limiti, perché alcuni dei valori originali non sono vincolati. Pertanto, si utilizzerà un approccio leggermente diverso e si rappresenterà Q-Table con un dizionario.
+
+1. Si usa la coppia *(state, action)* come chiave del dizionario e il valore corrisponderà al valore della voce Q-Table. (blocco di codice 9)
+
+   ```python
+   Q = {}
+   actions = (0,1)
+
+   def qvalues(state):
+       return [Q.get((state,a),0) for a in actions]
+   ```
+
+   Qui si definisce anche una funzione `qvalues()`, che restituisce un elenco di valori di Q-Table per un dato stato che corrisponde a tutte le azioni possibili. Se la voce non è presente nella Q-Table, si restituirà 0 come predefinito.
+
+## Far partire Q-Learning
+
+Ora si è pronti per insegnare a Pierino a bilanciare!
+
+1. Per prima cosa, si impostano alcuni iperparametri: (blocco di codice 10)
+
+   ```python
+   # iperparametri
+   alpha = 0.3
+   gamma = 0.9
+   epsilon = 0.90
+   ```
+
+   Qui, `alfa` è il **tasso di apprendimento** che definisce fino a che punto si dovranno regolare i valori correnti di Q-Table ad ogni passaggio. Nella lezione precedente si è iniziato con 1, quindi si è ridotto `alfa` per abbassare i valori durante l'allenamento. In questo esempio lo si manterrà costante solo per semplicità e si potrà sperimentare con la regolazione dei valori `alfa` in un secondo momento.
+
+   `gamma` è il **fattore di sconto** che mostra fino a che punto si dovrà dare la priorità alla ricompensa futura rispetto alla ricompensa attuale.
+
+   `epsilon` è il **fattore di esplorazione/sfruttamento** che determina se preferire l'esplorazione allo sfruttamento o viceversa. In questo algoritmo, nella percentuale `epsilon` dei casi si selezionerà l'azione successiva in base ai valori della Q-Table e nel restante numero di casi verrà eseguita un'azione casuale. Questo  permetterà di esplorare aree dello spazio di ricerca che non sono mai state viste prima.
+
+   ✅ In termini di bilanciamento - la scelta di un'azione casuale (esplorazione) agirebbe come un pugno casuale nella direzione sbagliata e il palo dovrebbe imparare a recuperare l'equilibrio da quegli "errori"
+
+### Migliorare l'algoritmo
+
+E' possibile anche apportare due miglioramenti all'algoritmo rispetto alla lezione precedente:
+
+- **Calcolare la ricompensa cumulativa media**, su una serie di simulazioni. Si stamperanno i progressi ogni 5000 iterazioni e si farà la media della ricompensa cumulativa in quel periodo di tempo. Significa che se si ottengono più di 195 punti, si può considerare il problema risolto, con una qualità ancora superiore a quella richiesta.
+
+- **Calcolare il risultato cumulativo medio massimo**, `Qmax`, e si memorizzerà la Q-Table corrispondente a quel risultato. Quando si esegue l'allenamento si noterà che a volte il risultato cumulativo medio inizia a diminuire e si vuole mantenere i valori di Q-Table che corrispondono al miglior modello osservato durante l'allenamento.
+
+1. Raccogliere tutte le ricompense cumulative ad ogni simulazione nel vettore `rewards` per ulteriori grafici. (blocco di codice 11)
+
+   ```python
+   def probs(v,eps=1e-4):
+       v = v-v.min()+eps
+       v = v/v.sum()
+       return v
+
+   Qmax = 0
+   cum_rewards = []
+   rewards = []
+   for epoch in range(100000):
+       obs = env.reset()
+       done = False
+       cum_reward=0
+       # == esegue la simulazione ==
+       while not done:
+           s = discretize(obs)
+           if random.random()<epsilon:
+               # sfruttamento - sceglie l'azione in accordo alle probabilità di Q-Table
+               v = probs(np.array(qvalues(s)))
+               a = random.choices(actions,weights=v)[0]
+           else:
+               # esplorazione - sceglie causalmente l'azione
+               a = np.random.randint(env.action_space.n)
+
+           obs, rew, done, info = env.step(a)
+           cum_reward+=rew
+           ns = discretize(obs)
+           Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))
+       cum_rewards.append(cum_reward)
+       rewards.append(cum_reward)
+       # == Stampa periodicamente i risultati a calcola la ricompensa media ==
+       if epoch%5000==0:
+           print(f"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}")
+           if np.average(cum_rewards) > Qmax:
+               Qmax = np.average(cum_rewards)
+               Qbest = Q
+           cum_rewards=[]
+   ```
+
+Cosa si potrebbe notare da questi risultati:
+
+- **Vicino all'obiettivo**. Si è molto vicini al raggiungimento dell'obiettivo di ottenere 195 ricompense cumulative in oltre 100 esecuzioni consecutive della simulazione, o si potrebbe averlo effettivamente raggiunto! Anche se si ottengono numeri più piccoli, non si sa ancora, perché si ha una media di oltre 5000 esecuzioni e nei criteri formali sono richieste solo 100 esecuzioni.
+
+- **La ricompensa inizia a diminuire**. A volte la ricompensa inizia a diminuire, il che significa che si possono "distruggere" i valori già appresi nella Q-Table con quelli che peggiorano la situazione.
+
+Questa osservazione è più chiaramente visibile se si tracciano i progressi dell'allenamento.
+
+## Tracciare i progressi dell'allenamento
+
+Durante l'addestramento, si è raccolto il valore cumulativo della ricompensa a ciascuna delle iterazioni nel vettore delle ricompense `reward` . Ecco come appare quando viene riportato al numero di iterazione:
+
+```python
+plt.plot(rewards)
+```
+
+![progresso grezzo](../images/train_progress_raw.png)
+
+Da questo grafico non è possibile dire nulla, perché a causa della natura del processo di allenamento stocastico la durata delle sessioni di allenamento varia notevolmente. Per dare più senso a questo grafico, si può calcolare la **media mobile su** una serie di esperimenti, ad esempio 100. Questo può essere fatto comodamente usando `np.convolve` : (blocco di codice 12)
+
+```python
+def running_average(x,window):
+    return np.convolve(x,np.ones(window)/window,mode='valid')
+
+plt.plot(running_average(rewards,100))
+```
+
+![Progressi dell'allenamento](../images/train_progress_runav.png)
+
+## Variare gli iperparametri
+
+Per rendere l'apprendimento più stabile, ha senso regolare alcuni degli iperparametri durante l'allenamento. In particolare:
+
+- **Per il tasso di apprendimento**, `alfa`, si può iniziare con valori vicini a 1 e poi continuare a diminuire il parametro. Con il tempo, si otterranno buoni valori di probabilità nella Q-Table, e quindi si dovranno modificare leggermente e non sovrascrivere completamente con nuovi valori.
+
+- **Aumentare epsilon**. Si potrebbe voler aumentare lentamente `epsilon`, in modo da esplorare di meno e sfruttare di più. Probabilmente ha senso iniziare con un valore inferiore di `epsilone` e salire fino a quasi 1.
+
+> **Compito 1**: giocare con i valori degli iperparametri e vedere se si riesce a ottenere una ricompensa cumulativa più alta. Si stanno superando i 195?
+
+> **Compito 2**: per risolvere formalmente il problema, si devono ottenere 195 ricompense medie in 100 esecuzioni consecutive. Misurare questo durante l'allenamento e assicurarsi di aver risolto formalmente il problema!
+
+## Vedere il risultato in azione
+
+Sarebbe interessante vedere effettivamente come si comporta il modello addestrato. Si esegue la simulazione e si segue la stessa strategia di selezione dell'azione utilizzata durante l'addestramento, campionando secondo la distribuzione di probabilità in Q-Table: (blocco di codice 13)
+
+```python
+obs = env.reset()
+done = False
+while not done:
+   s = discretize(obs)
+   env.render()
+   v = probs(np.array(qvalues(s)))
+   a = random.choices(actions,weights=v)[0]
+   obs,_,done,_ = env.step(a)
+env.close()
+```
+
+Si dovrebbe vedere qualcosa del genere:
+
+![un cartpole equilibratore](../images/cartpole-balance.gif)
+
+---
+
+## 🚀 Sfida
+
+> **Compito 3**: qui si stava usando la copia finale di Q-Table, che potrebbe non essere la migliore. Ricordare che si è memorizzato la Q-Table con le migliori prestazioni nella variabile `Qbest`! Provare lo stesso esempio con la Q-Table di migliori prestazioni copiando `Qbest` su `Q` e vedere se si nota la differenza.
+
+> **Compito 4**: Qui non si stava selezionando l'azione migliore per ogni passaggio, ma piuttosto campionando con la corrispondente distribuzione di probabilità. Avrebbe più senso selezionare sempre l'azione migliore, con il valore Q-Table più alto? Questo può essere fatto usando la funzione `np.argmax` per trovare il numero dell'azione corrispondente al valore della Q-Table più alto. Implementare questa strategia e vedere se migliora il bilanciamento.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/?loc=it)
+
+## Compito: [addestrare un'auto di montagna](assignment.it.md)
+
+## Conclusione
+
+Ora si è imparato come addestrare gli agenti a ottenere buoni risultati semplicemente fornendo loro una funzione di ricompensa che definisce lo stato desiderato del gioco e dando loro l'opportunità di esplorare in modo intelligente lo spazio di ricerca. E' stato applicato con successo l'algoritmo di Q-Learning nei casi di ambienti discreti e continui, ma con azioni discrete.
+
+È importante studiare anche situazioni in cui anche lo stato di azione è continuo e quando lo spazio di osservazione è molto più complesso, come l'immagine dalla schermata di gioco dell'Atari. In questi problemi spesso è necessario utilizzare tecniche di apprendimento automatico più potenti, come le reti neurali, per ottenere buoni risultati. Questi argomenti più avanzati sono l'oggetto del prossimo corso di intelligenza artificiale più avanzato.
diff --git a/8-Reinforcement/2-Gym/translations/README.ko.md b/8-Reinforcement/2-Gym/translations/README.ko.md
new file mode 100644
index 000000000..8fc05e8f4
--- /dev/null
+++ b/8-Reinforcement/2-Gym/translations/README.ko.md
@@ -0,0 +1,340 @@
+# CartPole 스케이팅
+
+이전 강의에서 풀었던 문제는 장난감 문제처럼 보일 수 있고, 실제 시나리오에서 진짜 적용되지 않습니다. 체스나 바둑을 즐기는 것을 포함한 - 시나리오에 많은 실제 문제와 공유하기 때문에, 이 케이스는 아닙니다. 주어진 룰과 **discrete state**를 보드가 가지고 있기 때문에 비슷합니다. 
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/)
+
+## 소개
+
+이 강의에서 Q-Learning의 같은 원칙을 하나 이상의 실수가 주어진 state인, **continuous state** 문제로 적용할 예정입니다. 다음 문제를 다루게 됩니다:
+
+> **문제**: 만약 Peter가 늑대로부터 도망가길 원한다면, 빠르게 움직일 필요가 있습니다. Peter가 특히 Q-Learning으로, 밸런스를 유지하면서, 스케이트를 배울 수 있는지 보게 됩니다.
+
+![The great escape!](../images/escape.png)
+
+> Peter와 친구들은 늑대로부터 도망갈 창의력을 얻습니다! Image by [Jen Looper](https://twitter.com/jenlooper)
+
+**CartPole** 문제로 알려진 밸런스있는 간단한 버전을 사용할 예정입니다. cartpole 월드에서, 왼쪽과 오른쪽으로 움직일 수 있는 수평 슬라이더를 가지며, 목표는 슬라이더의 위쪽에 있는 수직 폴을 밸런스하는 것입니다.
+
+<img alt="a cartpole" src="../images/cartpole.png" width="200"/>
+
+## 전제 조건
+
+이 강의에서, **OpenAI Gym**으로 불리는 라이브러리를 사용해서 다른 **environments**를 시뮬레이션합니다. 이 강의의 코드를 로컬에서 (에시. from Visual Studio Code), 새로운 윈도우로 열어서 시뮬레이션할 수 있습니다. 코드를 온라인으로 실행할 때는, [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7)에서 설명된 것처럼, 코드를 약간 트윅할 필요가 있습니다.
+
+## OpenAI Gym
+
+이전 강의에서, 게임의 룰과 state는 스스로 정의했던 `Board` 클래스로 주어졌습니다. balancing pole의 물리학 그늘에서 시뮬레이션하는, 특별한 **simulation environment**를 사용할 예정입니다. 가장 인기있는 reinforcement learning 알고리즘을 훈련하기 위한 시뮬레이션 환경은 [OpenAI](https://openai.com/)애서 관리하는, [Gym](https://gym.openai.com/)이라고 불립니다. 이 gym을 사용해서 cartpole 시뮬레이션부터 Atari 게임까지 다양한 **environments**를 만들 수 있습니다.
+
+> **노트**: OpenAI Gym [here](https://gym.openai.com/envs/#classic_control)에서 사용할 수 있는 다양한 환경을 볼 수 있습니다.
+
+먼저, gym을 설치하고 필요한 라이브러리를 가져옵니다 (code block 1):
+
+```python
+import sys
+!{sys.executable} -m pip install gym 
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import random
+```
+
+## 연습 - cartpole 환경 초기화
+
+cartpole balancing 문제를 풀려면, 대상 환경을 초기화할 필요가 있습니다.각 환경은 이렇게 연결되었습니다:
+
+- **Observation space** 은 환경으로부터 받는 정보의 구조를 정의합니다. cartpole 문제라면, 폴의 위치, 속도와 일부 기타 값을 받습니다.
+
+- **Action space** 는 가능한 액션을 정의합니다. 이 케이스에서 action space는 추상적이고, 두 액션 **left**와 **right**로 이루어져 있습니다. (code block 2)
+
+1. 초기화하려면, 다음 코드를 타이핑합니다:
+
+    ```python
+    env = gym.make("CartPole-v1")
+    print(env.action_space)
+    print(env.observation_space)
+    print(env.action_space.sample())
+    ```
+
+어떻게 환경이 동작하는지 보려면, 100 번 짧게 시뮬레이션 돌립니다. 각 단계에서, 수행할 액션 중 하나를 제공합니다 - 이 시뮬레이션에서 `action_space`로 액션을 랜덤 선택하게 됩니다.
+
+1. 아래 코드를 실행하고 어떤 결과가 나오는지 봅니다.
+
+    ✅ 로컬 설치한 Python에서 이 코드를 실행하도록 권장한다는 점을 기억합니다! (code block 3)
+
+    ```python
+    env.reset()
+    
+    for i in range(100):
+       env.render()
+       env.step(env.action_space.sample())
+    env.close()
+    ```
+
+    이 이미지와 비슷한 내용이 보여지게 됩니다:
+
+    ![non-balancing cartpole](../images/cartpole-nobalance.gif)
+
+1. 시뮬레이션하는 동안, 액션 방식을 판단하기 위해서 관찰할 필요가 있습니다. 사실, step 함수는 현재 관측치, 보상 함수, 그리고 시뮬레이션을 지속하거나 끝내고자 가리키는 종료 플래그를 반환합니다: (code block 4)
+
+    ```python
+    env.reset()
+    
+    done = False
+    while not done:
+       env.render()
+       obs, rew, done, info = env.step(env.action_space.sample())
+       print(f"{obs} -> {rew}")
+    env.close()
+    ```
+
+    노트북 출력에서 이렇게 보이게 됩니다:
+
+    ```text
+    [ 0.03403272 -0.24301182  0.02669811  0.2895829 ] -> 1.0
+    [ 0.02917248 -0.04828055  0.03248977  0.00543839] -> 1.0
+    [ 0.02820687  0.14636075  0.03259854 -0.27681916] -> 1.0
+    [ 0.03113408  0.34100283  0.02706215 -0.55904489] -> 1.0
+    [ 0.03795414  0.53573468  0.01588125 -0.84308041] -> 1.0
+    ...
+    [ 0.17299878  0.15868546 -0.20754175 -0.55975453] -> 1.0
+    [ 0.17617249  0.35602306 -0.21873684 -0.90998894] -> 1.0
+    ```
+
+    시뮬레이션의 각 단계에서 반환되는 관찰 백터는 다음 값을 포함합니다:
+    - Position of cart
+    - Velocity of cart
+    - Angle of pole
+    - Rotation rate of pole
+
+1. 이 숫자의 최소 최대 값을 가져옵니다: (code block 5)
+
+    ```python
+    print(env.observation_space.low)
+    print(env.observation_space.high)
+    ```
+
+    각 시뮬레이션 단계의 보상 값은 항상 1이라는 점을 알 수 있습니다. 목표는 가장 긴 기간에 폴을 합리적으로 수직 위치에 유지하며, 가능한 오래 생존하는 것입니다.
+
+    ✅ 사실, the CartPole 시뮬레이션은 100번 넘는 시도에서 평균 195개 보상을 얻을 수 있게 관리되면 해결된 것으로 여깁니다.
+
+## State discretization
+
+Q=Learning에서, 각 state에서 할 것을 정의하는 Q-Table을 만들 필요가 있습니다. 이렇게 하려면, state가 **discreet**으로 되어야하고, 더 정확해지면, 한정된 discrete 값 숫자를 포함해야 합니다. 그래서, 관측치를 어떻게든지 **discretize** 해서, 한정된 state 세트와 맵핑할 필요가 있습니다.
+
+이렇게 할 수 있는 몇 방식이 있습니다:
+
+- **Divide into bins**. 만약 특정 값의 간격을 알고있다면, 간격을 **bins**의 수로 나누고, 값을 속해있는 bin 숫자로 변환할 수 있습니다. numpy [`digitize`](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html) 메소드로 마무리 지을 수 있습니다. 이 케이스에서, 디지털화를 선택하기 위해서 bin의 수를 기반했기 때문에, state 크기를 정확히 알게 됩니다.
+  
+✅ linear interpolation으로 값을 한정된 간격 (say, from -20 to 20)에 가져오고, 반올림해서 숫자를 정수로 변환할 수 있습니다. 특별하게 입력 값의 정확한 범위를 모른다면, state의 크기에서 조금만 컨트롤하게 둡니다. 예시로, 4개 중에 2개 값은 상/하 한도가 없으므로, state 개수에서 무한으로 결과가 나올 수 있습니다. 
+
+예시에서, 두 번째 접근 방식을 사용하겠습니다. 나중에 알게 되듯이, 정의하지 못한 상/하 한도에도, 이 값은 특정하게 한정된 간격에 벗어난 값을 드물게 가지므로, 극단적인 값을 가진 state는 매우 드뭅니다.
+
+1. 여기는 모델에서 관측치를 가지고, 4개 정수 값의 튜플을 만드는 함수입니다: (code block 6)
+
+    ```python
+    def discretize(x):
+        return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))
+    ```
+
+1. bin으로 다른 discretization 방식도 찾아봅니다: (code block 7)
+
+    ```python
+    def create_bins(i,num):
+        return np.arange(num+1)*(i[1]-i[0])/num+i[0]
+    
+    print("Sample bins for interval (-5,5) with 10 bins\n",create_bins((-5,5),10))
+    
+    ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter
+    nbins = [20,20,10,10] # number of bins for each parameter
+    bins = [create_bins(ints[i],nbins[i]) for i in range(4)]
+    
+    def discretize_bins(x):
+        return tuple(np.digitize(x[i],bins[i]) for i in range(4))
+    ```
+
+1. 지금부터 짧은 시뮬레이션을 실행하고 분리된 환경 값을 관찰합니다. 자유롭게 `discretize`와 `discretize_bins`를 시도하고 다른점이 있다면 봅시다.
+
+    ✅ discretize_bins는 0-베이스인, bin 숫자를 반환합니다. 그래서 0 주변 입력 변수의 값은 간격의 중간에서 (10) 숫자를 반환합니다. discretize에서, 출력 값의 범위에 대해서 신경쓰지 못하고, negative를 허용하므로, state 값은 바뀌지 못하고, 0은 0에 대응합니다. (code block 8)
+
+    ```python
+    env.reset()
+    
+    done = False
+    while not done:
+       #env.render()
+       obs, rew, done, info = env.step(env.action_space.sample())
+       #print(discretize_bins(obs))
+       print(discretize(obs))
+    env.close()
+    ```
+
+    ✅ 만약 환경 실행이 어떻게 되는지 보려면 env.render로 시작되는 줄을 주석 해제합니다. 그렇지 않으면 빠르게, 백그라운드에서 실행할 수 있습니다. Q-Learning 프로세스가 진행되면서 "invisible" 실행으로 사용할 수 있습니다.
+
+## Q-Table 구조
+
+이전 강의에서, state는 0부터 8까지 숫자의 간단한 쌍이라서, 8x8x2 형태 numpy tensor로 Q-Table를 표현하는 게 편리합니다. 만약 bins discretization를 사용하면, state vector의 크기를 알 수 있으므로, 같은 접근 방식을 사용하고 20x20x10x10x2 형태 배열로 state를 나타냅니다 (여기 2 개는 액션 공간의 차원이고, 첫 차원은 관찰 공간에서 각 파라미터를 사용하고자 선택된 bin의 숫자에 해당합니다).
+
+그러나, 가끔은 관찰 공간의 정확한 넓이를 알 수 없습니다. `discretize` 함수의 케이스에서, 원본 값의 일부는 결합되지 않으므로, state가 특정한 제한 사항에 놓여있는지 확신할 수 없습니다. 따라서, 조금 다른 접근 방식으로 사용하고 dictionary로 Q-Table을 표현할 예정입니다.
+
+1. dictionary 키로 *(state,action)* 쌍을 사용하고, 값을 Q-Table 엔트리 값에 대응합니다. (code block 9)
+
+    ```python
+    Q = {}
+    actions = (0,1)
+    
+    def qvalues(state):
+        return [Q.get((state,a),0) for a in actions]
+    ```
+
+    여기 가능한 모든 액션에 해당하는 state로 주어진 Q-Table 값의 리스트를 반환하는, `qvalues()` 함수도 정의합니다. 만약 앤트리가 Q-Table에 없다면, 기본적으로 0을 반환합니다.
+
+## Q-Learning 시작합시다
+
+지금부터 Peter에게 밸런스를 가르치기 직전입니다!
+
+1. 먼저, 일부 hyperparameters를 맞춥시다: (code block 10)
+
+    ```python
+    # hyperparameters
+    alpha = 0.3
+    gamma = 0.9
+    epsilon = 0.90
+    ```
+
+    여기, `alpha`는 각 단계에서 Q-Table의 현재 값을 어느정도 범위로 조정할 수 있게 정의하는 **learning rate**입니다. 이전 강의에서는 1로 시작하고, 훈련하는 동안 `alpha`를 낮은 값으로 낮춥니다. 이 예시에서 간단하게 하고자 변함없이 그대로 두고, `alpha` 값을 조정하는 실험을 나중에 진행할 수 있습니다. 
+
+    `gamma`는 현재 보상을 넘는 미래 보상에 얼마나 우선 순위를 두어야 할지 나타내는 **discount factor**입니다.
+
+    `epsilon`는 exploitation보다 exploration을 선호하는지 안 하는지에 관해 결정하는 **exploration/exploitation factor**입니다. 알고리즘에서, `epsilon` 퍼센트의 케이스는 Q-Table 값에 따라서 다음 액션을 선택하고, 남는 케이스에서는 랜덤 액션을 수행할 예정입니다. 전에 본 적 없는 검색 공간의 영역을 탐색할 수 있습니다.
+
+    ✅ 밸런싱의 측면에서 - 랜덤 액션을 (exploration) 선택하는 것은 잘못된 방향으로 랜덤 펀치를 날릴 수 있으며, pole은 이 "실수"에서 밸런스를 복구하는 방식을 배우게 됩니다.
+
+### 알고리즘 개선
+
+이전 강의의 알고리즘을 두 번 개선할 수 있습니다:
+
+- 여러 번 시뮬레이션해서, **평균 누적 보상을 계산합니다**. 각자 5000번 반복하면서 프로세스를 출력하고, 이 시간 동안에 누적 보상을 계산합니다.  195 포인트보다 더 얻었다면 - 필수보다 더 높은 퀄리티로, 문제를 해결한 것으로 간주한다는 점을 의미합니다.
+
+- `Qmax`로, **최대 평균 누적 결과를 계산하고**, 이 결과와 일치하는 Q-Table에 저장합니다. 훈련을 할 때 가끔 평균 누적 결과가 드랍되기 시작하는 것을 알아챌 수 있고, 훈련 중 관찰된 좋은 모델에 해당하는 Q-Table의 값을 그대로 두고 싶습니다.
+
+1. plot을 진행하기 위해서 `rewards` vector로 각 시뮬레이션의 누적 보상을 얻을 수 있습니다. (code block  11)
+
+    ```python
+    def probs(v,eps=1e-4):
+        v = v-v.min()+eps
+        v = v/v.sum()
+        return v
+    
+    Qmax = 0
+    cum_rewards = []
+    rewards = []
+    for epoch in range(100000):
+        obs = env.reset()
+        done = False
+        cum_reward=0
+        # == do the simulation ==
+        while not done:
+            s = discretize(obs)
+            if random.random()<epsilon:
+                # exploitation - chose the action according to Q-Table probabilities
+                v = probs(np.array(qvalues(s)))
+                a = random.choices(actions,weights=v)[0]
+            else:
+                # exploration - randomly chose the action
+                a = np.random.randint(env.action_space.n)
+    
+            obs, rew, done, info = env.step(a)
+            cum_reward+=rew
+            ns = discretize(obs)
+            Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))
+        cum_rewards.append(cum_reward)
+        rewards.append(cum_reward)
+        # == Periodically print results and calculate average reward ==
+        if epoch%5000==0:
+            print(f"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}")
+            if np.average(cum_rewards) > Qmax:
+                Qmax = np.average(cum_rewards)
+                Qbest = Q
+            cum_rewards=[]
+    ```
+
+이 결과에서 알 수 있습니다:
+
+- **목표에 가까워집니다**. 시뮬레이션을 100+번 넘게 계속 하면 195개 누적 보상을 얻는 목표에 가까워지거나, 실제로 달성했을 수도 있습니다! 만약 작은 숫자를 얻더라도, 평균적으로 5000번 넘게 하고, 드러나있는 표준으로도 100번만 수행해도 되므로, 여전히 알 수 없습니다.
+  
+- **보상이 드랍되기 시작합니다**. 가끔 보상이 드랍되기 시작하면, 시뮬레이션을 안 좋게 만들어서 Q-Table에 이미 학습한 값을 "destroy"할 수 있다는 점을 의미합니다.
+
+이 관측치는 훈련 프로세스를 plot하면, 더 명확히 보입니다.
+
+## 훈련 프로세스 Plotting
+
+훈련 하면서, `rewards` vector로 각 반복에서 누적 보상 값을 얻었습니다. 여기에서 반복 숫자로 plot할 때 볼 수 았습니다:
+
+```python
+plt.plot(rewards)
+```
+
+![raw  progress](../images/train_progress_raw.png)
+
+그래프에서, 추계학 훈련 프로세스의 특성으로 훈련 세션 길이가 매우 달라지므로, 모든 것을 말할 수 없습니다. 이 그래프를 잘 이해하도록, 훈련 시리즈로 **running average**를 계산할 수 있습니다. 100으로 합시다. `np.convolve`으로 편하게 마무리 지을 수 있습니다: (code block 12)
+
+```python
+def running_average(x,window):
+    return np.convolve(x,np.ones(window)/window,mode='valid')
+
+plt.plot(running_average(rewards,100))
+```
+
+![training progress](../images/train_progress_runav.png)
+
+## 다양한 hyperparameters
+
+더 안정적으로 훈련하기 위해서, 훈련 중에 일부 hyperparameters를 조정하는 게 괜찮습니다. 이런 특징이 있습니다:
+
+- **For learning rate**, `alpha`는, 1에 근접한 값으로 시작하고, 파라이터를 계속 줄이게 됩니다. 시간이 지나고, Q-Table에서 좋은 확률 값을 얻으므로, 새로운 값으로 완전히 덮지않고, 살짝 조정하게 됩니다.
+
+- **Increase epsilon**. 덜 explore하고 더 exploit하려고, `epsilon`을 천천히 증가하기 원할 수 있습니다. `epsilon`의 낮은 값으로 시작하고, 1에 가까이 올리는 것이 괜찮습니다.
+
+> **Task 1**: hyperparameter 값으로 플레이하고 높은 누적 보상에 달성할 수 있는지 확인합니다. 195보다 더 얻을 수 있나요?
+
+> **Task 2**: 정식으로 문제를 해결하려면, 100번 연속으로 195개 평균 보상을 얻을 필요가 있습니다. 훈련하면서 측정하고 공식적으로 문제를 해결했는지 확인합니다!
+
+## 실행 결과 보기
+
+실제로 훈련된 모델이 어떻게 동작하는지 보는 것은 흥미롭습니다. 시뮬레이션을 돌리고 Q-Table 확률 분포에 따라서 샘플링된, 훈련을 하는 동안 같은 액션 선택 전략으로 따라갑니다: (code block 13)
+
+```python
+obs = env.reset()
+done = False
+while not done:
+   s = discretize(obs)
+   env.render()
+   v = probs(np.array(qvalues(s)))
+   a = random.choices(actions,weights=v)[0]
+   obs,_,done,_ = env.step(a)
+env.close()
+```
+
+이렇게 보입니다:
+
+![a balancing cartpole](../images/cartpole-balance.gif)
+
+---
+
+## 🚀 도전
+
+> **Task 3**: 여기, Q-Table의 최종 복사본을 사용하는 게, 최고가 아닐 수 있습니다. 최적-성능을 내는 Q-Table로 `Qbest` 변수를 저장했다는 점을 기억합니다! `Qbest`를 `Q`에 복사한 최적-성능을 내는 Q-Table로 같은 예시를 시도하고 다른 점을 파악합니다.
+
+> **Task 4**: 여기에는 각 단계에서 최상의 액션을 선택하지 않고, 일치하는 확률 분포로 샘플링했습니다. 가장 높은 Q-Table 값으로, 항상 최상의 액션을 선택하면 더 합리적인가요? `np.argmax` 함수로 높은 Q-Table 값에 해당되는 액션 숫자를 찾아서 마무리할 수 있습니다. 이 전략을 구현하고 밸런스를 개선했는지 봅니다.
+
+## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
+
+## 과제: [Train a Mountain Car](../assignment.md)
+
+## 결론
+
+지금부터 agent에 게임에서 원하는 state를 정의하는 보상 함수로 제공하고, 검색 공간을 지능적으로 탐색할 기회를 주며 좋은 결과로 도달하도록 어떻게 훈련하는지 배웠습니다. discrete적이고 연속 환경의 케이스에서 Q-Learning 알고리즘을 성공적으로 적용했지만, discrete적인 액션으로 했습니다.
+
+Atari 게임 스크린에서의 이미지처럼, 액션 상태 또한 연속적이고, 관찰 공간이 조금 더 복잡해지는 시뮬레이션을 공부하는 것도 중요합니다. 이 문제는 좋은 결과에 도달하기 위해서, neural networks처럼, 더 강한 머신러닝 기술을 자주 사용해야 합니다. 이러한 더 구체적인 토픽은 곧 오게 될 더 어려운 AI 코스의 주제입니다.
\ No newline at end of file
diff --git a/8-Reinforcement/2-Gym/translations/assignment.it.md b/8-Reinforcement/2-Gym/translations/assignment.it.md
new file mode 100644
index 000000000..63440ce65
--- /dev/null
+++ b/8-Reinforcement/2-Gym/translations/assignment.it.md
@@ -0,0 +1,44 @@
+# Addestrare un'Auto di Montagna
+
+[OpenAI Gym](http://gym.openai.com) è stato progettato in modo tale che tutti gli ambienti forniscano la stessa API, ovvero gli stessi metodi di `reset`, `step` e `render` e le stesse astrazioni dello **spazio di azione** e dello **spazio di osservazione**. Pertanto dovrebbe essere possibile adattare gli stessi algoritmi di reinforcement learning a diversi ambienti con modifiche minime al codice.
+
+## Un ambiente automobilistico di montagna
+
+[L'ambiente Mountain Car](https://gym.openai.com/envs/MountainCar-v0/) contiene un'auto bloccata in una valle:
+
+<img src="../images/mountaincar.png" width="300"/>
+
+L'obiettivo è uscire dalla valle e catturare la bandiera, compiendo ad ogni passaggio una delle seguenti azioni:
+
+| Valore | Significato |
+|---|---|
+| 0 | Accelerare a sinistra |
+| 1 | Non accelerare |
+| 2 | Accelerare a destra |
+
+Il trucco principale di questo problema è, tuttavia, che il motore dell'auto non è abbastanza forte per scalare la montagna in un solo passaggio. Pertanto, l'unico modo per avere successo è andare avanti e indietro per aumentare lo slancio.
+
+Lo spazio di osservazione è costituito da due soli valori:
+
+| Num | Osservazione | Min | Max |
+|-----|--------------|-----|-----|
+| 0 | Posizione dell'auto | -1,2 | 0.6 |
+| 1 | Velocità dell'auto | -0.07% | 0,07 |
+
+Il sistema di ricompensa per l'auto di montagna è piuttosto complicato:
+
+* La ricompensa di 0 viene assegnata se l'agente ha raggiunto la bandiera (posizione = 0,5) in cima alla montagna.
+* La ricompensa di -1 viene assegnata se la posizione dell'agente è inferiore a 0,5.
+
+L'episodio termina se la posizione dell'auto è maggiore di 0,5 o la durata dell'episodio è maggiore di 200.
+
+## Istruzioni
+
+Adattare l'algoritmo di reinforcement learning per risolvere il problema della macchina di montagna. Iniziare con il codice nel [notebook.ipynb](notebook.ipynb) esistente, sostituire il nuovo ambiente, modificare le funzioni di discretizzazione dello stato e provare ad addestrare l'algoritmo esistente con modifiche minime del codice. Ottimizzare il risultato regolando gli iperparametri.
+
+> **Nota**: è probabile che sia necessaria la regolazione degli iperparametri per far convergere l'algoritmo.
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------- | -------- | ----------------- |
+|          | L'algoritmo di Q-Learning è stato adattato con successo dall'esempio CartPole, con modifiche minime al codice, che è in grado di risolvere il problema di catturare la bandiera in meno di 200 passaggi. | Un nuovo algoritmo di Q-Learning è stato adottato da Internet, ma è ben documentato; oppure è stato adottato l'algoritmo esistente, ma non raggiunge i risultati desiderati | Lo studente non è stato in grado di adottare con successo alcun algoritmo, ma ha compiuto passi sostanziali verso la soluzione (discretizzazione dello stato implementata, struttura dati Q-Table, ecc.) |
diff --git a/8-Reinforcement/translations/README.it.md b/8-Reinforcement/translations/README.it.md
new file mode 100644
index 000000000..aa683dda8
--- /dev/null
+++ b/8-Reinforcement/translations/README.it.md
@@ -0,0 +1,53 @@
+# Introduzione al reinforcement learning
+
+Il reinforcement learning (apprendimento per rinforzo), RL, è visto come uno dei paradigmi di base di machine learning, accanto all'apprendimento supervisionato e all'apprendimento non supervisionato. RL è tutta una questione di decisioni: fornire le decisioni giuste o almeno imparare da esse.
+
+Si immagini di avere un ambiente simulato come il mercato azionario. Cosa succede se si impone un determinato regolamento. Ha un effetto positivo o negativo? Se accade qualcosa di negativo, si deve accettare questo _rinforzo negativo_, imparare da esso e cambiare rotta. Se è un risultato positivo, si deve costruire su quel _rinforzo positivo_.
+
+![Pierino e il lupo](../images/peter.png)
+
+> Pierino e i suoi amici devono sfuggire al lupo affamato! Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+## Tema regionale: Pierino e il lupo (Russia)
+
+[Pierino e il Lupo](https://it.wikipedia.org/wiki/Pierino_e_il_lupo) è una fiaba musicale scritta dal compositore russo [Sergei Prokofiev](https://it.wikipedia.org/wiki/Sergei_Prokofiev). È la storia del giovane pioniere Pierino, che coraggiosamente esce di casa per inseguire il lupo nella radura della foresta . In questa sezione, si addestreranno algoritmi di machine learning che aiuteranno Pierino a:
+
+- **Esplorare** l'area circostante e costruire una mappa di navigazione ottimale
+- **Imparare** a usare uno skateboard e bilanciarsi su di esso, per muoversi più velocemente.
+
+[![Pierino e il lupo](https://img.youtube.com/vi/Fmi5zHg4QSM/0.jpg)](https://www.youtube.com/watch?v=Fmi5zHg4QSM)
+
+> 🎥 Cliccare sull'immagine sopra per ascoltare Pierino e il Lupo di Prokofiev
+
+## Reinforcement learning
+
+Nelle sezioni precedenti, si sono visti due esempi di problemi di machine learning:
+
+- **Supervisionato**, dove si ha un insieme di dati che suggeriscono soluzioni campione al problema da risolvere. [La classificazione](../../4-Classification/translations/README.it.md) e la [regressione](../../2-Regression/translations/README.it.md) sono attività di apprendimento supervisionato.
+- **Non** supervisionato, in cui non si dispone di dati di allenamento etichettati. L'esempio principale di apprendimento non supervisionato è il [Clustering](../../5-Clustering/translations/README.it.md).
+
+In questa sezione, viene presentato un nuovo tipo di problemi di apprendimento che non richiede dati di addestramento etichettati. Esistono diversi tipi di tali problemi:
+
+- **[Apprendimento semi-supervisionato](https://wikipedia.org/wiki/Semi-supervised_learning)**, in cui si dispone di molti dati non etichettati che possono essere utilizzati per pre-addestrare il modello.
+- **[Apprendimento per rinforzo](https://it.wikipedia.org/wiki/Apprendimento_per_rinforzo)**, in cui un agente impara come comportarsi eseguendo esperimenti in un ambiente simulato.
+
+### Esempio: gioco per computer
+
+Si supponga di voler insegnare a un computer a giocare a un gioco, come gli scacchi o [Super Mario](https://it.wikipedia.org/wiki/Mario_(serie_di_videogiochi)). Affinché il computer possa giocare, occorre prevedere quale mossa fare in ciascuno degli stati di gioco. Anche se questo può sembrare un problema di classificazione, non lo è, perché non si dispone di un insieme di dati con stati e azioni corrispondenti. Sebbene si potrebbero avere alcuni dati come partite di scacchi esistenti o registrazioni di giocatori che giocano a Super Mario, è probabile che tali dati non coprano a sufficienza un numero adeguato di possibili stati.
+
+Invece di cercare dati di gioco esistenti, **Reinforcement Learning** (RL) si basa sull'idea di *far giocare il computer* molte volte e osservare il risultato. Quindi, per applicare il Reinforcement Learning, servono due cose:
+
+- **Un ambiente** e **un simulatore** che permettono di giocare molte volte un gioco. Questo simulatore definirebbe tutte le regole del gioco, nonché possibili stati e azioni.
+
+- **Una funzione di ricompensa**, che informi di quanto bene si è fatto durante ogni mossa o partita.
+
+La differenza principale tra altri tipi di machine learning e RL è che in RL in genere non si sa se si vince o si perde finchè non si finisce il gioco. Pertanto, non è possibile dire se una determinata mossa da sola sia buona o meno: si riceve una ricompensa solo alla fine del gioco. L'obiettivo è progettare algoritmi che consentano di addestrare un modello in condizioni incerte. Si imparerà a conoscere un algoritmo RL chiamato **Q-learning**.
+
+## Lezioni
+
+1. [Introduzione a reinforcement learning e al Q-Learning](../1-QLearning/translations/README.it.md)
+2. [Utilizzo di un ambiente di simulazione in palestra](../2-Gym/translations/README.it.md)
+
+## Crediti
+
+"Introduzione al Reinforcement Learning" è stato scritto con ♥️ da [Dmitry Soshnikov](http://soshnikov.com)
diff --git a/8-Reinforcement/translations/README.ko.md b/8-Reinforcement/translations/README.ko.md
new file mode 100644
index 000000000..e05411578
--- /dev/null
+++ b/8-Reinforcement/translations/README.ko.md
@@ -0,0 +1,54 @@
+# Reinforcement learning 소개하기
+
+Reinforcement learning, 즉 RL은, supervised learning 과 unsupervised learning 다음의, 기초 머신러닝 페러다임 중 하나로 봅니다. RL은 모든 의사결정입니다: 올바른 결정을 하거나 최소한 배우게 됩니다.
+
+주식 시장처럼 시뮬레이션된 환경을 상상해봅니다. 만약 규제시키면 어떤 일이 벌어질까요. 긍정적이거나 부정적인 영향을 주나요? 만약 부정적인 일이 생긴다면, 진로를 바꾸어, _negative reinforcement_ 을 배울 필요가 있습니다. 긍정적인 결과는, _positive reinforcement_ 로 만들 필요가 있습니다.
+
+![peter and the wolf](../images/peter.png)
+
+> Peter and his friends need to escape the hungry wolf! Image by [Jen Looper](https://twitter.com/jenlooper)
+
+## 지역 토픽: 피터와 늑대 (러시아)
+
+[Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf)는 러시아 작곡가 [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev)가 작성한 뮤지컬 동화입니다. 늑대를 쫓아내기 위해 용감하게 집 밖의 숲으로 떠나는, 젊은 개척가 피터의 이야기입니다. 이 섹션에서, Peter를 도와주기 위해서 머신러닝 알고리즘을 훈련해볼 예정입니다:
+
+- 주변 영역을 **탐색**하고 최적의 길을 안내하는 지도 만들기
+- 빨리 움직이기 위해서, 스케이트보드를 사용하고 밸런스잡는 방식을 **배우기**
+
+[![Peter and the Wolf](https://img.youtube.com/vi/Fmi5zHg4QSM/0.jpg)](https://www.youtube.com/watch?v=Fmi5zHg4QSM)
+
+> 🎥 Peter and the Wolf by Prokofiev를 들으려면 이미지 클릭
+
+## Reinforcement learning
+
+이전 섹션에서, 머신러닝 문제의 예시를 보았습니다:
+
+- **Supervised**, 해결하려는 문제에 대해서 예시 솔루션을 추천할 데이터셋이 있습니다. [Classification](../../4-Classification/translations/README.ko.md) 과 [regression](../../2-Regression/translations/README.ko.md)은 supervised learning 작업입니다.
+
+- **Unsupervised**, 라벨링된 훈련 데이터가 없습니다. unsupervised learning의 주요 예시는 [Clustering](../../5-Clustering/translations/README.ko.md)입니다.
+
+이 섹션에서, 라벨링된 훈련 데이터가 필요없는 학습 문제의 새로운 타입에 대하여 소개할 예정입니다. 여러 문제의 타입이 있습니다:
+
+- **[Semi-supervised learning](https://wikipedia.org/wiki/Semi-supervised_learning)**, 모델을 사전-훈련하며 사용할 수 있던 라벨링하지 않은 데이터를 많이 가지고 있습니다.
+- **[Reinforcement learning](https://wikipedia.org/wiki/Reinforcement_learning)**, 에이전트는 시뮬레이션된 환경에서 실험해서 행동하는 방식을 학습합니다.
+
+### 예시 - 컴퓨터 게임
+
+체스나, [Super Mario](https://wikipedia.org/wiki/Super_Mario) 같은, 게임 플레이를 컴퓨터에게 가르치고 싶다고 가정합니다. 컴퓨터가 게임을 플레이하려면, 게임 상태마다 어떻게 움직여야 되는지 예측할 필요가 있습니다. classification 문제처럼 보이겠지만, 아닙니다 - 상태와 일치하는 작업이 같이 있는 데이터셋은 없기 때문입니다. 기존 체스 경기 혹은 Super Mario를 즐기는 플레이어의 기록이 같은 소수의 데이터가 있을 수 있겠지만, 데이터가 가능한 상태의 충분히 많은 수를 커버할 수 없을 수 있습니다.
+
+기존 게임 데이터를 찾는 대신에, **Reinforcement Learning** (RL)은  매번 *making the computer play* 하고 결과를 지켜보는 아이디어가 기반됩니다.  그래서, Reinforcement Learning을 적용하면, 2가지가 필요합니다:              
+
+- 게임을 계속 플레이할 수 있는 **환경**과 **시뮬레이터**. 시뮬레이터는 모든 게임 규칙의 가능한 상태와 동작을 정의합니다.
+
+- **Reward function**, 각자 움직이거나 게임이 진행되면서 얼마나 잘 했는지 알려줍니다.
+
+다른 타입의 머신러닝과 RL 사이에 다른 주요 포인트는 RL에서 일반적으로 게임을 끝내기 전에 이기거나 지는 것을 알 수 없다는 점입니다. 그래서, 특정 동작이 좋을지 나쁠지 말할 수 없습니다 - 오직 게임의 끝에서 보상을 받습니다. 그리고 목표는 불확실 조건에서 모델을 훈련할 알고리즘을 만드는 것입니다. **Q-learning**이라고 불리는 RL 알고리즘에 대하여 배울 예정입니다.
+
+## 강의
+
+1. [Reinforcement learning과 Q-Learning 소개하기](../1-QLearning/translations/EADME.ko.md)
+2. [헬스장 시뮬레이션 환경 사용하기](../2-Gym/translations/README.ko.md)
+
+## 크레딧
+
+"Introduction to Reinforcement Learning" was written with ♥️ by [Dmitry Soshnikov](http://soshnikov.com)
diff --git a/8-Reinforcement/translations/README.zh-cn.md b/8-Reinforcement/translations/README.zh-cn.md
new file mode 100644
index 000000000..28973b2a4
--- /dev/null
+++ b/8-Reinforcement/translations/README.zh-cn.md
@@ -0,0 +1,53 @@
+# 强化学习简介
+
+强化学习 (RL, Reinforcement Learning)，是基本的机器学习范式之一（仅次于监督学习 (Supervised Learning) 和无监督学习(Unsupervised Learning)）。强化学习和「策略」息息相关：它应当产生正确的策略，或从错误的策略中学习。
+
+假设有一个模拟环境，比如说股市。当我们用某一个规则来限制这个市场时，会发生什么？这个规则（或者说策略）有积极或消极的影响吗？如果它的影响是正面的，我们需要从这种_负面强化_中学习，改变我们的策略。如果它的影响是正面的，我们需要在这种_积极强化_的基础上再进一步发展。
+
+![彼得和狼](../images/peter.png)
+
+> 彼得和他的朋友们得从饥饿的狼这儿逃掉！图片来自 [Jen Looper](https://twitter.com/jenlooper)
+
+## 本节主题：彼得与狼（俄罗斯）
+
+[彼得与狼](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) 是俄罗斯作曲家[谢尔盖·普罗科菲耶夫](https://en.wikipedia.org/wiki/Sergei_Prokofiev)创作的音乐童话。它讲述了彼得勇敢地走出家门，到森林中央追逐狼的故事。在本节中，我们将训练帮助彼得追狼的机器学习算法：
+
+- **探索**周边区域并构建最佳地图
+- **学习**如何使用滑板并在滑板上保持平衡，以便更快地移动。
+
+[![彼得和狼](https://img.youtube.com/vi/Fmi5zHg4QSM/0.jpg)](https://www.youtube.com/watch?v=Fmi5zHg4QSM)
+
+> 🎥 点击上面的图片，听普罗科菲耶夫的《彼得与狼》
+
+## 强化学习
+
+在前面的部分中，您已经看到了两类机器学习问题的例子：
+
+- **监督**，在有已经标记的，暗含解决方案的数据集的情况下。 [分类](../../4-Classification/translations/README.zh-cn.md) 和 [回归](../../2-Regression/translations/README.zh-cn.md) 是监督学习任务。
+- **无监督**，在我们没有标记训练数据集的情况下。无监督学习的主要例子是 [聚类](../../5-Clustering/translations/README.zh-cn.md)。
+
+在本节中，我们将学习一类新的机器学习问题，它不需要已经标记的训练数据 —— 比如这两类问题：
+
+- **[半监督学习](https://wikipedia.org/wiki/Semi-supervised_learning)**，在我们有很多未标记的、可以用来预训练模型的数据的情况下。
+- **[强化学习](https://wikipedia.org/wiki/Reinforcement_learning)**，在这种方法中，机器通过在某种模拟环境中进行实验来学习最佳策略。
+
+### 示例 - 电脑游戏
+
+假设我们要教会计算机玩某一款游戏 —— 例如国际象棋，或者 [超级马里奥](https://wikipedia.org/wiki/Super_Mario)。为了让计算机学会玩游戏，我们需要它预测在每个游戏「状态」下，它应该做什么「操作」。虽然这看起来像是一个分类问题，但事实并非如此，因为我们并没有像这样的，包含「状态」和状态对应的「操作」的数据集。我们只有一些有限的数据，比如来自国际象棋比赛的记录，或者是玩家玩超级马里奥的记录。这些数据可能无法涵盖足够多的「状态」。
+
+不同于这种需要大量现有的数据的方法，**强化学习**是基于*让计算机多次玩*并观察玩的结果的想法。因此，要使用强化学习方法，我们需要两个要素：
+
+- **环境**和**模拟器**，它们允许我们多次玩游戏。该模拟器应该定义所有游戏规则，以及可能的状态和动作。
+
+- **奖励函数**，它会告诉我们每个每一步（或者每局游戏）的表现如何。
+
+其他类型的机器学习和强化学习 (RL) 之间的主要区别在于，在 RL 中，我们通常在完成游戏之前，都不知道我们是赢还是输。因此，我们不能说单独的某个动作是不是「好」的 - 我们只会在游戏结束时获得奖励。我们的目标是设计算法，使我们能够在这种不确定的条件下训练模型。我们将了解一种称为 **Q-learning** 的 RL 算法。
+
+## 课程
+
+1. [强化学习和 Q-Learning 介绍](1-QLearning/README.md)
+2. [使用 Gym 模拟环境](2-Gym/README.md)
+
+## 本文作者
+
+“强化学习简介” 由 [Dmitry Soshnikov](http://soshnikov.com) 用 ♥️ 编写
diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md
index 154c57d6d..b06b8b716 100644
--- a/9-Real-World/1-Applications/README.md
+++ b/9-Real-World/1-Applications/README.md
@@ -8,7 +8,7 @@ In this curriculum, you have learned many ways to prepare data for training and
 
 While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/49/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/)
 
 ## 💰 Finance
 
@@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
 
 Identify another sector that benefits from some of the techniques you learned in this curriculum, and discover how it uses ML.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/50/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/)
 
 ## Review & Self Study
 
diff --git a/9-Real-World/1-Applications/translations/README.it.md b/9-Real-World/1-Applications/translations/README.it.md
new file mode 100644
index 000000000..edf844738
--- /dev/null
+++ b/9-Real-World/1-Applications/translations/README.it.md
@@ -0,0 +1,162 @@
+# Poscritto: Machine learning nel mondo reale
+
+![Riepilogo di machine learning nel mondo reale in uno sketchnote](../../../sketchnotes/ml-realworld.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+In questo programma di studi si sono appresi molti modi per preparare i dati per l'addestramento e creare modelli di machine learning. Sono stati creati una serie di modelli classici di regressione, clustering, classificazione, elaborazione del linguaggio naturale e serie temporali. Congratulazioni! Ora, se ci si sta chiedendo a cosa serva tutto questo... quali sono le applicazioni del mondo reale per questi modelli?
+
+Sebbene l'intelligenza artificiale abbia suscitato molto interesse nell'industria, che di solito sfrutta il deep learning, esistono ancora preziose applicazioni per i modelli classici di machine learning. Si potrebbero anche usare alcune di queste applicazioni oggi! In questa lezione, si esplorerà come otto diversi settori e campi relativi all'argomento utilizzano questi tipi di modelli per rendere le loro applicazioni più performanti, affidabili, intelligenti e preziose per gli utenti.
+
+## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/?loc=it)
+
+## Finanza
+
+Il settore finanziario offre molte opportunità per machine learning. Molti problemi in quest'area si prestano ad essere modellati e risolti utilizzando machine learning.
+
+### Rilevamento frodi con carta di credito
+
+Si è appreso del [clustering k-means](../../../5-Clustering/2-K-Means/translations/README.it.md) in precedenza nel corso, ma come può essere utilizzato per risolvere i problemi relativi alle frodi con carta di credito?
+
+Il clustering K-means è utile con una tecnica di rilevamento delle frodi con carta di credito chiamata **rilevamento dei valori anomali**. I valori anomali, o le deviazioni nelle osservazioni su un insieme di dati, possono svelare se una carta di credito viene utilizzata normalmente o se sta succedendo qualcosa di insolito. Come mostrato nel documento collegato di seguito, è possibile ordinare i dati della carta di credito utilizzando un algoritmo di clustering k-means e assegnare ogni transazione a un cluster in base a quanto sembra essere un valore anomalo. Quindi, si possono valutare i cluster più rischiosi per le transazioni fraudolente rispetto a quelle legittime.
+
+https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf
+
+### Gestione patrimoniale
+
+Nella gestione patrimoniale, un individuo o un'impresa gestisce gli investimenti per conto dei propri clienti. Il loro compito è sostenere e far crescere la ricchezza a lungo termine, quindi è essenziale scegliere investimenti che funzionino bene.
+
+Un modo per valutare le prestazioni di un particolare investimento è attraverso la regressione statistica. La[regressione lineare](../../../2-Regression/1-Tools/translations/README.it.md) è uno strumento prezioso per capire come si comporta un fondo rispetto a un benchmark. Si può anche dedurre se i risultati della regressione sono statisticamente significativi o quanto influenzerebbero gli investimenti di un cliente. Si potrebbe anche espandere ulteriormente la propria analisi utilizzando la regressione multipla, in cui è possibile prendere in considerazione ulteriori fattori di rischio. Per un esempio di come funzionerebbe per un fondo specifico, consultare il documento di seguito sulla valutazione delle prestazioni del fondo utilizzando la regressione.
+
+http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/
+
+## Istruzione
+
+Anche il settore educativo è un'area molto interessante in cui si può applicare machine learning. Ci sono problemi interessanti da affrontare come rilevare l'imbroglio nei test o nei saggi o gestire i pregiudizi nel processo di correzione, non intenzionali o meno.
+
+### Prevedere il comportamento degli studenti
+
+[Coursera](https://coursera.com), un fornitore di corsi aperti online, ha un fantastico blog di tecnologia in cui discutono molte decisioni ingegneristiche. In questo caso di studio, hanno tracciato una linea di regressione per cercare di esplorare qualsiasi correlazione tra un punteggio NPS (Net Promoter Score) basso e il mantenimento o l'abbandono del corso.
+
+https://medium.com/coursera-engineering/regressione-controllata-quantificare-l'impatto-della-qualità-del-corso-sulla-ritenzione-dell'allievo-31f956bd592a
+
+### Mitigare i pregiudizi
+
+[Grammarly](https://grammarly.com), un assistente di scrittura che controlla gli errori di ortografia e grammatica, utilizza sofisticati [sistemi di elaborazione del linguaggio naturale](../../../6-NLP/translations/README.it.md) in tutti i suoi prodotti. Hanno pubblicato un interessante caso di studio nel loro blog tecnologico su come hanno affrontato il pregiudizio di genere nell'apprendimento automatico, di cui si si è appreso nella [lezione introduttiva sull'equità](../../../1-Introduction/3-fairness/translations/README.it.md).
+
+https://www.grammarly.com/blog/engineering/mitigating-gender-bias-in-autocorrect/
+
+## Vendita al dettaglio
+
+Il settore della vendita al dettaglio può sicuramente trarre vantaggio dall'uso di machine learning, dalla creazione di un percorso migliore per il cliente allo stoccaggio dell'inventario in modo ottimale.
+
+### Personalizzare il percorso del cliente
+
+In Wayfair, un'azienda che vende articoli per la casa come i mobili, aiutare i clienti a trovare i prodotti giusti per i loro gusti e le loro esigenze è fondamentale. In questo articolo, gli ingegneri dell'azienda descrivono come utilizzano ML e NLP per "far emergere i risultati giusti per i clienti". In particolare, il loro motore di intento di ricerca è stato creato per utilizzare l'estrazione di entità, l'addestramento di classificatori, l'estrazione di risorse e opinioni e l'etichettatura del sentimento sulle recensioni dei clienti. Questo è un classico caso d'uso di come funziona NLP nella vendita al dettaglio online.
+
+https://www.aboutwayfair.com/tech-innovation/how-we-use-machine-learning-and-natural-language-processing-to-empower-search
+
+### Gestione dell’inventario
+
+Aziende innovative e agili come [StitchFix](https://stitchfix.com), un servizio che spedisce abbigliamento ai consumatori, si affidano molto al machine learning per consigli e gestione dell'inventario. I loro team di stilisti lavorano insieme ai loro team di merchandising, infatti: "uno dei nostri data scientist ha armeggiato con un algoritmo genetico e lo ha applicato all'abbigliamento per prevedere quale sarebbe un capo di abbigliamento di successo che oggi non esiste. L'abbiamo portato al team del merchandising e ora possono usarlo come strumento".
+
+https://www.zdnet.com/article/how-stitch-fix-uses-machine-learning-to-master-the-science-of-styling/
+
+## Assistenza sanitaria
+
+Il settore sanitario può sfruttare il machine learning per ottimizzare le attività di ricerca e anche problemi logistici come la riammissione dei pazienti o l'arresto della diffusione delle malattie.
+
+### Gestione delle sperimentazioni cliniche
+
+La tossicità negli studi clinici è una delle principali preoccupazioni per i produttori di farmaci. Quanta tossicità è tollerabile? In questo studio, l'analisi di vari metodi di sperimentazione clinica ha portato allo sviluppo di un nuovo approccio per prevedere le probabilità dei risultati della sperimentazione clinica. Nello specifico, sono stati in grado di usare random forest per produrre un [classificatore](../../../4-Classification/translations/README.it.md) in grado di distinguere tra gruppi di farmaci.
+
+https://www.sciencedirect.com/science/article/pii/S2451945616302914
+
+### Gestione della riammissione ospedaliera
+
+Le cure ospedaliere sono costose, soprattutto quando i pazienti devono essere ricoverati di nuovo. Questo documento discute un'azienda che utilizza il machine learning per prevedere il potenziale di riammissione utilizzando algoritmi di [clustering](../../../5-Clustering/translations/README.it.md). Questi cluster aiutano gli analisti a "scoprire gruppi di riammissioni che possono condividere una causa comune".
+
+https://healthmanagement.org/c/healthmanagement/issuearticle/hospital-readmissions-and-machine-learning
+
+### Gestione della malattia
+
+La recente pandemia ha messo in luce i modi in cui machine learning può aiutare a fermare la diffusione della malattia. In questo articolo, si riconoscerà l'uso di ARIMA, curve logistiche, regressione lineare e SARIMA. "Questo lavoro è un tentativo di calcolare il tasso di diffusione di questo virus e quindi di prevedere morti, guarigioni e casi confermati, in modo che possa aiutare a prepararci meglio e sopravvivere".
+
+https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979218/
+
+## 🌲 Ecologia e Green Tech
+
+Natura ed ecologia sono costituiti da molti sistemi sensibili in cui l'interazione tra animali e natura viene messa a fuoco. È importante essere in grado di misurare accuratamente questi sistemi e agire in modo appropriato se accade qualcosa, come un incendio boschivo o un calo della popolazione animale.
+
+### Gestione delle foreste
+
+Si è appreso il [Reinforcement Learning](../../../8-Reinforcement/translations/README.it.md) nelle lezioni precedenti. Può essere molto utile quando si cerca di prevedere i modelli in natura. In particolare, può essere utilizzato per monitorare problemi ecologici come gli incendi boschivi e la diffusione di specie invasive. In Canada, un gruppo di ricercatori ha utilizzato Reinforcement Learning per costruire modelli di dinamica degli incendi boschivi da immagini satellitari. Utilizzando un innovativo "processo di diffusione spaziale (SSP)", hanno immaginato un incendio boschivo come "l'agente in qualsiasi cellula del paesaggio". "L'insieme di azioni che l'incendio può intraprendere da un luogo in qualsiasi momento include la diffusione a nord, sud, est o ovest o la mancata diffusione.
+
+Questo approccio inverte la solita configurazione RL poiché la dinamica del corrispondente Processo Decisionale di Markov (MDP) è una funzione nota per la diffusione immediata degli incendi". Maggiori informazioni sugli algoritmi classici utilizzati da questo gruppo al link sottostante.
+
+https://www.frontiersin.org/articles/10.3389/fneur.2018.00006/pieno
+
+### Rilevamento del movimento degli animali
+
+Mentre il deep learning ha creato una rivoluzione nel tracciamento visivo dei movimenti degli animali (qui si può costruire il proprio [localizzatore di orsi polari](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa) ), il machine learning classico ha ancora un posto in questo compito.
+
+I sensori per tracciare i movimenti degli animali da fattoria e l'internet delle cose fanno uso di questo tipo di elaborazione visiva, ma tecniche di machine learning di base sono utili per preelaborare i dati. Ad esempio, in questo documento, le posture delle pecore sono state monitorate e analizzate utilizzando vari algoritmi di classificazione. Si potrebbe riconoscere la curva ROC a pagina 335.
+
+https://druckhaus-hofmann.de/gallery/31-wj-feb-2020.pdf
+
+### Gestione energetica
+
+Nelle lezioni sulla [previsione delle serie temporali](../../../7-TimeSeries/translations/README.it.md), si è invocato il concetto di parchimetri intelligenti per generare entrate per una città in base alla comprensione della domanda e dell'offerta. Questo articolo discute in dettaglio come il raggruppamento, la regressione e la previsione delle serie temporali si sono combinati per aiutare a prevedere il futuro uso di energia in Irlanda, sulla base della misurazione intelligente.
+
+https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf
+
+## Assicurazione
+
+Il settore assicurativo è un altro settore che utilizza machine learning per costruire e ottimizzare modelli finanziari e attuariali sostenibili.
+
+### Gestione della volatilità
+
+MetLife, un fornitore di assicurazioni sulla vita, è disponibile con il modo in cui analizzano e mitigano la volatilità nei loro modelli finanziari. In questo articolo si noteranno le visualizzazioni di classificazione binaria e ordinale. Si scopriranno anche visualizzazioni di previsione.
+
+https://investments.metlife.com/content/dam/metlifecom/us/investments/insights/research-topics/macro-strategy/pdf/MetLifeInvestmentManagement_MachineLearnedRanking_070920.pdf
+
+## 🎨 Arte, cultura e letteratura
+
+Nelle arti, per esempio nel giornalismo, ci sono molti problemi interessanti. Rilevare notizie false è un problema enorme poiché è stato dimostrato che influenza l'opinione delle persone e persino che fa cadere le democrazie. I musei possono anche trarre vantaggio dall'utilizzo di machine learning in tutto, dalla ricerca di collegamenti tra gli artefatti alla pianificazione delle risorse.
+
+### Rilevamento di notizie false
+
+Rilevare notizie false è diventato un gioco del gatto e del topo nei media di oggi. In questo articolo, i ricercatori suggeriscono che un sistema che combina diverse delle tecniche ML qui studiate può essere testato e il miglior modello implementato: "Questo sistema si basa sull'elaborazione del linguaggio naturale per estrarre funzionalità dai dati e quindi queste funzionalità vengono utilizzate per l'addestramento di classificatori di machine learning come Naive Bayes, Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD) e Logistic Regression (LR)."
+
+https://www.irjet.net/archives/V7/i6/IRJET-V7I6688.pdf
+
+Questo articolo mostra come la combinazione di diversi campi ML possa produrre risultati interessanti in grado di aiutare a impedire che le notizie false si diffondano e creino danni reali; in questo caso, l'impulso è stato la diffusione di voci su trattamenti COVID che incitavano alla violenza di massa.
+
+### ML per Musei
+
+I musei sono all'apice di una rivoluzione dell'intelligenza artificiale in cui catalogare e digitalizzare le collezioni e trovare collegamenti tra i manufatti sta diventando più facile con l'avanzare della tecnologia. Progetti come [In Codice Ratio](https://www.sciencedirect.com/science/article/abs/pii/S0306457321001035#:~:text=1.,studies%20over%20large%20historical%20sources.) stanno aiutando a svelare i misteri di collezioni inaccessibili come gli Archivi Vaticani. Ma anche l'aspetto commerciale dei musei beneficia dei modelli di machine learning.
+
+Ad esempio, l'Art Institute di Chicago ha costruito modelli per prevedere a cosa è interessato il pubblico e quando parteciperà alle esposizioni. L'obiettivo è creare esperienze di visita personalizzate e ottimizzate ogni volta che l'utente visita il museo. "Durante l'anno fiscale 2017, il modello ha previsto presenze e ammissioni entro l'1% di scostamento, afferma Andrew Simnick, vicepresidente senior dell'Art Institute".
+
+https://www.chicagobusiness.com/article/20180518/ISSUE01/180519840/art-institute-of-chicago-uses-data-to-make-exhibit-choices
+
+## Marketing
+
+### Segmentazione della clientela
+
+Le strategie di marketing più efficaci si rivolgono ai clienti in modi diversi in base a vari raggruppamenti. In questo articolo vengono discussi gli usi degli algoritmi di Clustering per supportare il marketing differenziato. Il marketing differenziato aiuta le aziende a migliorare il riconoscimento del marchio, raggiungere più clienti e guadagnare di più.
+
+https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
+
+## 🚀 Sfida
+
+Identificare un altro settore che beneficia di alcune delle tecniche apprese in questo programma di studi e scoprire come utilizza il machine learning.
+
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/?loc=it)
+
+## Revisione e Auto Apprendimento
+
+Il team di data science di Wayfair ha diversi video interessanti su come usano il machine learning nella loro azienda. Vale la pena [dare un'occhiata](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)!
+
+## Compito
+
+[Una caccia al tesoro per ML](assignment.it.md)
diff --git a/9-Real-World/1-Applications/translations/README.ko.md b/9-Real-World/1-Applications/translations/README.ko.md
new file mode 100644
index 000000000..62c09752c
--- /dev/null
+++ b/9-Real-World/1-Applications/translations/README.ko.md
@@ -0,0 +1,163 @@
+# 추신: 현실의 머신러닝
+
+
+![Summary of Machine learning in the real world in a sketchnote](../../../sketchnotes/ml-realworld.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+이 커리큘럼에서, 훈련하기 위한 데이터를 준비하고 머신러닝 모델으로 만드는 다양한 방식을 배웠습니다. 일렬의 classic regression, clustering, classification, natural language processing과, time series 모델을 만들었습니다. 축하드립니다! 지금부터, 모두 어떤 것을 고려했는지 궁금할 수 있습니다... 이 모델로 어떤 현실 어플리케이션을 만들었나요?
+
+보통 딥러닝을 활용하는, AI로 산업에 많은 관심이 모이지만, 여전히 classical 머신러닝 모델의 가치있는 애플리케이션도 존재합니다. 오늘 이 애플리케이션 일부를 사용할 수도 있습니다! 이 강의에서, 8개 다양한 산업과 subject-matter 도메인에서 이 모델 타입으로 애플리케이션의 성능, 신뢰, 지능과, 사용자 가치를 어떻게 더 높일지 탐색할 예정입니다.  
+
+## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/)
+
+## 💰 금융
+
+금융 섹터는 머신러닝을 위해 많은 기회를 제공합니다. 이 영역의 많은 문제는 ML로 모델링하고 풀면서 할 수 있습니다.
+
+### 신용카드 사기 감지
+
+코스 초반에 [k-means clustering](../../../5-Clustering/2-K-Means/README.md)에 대하여 배웠지만, 신용카드 사기와 관련된 문제를 풀려면 어떻게 사용할 수 있을까요?
+
+K-means clustering은 **outlier detection**이라고 불리는 신용카드 사기 탐지 기술 중에 능숙합니다. 데이터셋에 대한 관찰의 아웃라이어 또는, 표준 편차는, 신용카드가 일반적인 수용량으로 사용되거나 평범하지 않은 일이 일어나는지 알려줄 수 있습니다. 다음 링크된 논문에서 본 것처럼, k-means clustering 알고리즘으로 신용카드를 정렬하고 아웃라이어가 얼마나 나오는지 기반해서 트랜잭션을 클러스터에 할당할 수 있습니다. 그러면, 합법적인 트랜잭션과 대비해서 사기 행위인 위험한 클러스터를 평가할 수 있습니다.
+
+https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf
+
+### 재산 관리
+
+재산 관리에서, 개인 혹은 공사는 클라이언트를 대신해서 투자합니다. 이 일은 오래 재산을 유지하고 증식시키기 위해서, 효율이 나는 투자를 선택하는 게 필수적입니다.
+
+투자 효율을 평가하는 방식 중 하나는 statistical regression을 통하는 것입니다. [Linear regression](../../../2-Regression/1-Tools/README.md)은 일부 벤치마크와 비교해서 펀드 효율을 이해하는데 가치있는 도구입니다. 또한 regression의 결과가 통게적으로 의미가 있는지, 클라이언트의 투자에 영향을 받을지 따질 수 있습니다. 추가적인 리스크를 고려할 수 있을 때, multiple regression으로 분석을 더욱 더 확장할 수 있습니다. 특정 펀드가 어덯게 동작하는지에 대한 예시로, regression으로 펀드 효율을 평가하는 다음 논문을 확인합니다.
+
+http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/
+
+## 🎓 교육
+
+교육 섹터도 ML이 적용되었을 때 매우 흥미롭습니다. 테스트나 에세이에서 치팅을 감지하거나 의도와 상관없는 편견을 정정하는 프로세스 관리처럼, 다루어야 할 흥미로운 문제입니다.
+
+### 학생 행동 예측
+
+[Coursera](https://coursera.com)라는, 온라인 오픈코스 제공자는, 많은 엔지니어링 결정을 논의하는 훌륭한 기술 블로그입니다. 이 연구 케이스에서, 낮은 NPS (Net Promoter Score) 점수와 코스를 유지하거나 하차하는 사이의 모든 상관 관계를 탐색하려는 regression 라인을 plot합니다.
+
+https://medium.com/coursera-engineering/controlled-regression-quantifying-the-impact-of-course-quality-on-learner-retention-31f956bd592a
+
+### 편견 완화
+
+[Grammarly](https://grammarly.com)는, 맞춤법과 문법 오류를 확인하고, 프로덕트 전반적으로 복잡한 [natural language processing systems](../../../6-NLP/README.md)으로, 작문을 돕습니다. [introductory fairness lesson](../../../1-Introduction/3-fairness/README.md)에서 배운, 머신러닝의 gender bias를 다루는 방식을 기술 블로그에 흥미로운 케이스 스터디로 배포했습니다.
+
+https://www.grammarly.com/blog/engineering/mitigating-gender-bias-in-autocorrect/
+
+## 👜 리테일
+
+리테일 섹터는 더 좋은 고객 기록을 만들고 최적의 방식으로 재고를 모으는 모든 것에 대하여, ML로 명확하게 이익을 낼 수 있습니다.
+
+### 고객 기록 개인화
+
+가구같은 가정용품을 파는 회사인, Wayfair에서는, 고객이 취향과 니즈에 맞는 제품을 찾도록 돕는 게 최고입니다. 이 아티클에서, 회사의 엔지니어들은 ML과 NLP로 "surface the right results for customers"하는 방식을 설명합니다. 특히나, Query Intent 엔진은 엔티티 추출, classifier 훈련, 자산과 의견 추출, 그리고 고객 리뷰에서 감정 태그까지 사용하도록 만들어졌습니다. 온라인 리테일에서 NLP가 어떻게 작동하는가에 대한 고전적인 사용 방식입니다.
+
+https://www.aboutwayfair.com/tech-innovation/how-we-use-machine-learning-and-natural-language-processing-to-empower-search
+
+### 재고 관리
+
+소비자에게 의상을 배달해주는 박스 서비스인, [StitchFix](https://stitchfix.com)처럼 혁신적이고, 재빠른 회사는, 추천과 재고 관리를 ML에 많이 의존합니다. styling 팀은 merchandising 팀과 같이 일합니다, 사실은 이렇습니다: "one of our data scientists tinkered with a genetic algorithm and applied it to apparel to predict what would be a successful piece of clothing that doesn't exist today. We brought that to the merchandise team and now they can use that as a tool."
+
+https://www.zdnet.com/article/how-stitch-fix-uses-machine-learning-to-master-the-science-of-styling/
+
+## 🏥 의료
+
+의료 섹션은 ML을 활용해서 연구 작업과 재입원 환자 또는 질병 확산 방지처럼 logistic 문제를 최적화할 수 있습니다.
+
+### 임상실험 관리
+
+임상 실험에서 독성은 제약사의 주요 관심사입니다. 알미니 많은 독성을 참을 수 있을까요? 연구에서는, 다양한 임상 시도 방식을 분석해서 임상 시도 결과의 확률을 예측하는 새로운 접근 방식이 개발되었습니다. 특히나, random forest로 약물 그룹 사이에서 식별할 수 있는 [classifier](../../../4-Classification/README.md)도 만들 수 있었습니다.
+
+https://www.sciencedirect.com/science/article/pii/S2451945616302914
+
+### 병원 재입원 관리
+
+병원 치료는 특히나, 환자가 다시 입원해야 될 때 손실이 큽니다. 이 논문은 [clustering](../../../5-Clustering/README.md) 알고리즘으로 다시 입원할 가능성을 예측하는 ML로 사용하고 있는 회사를 설명합니다. 이 클러스터는 분석가가 "discover groups of readmissions that may share a common cause"하는 것이 도움됩니다.
+
+https://healthmanagement.org/c/healthmanagement/issuearticle/hospital-readmissions-and-machine-learning
+
+### 질병 관리
+
+최근 팬데믹은 머신러닝이 질병 확산을 막을 수 있게 도와주는 방식에서 희망찬 미래를 보여주었습니다. 이 아티클에서, ARIMA, ARIMA, logistic curves, linear regression과, SARIMA의 사용법을 알게 됩니다. "This work is an attempt to calculate the rate of spread of this virus and thus to predict the deaths, recoveries, and confirmed cases, so that it may help us to prepare better and survive."
+
+https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979218/
+
+## 🌲 생태학과 환경 기술
+
+자연과 생태학은 동물과 자연 사이의 상호 작용으로 초점을 맞추어진 많이 민감한 시스템으로 이루어져 있습니다. 이 시스템을 정확히 측정하고 산불이 나거나 동물 개체군이 줄어드는 것처럼, 일이 생기면 적절하게 행동하는 게 중요합니다.
+
+### 숲 관리
+
+이전 강의에서 [Reinforcement Learning](../../../8-Reinforcement/README.md)에 대하여 배웠습니다. 자연에서 패턴을 예측 시도하면 매우 유용할 수 있습니다. 특히나, 산불이 나고 외래종의 확산처럼 생태학적 문제를 추적할 때 사용할 수 있습니다. Canada에서, 연구원 그룹은 Reinforcement Learning으로 위성 이미지에서 산불 역학 모델을 만들었습니다. 혁신적인 "spatially spreading process (SSP)"을 사용해서, "the agent at any cell in the landscape."로 산불을 상상했습니다. "The set of actions the fire can take from a location at any point in time includes spreading north, south, east, or west or not spreading.
+
+This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP) is a known function for immediate wildfire spread." 다음 링크에서 그룹이 사용했던 classic 알고리즘에 대하여 더 읽어봅니다.
+
+https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full
+
+### 동물의 움직임 감지
+
+딥러닝이 동물 움직임을 시각적으로-추적하려고 혁신적으로 만들었지만 (여기에 [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa)를 만들 수 있습니다), classic ML은 여전히 이 작업에서 자리를 차지하고 있습니다.
+
+농장 동물의 움직임을 추적하는 센서와 이 비주얼 프로세싱 타입을 사용하는 IoT도 있지만, 더 기본적인 ML 기술은 데이터를 전처리할 때 유용합니다. 예시로, 논문에서, 다양한 classifier 알고리즘으로 양의 상태를 모니터링하고 분석했습니다. 335 페이지에서 ROC curve를 알게 될 수 있습니다.
+
+https://druckhaus-hofmann.de/gallery/31-wj-feb-2020.pdf
+
+### ⚡️ 에너지 관리
+  
+[time series forecasting](../../../7-TimeSeries/README.md)의 강의에서, 수요와 공급의 이해를 기반해서 마을 수익을 발생시키기 위한 smart parking meter의 컨셉을 불렀습니다. 이 아티클에서 clustering, regression과 time series forecasting이 합쳐진, smart metering을 기반으로, Ireland의 미래 애너지 사용량 예측을 어떻게 도와줄 수 있는지 자세히 이야기 합니다.
+
+https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf
+
+## 💼 보험
+
+보험 섹터는 ML로 수행 가능한 재무와 보험계리학 모델을 구성하고 최적화하는 또 다른 섹터입니다.
+
+### 변동성 관리
+
+생명 보험 제공자인, MetLife는, 재무 모델에서 변동성을 분석하고 완화하는 방식을 곧 보입니다. 이 아티클에서 binary와 ordinal classification 시각화를 파악할 수 있습니다. 또한 예측 시각화도 찾을 수 있습니다.
+
+https://investments.metlife.com/content/dam/metlifecom/us/investments/insights/research-topics/macro-strategy/pdf/MetLifeInvestmentManagement_MachineLearnedRanking_070920.pdf
+
+## 🎨 예술, 문화, 그리고 문학
+
+예술에서, 저널리즘을 예시로 들자면, 많이 흥미로운 문제입니다. 가짜 뉴스를 감지하는 것은 사람들의 여론과 관계가 있고 민주주의를 흔든다고 입증되었기 때문에 큰 문제입니다. 박물관도 유물 사이 연결고리를 찾는 것부터 자원 관리까지 모든 것에 ML로 이익을 낼 수 있습니다.
+
+### 가짜 뉴스 감지
+
+가짜 뉴스를 감지하는 것은 오늘의 미디어에서 고양이와 쥐의 게임으로 되어있습니다. 이 아티클에서, 원구원들은 연구했던 여러 ML 기술을 합친 시스템을 테스트하고 최적의 모델을 배포할 수 있다고 제안했습니다: "This system is based on natural language processing to extract features from the data and then these features are used for the training of machine learning classifiers such as Naive Bayes,  Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD), and Logistic Regression(LR)."
+
+https://www.irjet.net/archives/V7/i6/IRJET-V7I6688.pdf
+
+이 아티클에서 서로 다른 ML 도메인을 합쳐서 가짜 뉴스의 확산과 실제 데미지를 막는 데 도울 수 있는 흥미로운 결과를 어떻게 얻을 수 있는지 보여줍니다; 이 케이스에서, 핵심은 집단 폭력을 유도했던 COVID 치료 방식에 대한 루머가 확산된 것입니다.
+
+### 박물관 ML
+
+박물관은 기술 발전이 다가오면서 수집품 카탈로그 작성과 디지털화 그리고 유물 사이 연결고리 찾는 게 더 쉬워지는 AI 혁신의 정점에 있습니다. [In Codice Ratio](https://www.sciencedirect.com/science/article/abs/pii/S0306457321001035#:~:text=1.,studies%20over%20large%20historical%20sources.) 같은 프로젝트는 Vatican Archives처럼 희귀한 수집품의 미스터리를 풀고자 도울 수 있습니다. 그러나, 박물관의 비지니스 측면에서도 ML 모델의 이익이 있습니다.
+
+예시로, Art Institute of Chicago는 관람객이 어디에 관심있고 언제 박람회에 참석하는지 예측하는 모델을 만들었습니다. 목표로 사용자가 박물관을 방문하는 순간마다 게인마다 맞춰서 최적화된 방문객 경험을 만들고자 했습니다. "During fiscal 2017, the model predicted attendance and admissions within 1 percent of accuracy, says Andrew Simnick, senior vice president at the Art Institute."
+
+https://www.chicagobusiness.com/article/20180518/ISSUE01/180519840/art-institute-of-chicago-uses-data-to-make-exhibit-choices
+
+## 🏷 마케팅
+
+### 고객 세분화
+
+가장 효과적인 마케팅 전략은 다양한 그룹핑을 기반으로 다양한 방식에 소비자를 타게팅합니다. 이 아티클에서, Clustering 알고리즘으로 차별화된 마케팅을 지원하기 위한 이야기를 합니다. 차별화된 마케팅은 회사 브랜드 인식을 개선하고, 더 많은 소비자에게 다가가고, 더 많이 돈을 벌고자 도와줄 수 있습니다.
+
+https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
+
+## 🚀 도전
+
+이 커리큘럼에서 배웠던 일부 기술로 이익을 낼 다른 색터를 식별하고, ML을 어떻게 사용하는지 탐색합니다. 
+
+## [강의 후 학습](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/)
+
+## 검토 & 자기주도 학습
+
+Wayfair 데이터 사이언스 팀은 회사에서 어떻게 ML을 사용하는지에 대한 여러 흥미로운 비디오를 남겼습니다. [taking a look](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)해볼 가치가 있습니다!
+
+## 과제
+
+[A ML scavenger hunt](../assignment.md)
diff --git a/9-Real-World/1-Applications/translations/assignment.it.md b/9-Real-World/1-Applications/translations/assignment.it.md
new file mode 100644
index 000000000..fadb90a90
--- /dev/null
+++ b/9-Real-World/1-Applications/translations/assignment.it.md
@@ -0,0 +1,13 @@
+# Una caccia al tesoro per ML
+
+## Istruzioni
+
+In questa lezione si sono appresi molti casi d'uso reali che sono stati risolti utilizzando machine learning classico. Sebbene l'uso del deep learning, di nuove tecniche e strumenti nell'intelligenza artificiale e lo sfruttamento delle reti neurali abbia contribuito ad accelerare la produzione di strumenti per aiutare in questi settori, il machine learning classico che utilizza le tecniche esposte in questo programma di studi ha ancora un grande valore.
+
+In questo compito, si immagini di partecipare a un [hackathon](https://it.wikipedia.org/wiki/Hackathon). Usare ciò che si è appreso nel programma di studi per proporre una soluzione usando ML classico per risolvere un problema in uno dei settori discussi in questa lezione. Creare una presentazione in cui si discute come implementare la propria idea. Punti bonus se si riesce a raccogliere dati di esempio e creare un modello ML per supportare il proprio concetto!
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------------- | ------------------------------------------------- | ---------------------- |
+|          | Viene esposta una presentazione PowerPoint - bonus per la creazione di un modello | Viene esposta una presentazione di base non innovativa | Il lavoro è incompleto |
diff --git a/9-Real-World/translations/README.it.md b/9-Real-World/translations/README.it.md
new file mode 100644
index 000000000..682a116f4
--- /dev/null
+++ b/9-Real-World/translations/README.it.md
@@ -0,0 +1,15 @@
+# Poscritto: applicazioni del mondo reale di machine learning classico
+
+In questa sezione del programma di studi, verranno presentate alcune applicazioni del mondo reale di machine learning classico. Internet è stata setacciata per trovare [whitepaper](https://it.wikipedia.org/wiki/White_paper) e articoli sulle applicazioni che hanno utilizzato queste strategie, evitando il più possibile le reti neurali, il deep learning e l'intelligenza artificiale. Si scoprirà come viene utilizzato machine learning nei sistemi aziendali, applicazioni ecologiche, finanza, arte e cultura e altro ancora.
+
+![scacchi](../images/chess.jpg)
+
+> Foto di <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> su <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Lezione
+
+1. [Applicazioni del mondo reale per ML](../1-Applications/translations/README.it.md)
+
+## Crediti
+
+"Real-World Applications" è stato scritto da un team di persone, tra cui [Jen Looper](https://twitter.com/jenlooper) e [Ornella Altunyan](https://twitter.com/ornelladotcom).
\ No newline at end of file
diff --git a/9-Real-World/translations/README.ko.md b/9-Real-World/translations/README.ko.md
new file mode 100644
index 000000000..26da3f745
--- /dev/null
+++ b/9-Real-World/translations/README.ko.md
@@ -0,0 +1,15 @@
+# Postscript: Classic 머신러닝의 현실 애플리케이션
+
+커리큘럼의 이 섹션에서, classical ML의 실제-세계 에플리케이션을 소개힐 예정입니다. 가능한 neural networks, 딥러닝과 AI를 피하면서, 이 전략을 사용한 애플리케이션에 대한 백서와 아티클을 찾으려 웹 서핑을 했습니다. ML이 비즈니스 시스템, 생태학 애플리케이션, 금융, 예술과 문화, 그리고 더 많은 곳에서 어떻게 사용되는지 배웁니다.
+
+![chess](../images/chess.jpg)
+
+> Photo by <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> on <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## 강의
+
+1. [ML의 현실 애플리케이션](../1-Applications/translations/README.ko.md)
+
+## 크레딧
+
+"Real-World Applications" was written by a team of folks, including [Jen Looper](https://twitter.com/jenlooper) and [Ornella Altunyan](https://twitter.com/ornelladotcom).
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index ebf23acad..72af9ba72 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -5,6 +5,8 @@ agree to a Contributor License Agreement (CLA) declaring that you have the right
 and actually do, grant us the rights to use your contribution. For details, visit
 https://cla.microsoft.com.
 
+> Important: when translating text in this repo, please ensure that you do not use machine translation. We will verify translations via the community, so please only volunteer for translations in languages where you are proficient.
+
 When you submit a pull request, a CLA-bot will automatically determine whether you need
 to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the
 instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
diff --git a/README.md b/README.md
index 34c09b570..9caa9d6a2 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@
 
 > 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍
 
-Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our forthcoming 'Data Science for Beginners' curriculum, as well!
+Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson (plus one!) curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our forthcoming 'Data Science for Beginners' curriculum, as well!
 
 Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
 
@@ -20,19 +20,22 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 **🎨 Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
 
- **🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+**🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+
+**🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!** 
 
 ---
+
 # Getting Started
 
 **Students**, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
 
-- Start with a pre-lecture quiz
-- Read the lecture and complete the activities, pausing and reflecting at each knowledge check. 
-- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `/solution` folders in each project-oriented lesson. 
-- Take the post-lecture quiz
-- Complete the challenge
-- Complete the assignment
+- Start with a pre-lecture quiz.
+- Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
+- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `/solution` folders in each project-oriented lesson.
+- Take the post-lecture quiz.
+- Complete the challenge.
+- Complete the assignment.
 - After completing a lesson group, visit the [Discussion board](https://github.com/microsoft/ML-For-Beginners/discussions) and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.
 
 > For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
@@ -48,6 +51,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 > 🎥 Click the image above for a video about the project and the folks who created it!
 
 ---
+
 ## Pedagogy
 
 We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. In addition, this curriculum has a common **theme** to give it cohesion.
@@ -55,6 +59,7 @@ We have chosen two pedagogical tenets while building this curriculum: ensuring t
 By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
 
 > Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
+
 ## Each lesson includes:
 
 - optional sketchnote
@@ -68,47 +73,47 @@ By ensuring that the content aligns with projects, the process is made more enga
 - assignment
 - post-lecture quiz
 
-> **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
-
-
-| Lesson Number |                           Topic                            |                       Lesson Grouping                       | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
-| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              Introduction to machine learning              |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              The History of machine learning               |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
-|      03       |               Fairness and machine learning                |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              Techniques for machine learning               |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                 Introduction to regression                 |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        A Web App 🔌                         |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               Introduction to classification               |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |           Delicious Asian and Indian cuisines 🍜            |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |           Delicious Asian and Indian cuisines 🍜            |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |           Delicious Asian and Indian cuisines 🍜            |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 Introduction to clustering                 |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       Introduction to natural language processing  ☕️       |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     Common NLP Tasks ☕️                     |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            Translation and sentiment analysis ♥️            |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                Romantic hotels of Europe ♥️                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews, 1                                                                                        |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                Romantic hotels of Europe ♥️                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          Introduction to time series forecasting           |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                Help Peter avoid the wolf! 🐺                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+> **A note about quizzes**: All quizzes are contained [in this app](https://white-water-09ec41f0f.azurestaticapps.net/), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
+
+| Lesson Number |                            Topic                           |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                   Linked Lesson                  |     Author     |
+|:-------------:|:----------------------------------------------------------:|:---------------------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------:|:--------------:|
+|       01      |              Introduction to machine learning              |       [Introduction](1-Introduction/README.md)      | Learn the basic concepts behind machine learning                                                                                |    [Lesson](1-Introduction/1-intro-to-ML/README.md)           |    Muhammad    |
+|       02      |               The History of machine learning              |       [Introduction](1-Introduction/README.md)      | Learn the history underlying this field                                                                                         |   [Lesson](1-Introduction/2-history-of-ML/README.md)          |   Jen and Amy  |
+|       03      |                Fairness and machine learning               |       [Introduction](1-Introduction/README.md)      | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [Lesson](1-Introduction/3-fairness/README.md)             |     Tomomi     |
+|       04      |               Techniques for machine learning              |       [Introduction](1-Introduction/README.md)      | What techniques do ML researchers use to build ML models?                                                                       | [Lesson](1-Introduction/4-techniques-of-ML/README.md)          |  Chris and Jen |
+|       05      |                 Introduction to regression                 |         [Regression](2-Regression/README.md)        | Get started with Python and Scikit-learn for regression models                                                                  |       <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)</li></ul>       |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
+|       06      |               North American pumpkin prices 🎃              |         [Regression](2-Regression/README.md)        | Visualize and clean data in preparation for ML                                                                                  |     <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2-R.ipynb)</li></ul>         |        <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
+|       07      |               North American pumpkin prices 🎃              |         [Regression](2-Regression/README.md)        | Build linear and polynomial regression models                                                                                   |     <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul>       |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
+|       08      |               North American pumpkin prices 🎃              |         [Regression](2-Regression/README.md)        | Build a logistic regression model                                                                                               |     <ul><li>[Python](2-Regression/4-Logistic/README.md)  </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul>     |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
+|       09      |                         A Web App 🔌                        |            [Web App](3-Web-App/README.md)           | Build a web app to use your trained model                                                                                       |        [Python](3-Web-App/1-Web-App/README.md)       |       Jen      |
+|       10     |               Introduction to classification               |     [Classification](4-Classification/README.md)    | Clean, prep, and visualize your data; introduction to classification                                                            |<ul><li>  [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|       11      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | Introduction to classifiers                                                                                                     |<ul><li> [Python](4-Classification/2-Classifiers-1/README.md)</li><li>[R](4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|       12      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | More classifiers                                                                                                                |<ul><li>  [Python](4-Classification/3-Classifiers-2/README.md)</li><li>[R](4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|       13      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | Build a recommender web app using your model                                                                                    |     [Python](4-Classification/4-Applied/README.md)     |       Jen      |
+|       14      |                 Introduction to clustering                 |         [Clustering](5-Clustering/README.md)        | Clean, prep, and visualize your data; Introduction to clustering                                                                |     <ul><li> [Python](5-Clustering/1-Visualize/README.md)</li><li>[R](5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb)    |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
+|       15      |             Exploring Nigerian Musical Tastes 🎧            |         [Clustering](5-Clustering/README.md)        | Explore the K-Means clustering method                                                                                           |      <ul><li> [Python](5-Clustering/2-K-Means/README.md)</li><li>[R](5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb)       |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>      |
+|       16      |        Introduction to natural language processing ☕️       |    [Natural language processing](6-NLP/README.md)   | Learn the basics about NLP by building a simple bot                                                                             |    [Python](6-NLP/1-Introduction-to-NLP/README.md)  |     Stephen    |
+|       17      |                     Common NLP Tasks ☕️                     |    [Natural language processing](6-NLP/README.md)   | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [Python](6-NLP/2-Tasks/README.md)     |     Stephen    |
+|       18      |            Translation and sentiment analysis ♥️            |    [Natural language processing](6-NLP/README.md)   | Translation and sentiment analysis with Jane Austen                                                                             |   [Python](6-NLP/3-Translation-Sentiment/README.md) |     Stephen    |
+|       19      |                 Romantic hotels of Europe ♥️                |    [Natural language processing](6-NLP/README.md)   | Sentiment analysis with hotel reviews 1                                                                                         |      [Python](6-NLP/4-Hotel-Reviews-1/README.md)    |     Stephen    |
+|       20      |                 Romantic hotels of Europe ♥️                |    [Natural language processing](6-NLP/README.md)   | Sentiment analysis with hotel reviews 2                                                                                         |      [Python](6-NLP/5-Hotel-Reviews-2/README.md)   |     Stephen    |
+|       21      |           Introduction to time series forecasting          |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [Python](7-TimeSeries/1-Introduction/README.md)    |    Francesca   |
+|       22      | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |        [Python](7-TimeSeries/2-ARIMA/README.md)        |    Francesca   |
+|       23      |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [Python](8-Reinforcement/1-QLearning/README.md)  |     Dmitry     |
+|       24      |                Help Peter avoid the wolf! 🐺                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [Python](8-Reinforcement/2-Gym/README.md)      |     Dmitry     |
+|   Postscript  |          Real-World ML scenarios and applications          |       [ML in the Wild](9-Real-World/README.md)      | Interesting and revealing real-world applications of classical ML                                                               |    [Lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+
 ## Offline access
 
 You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
 
 ## PDFs
 
-Find a pdf of the curriculum with links [here](pdf/readme.pdf)
+Find a pdf of the curriculum with links [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 ## Help Wanted!
 
-Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
+Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71).
 
 ## Other Curricula
 
@@ -116,4 +121,4 @@ Our team produces other curricula! Check out:
 
 - [Web Dev for Beginners](https://aka.ms/webdev-beginners)
 - [IoT for Beginners](https://aka.ms/iot-beginners)
-
+- [Data Science for Beginners](https://aka.ms/datascience-beginners)
\ No newline at end of file
diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md
index 22b0d37ad..01f2f1395 100644
--- a/TRANSLATIONS.md
+++ b/TRANSLATIONS.md
@@ -26,7 +26,7 @@ Similar to Readme's, please translate the assignments as well.
 
 3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/ML-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language.
 
-4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1 becomes https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=id
+4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://white-water-09ec41f0f.azurestaticapps.net/quiz/1 becomes https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=id
 
 **THANK YOU**
 
diff --git a/package.json b/package.json
index b64c6bf14..3b5c347c9 100644
--- a/package.json
+++ b/package.json
@@ -4,8 +4,8 @@
 	"description": "Machine Learning for Beginners - A Curriculum",
 	"main": "index.js",
 	"scripts": {
-    "convert": "node_modules/.bin/docsify-to-pdf"
-  },
+		"convert": "node_modules/.bin/docsify-to-pdf"
+	},
 	"repository": {
 		"type": "git",
 		"url": "git+https://github.com/microsoft/ML-For-Beginners.git"
diff --git a/quiz-app/README.md b/quiz-app/README.md
index 042d53ca1..83b30d1d9 100644
--- a/quiz-app/README.md
+++ b/quiz-app/README.md
@@ -1,6 +1,6 @@
 # Quizzes
 
-These quizzes are the pre- and post-lecture quizzes for the web development for ml curriculum at https://aka.ms/ml-beginners
+These quizzes are the pre- and post-lecture quizzes for the ML curriculum at https://aka.ms/ml-beginners
 
 ## Project setup
 
diff --git a/quiz-app/package-lock.json b/quiz-app/package-lock.json
index e9aebee3c..8f51a0baa 100644
--- a/quiz-app/package-lock.json
+++ b/quiz-app/package-lock.json
@@ -1087,16 +1087,6 @@
         "postcss": "^7.0.0"
       }
     },
-    "@kazupon/vue-i18n-loader": {
-      "version": "0.5.0",
-      "resolved": "https://registry.npmjs.org/@kazupon/vue-i18n-loader/-/vue-i18n-loader-0.5.0.tgz",
-      "integrity": "sha512-Tp2mXKemf9/RBhI9CW14JjR9oKjL2KH7tV6S0eKEjIBuQBAOFNuPJu3ouacmz9hgoXbNp+nusw3MVQmxZWFR9g==",
-      "dev": true,
-      "requires": {
-        "js-yaml": "^3.13.1",
-        "json5": "^2.1.1"
-      }
-    },
     "@mrmlnc/readdir-enhanced": {
       "version": "2.2.1",
       "resolved": "https://registry.npmjs.org/@mrmlnc/readdir-enhanced/-/readdir-enhanced-2.2.1.tgz",
@@ -1720,6 +1710,16 @@
           "integrity": "sha512-nQyp0o1/mNdbTO1PO6kHkwSrmgZ0MT/jCCpNiwbUjGoRN4dlBhqJtoQuCnEOKzgTVwg0ZWiCoQy6SxMebQVh8A==",
           "dev": true
         },
+        "ansi-styles": {
+          "version": "4.3.0",
+          "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz",
+          "integrity": "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "color-convert": "^2.0.1"
+          }
+        },
         "cacache": {
           "version": "13.0.1",
           "resolved": "https://registry.npmjs.org/cacache/-/cacache-13.0.1.tgz",
@@ -1746,6 +1746,53 @@
             "unique-filename": "^1.1.1"
           }
         },
+        "chalk": {
+          "version": "4.1.1",
+          "resolved": "https://registry.npmjs.org/chalk/-/chalk-4.1.1.tgz",
+          "integrity": "sha512-diHzdDKxcU+bAsUboHLPEDQiw0qEe0qd7SYUn3HgcFlWgbDcfLGswOHYeGrHKzG9z6UYf01d9VFMfZxPM1xZSg==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "ansi-styles": "^4.1.0",
+            "supports-color": "^7.1.0"
+          }
+        },
+        "color-convert": {
+          "version": "2.0.1",
+          "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz",
+          "integrity": "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "color-name": "~1.1.4"
+          }
+        },
+        "color-name": {
+          "version": "1.1.4",
+          "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz",
+          "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==",
+          "dev": true,
+          "optional": true
+        },
+        "has-flag": {
+          "version": "4.0.0",
+          "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz",
+          "integrity": "sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==",
+          "dev": true,
+          "optional": true
+        },
+        "loader-utils": {
+          "version": "2.0.0",
+          "resolved": "https://registry.npmjs.org/loader-utils/-/loader-utils-2.0.0.tgz",
+          "integrity": "sha512-rP4F0h2RaWSvPEkD7BLDFQnvSf+nK+wr3ESUjNTyAGobqrijmW92zc+SO6d4p4B1wh7+B/Jg1mkQe5NYUEHtHQ==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "big.js": "^5.2.2",
+            "emojis-list": "^3.0.0",
+            "json5": "^2.1.2"
+          }
+        },
         "source-map": {
           "version": "0.6.1",
           "resolved": "https://registry.npmjs.org/source-map/-/source-map-0.6.1.tgz",
@@ -1762,6 +1809,16 @@
             "minipass": "^3.1.1"
           }
         },
+        "supports-color": {
+          "version": "7.2.0",
+          "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
+          "integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "has-flag": "^4.0.0"
+          }
+        },
         "terser-webpack-plugin": {
           "version": "2.3.8",
           "resolved": "https://registry.npmjs.org/terser-webpack-plugin/-/terser-webpack-plugin-2.3.8.tgz",
@@ -1778,6 +1835,18 @@
             "terser": "^4.6.12",
             "webpack-sources": "^1.4.3"
           }
+        },
+        "vue-loader-v16": {
+          "version": "npm:vue-loader@16.3.0",
+          "resolved": "https://registry.npmjs.org/vue-loader/-/vue-loader-16.3.0.tgz",
+          "integrity": "sha512-UDgni/tUVSdwHuQo+vuBmEgamWx88SuSlEb5fgdvHrlJSPB9qMBRF6W7bfPWSqDns425Gt1wxAUif+f+h/rWjg==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "chalk": "^4.1.0",
+            "hash-sum": "^2.0.0",
+            "loader-utils": "^2.0.0"
+          }
         }
       }
     },
@@ -10953,87 +11022,6 @@
         }
       }
     },
-    "vue-loader-v16": {
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diff --git a/quiz-app/src/App.vue b/quiz-app/src/App.vue
index 78482d496..ef95dbed2 100644
--- a/quiz-app/src/App.vue
+++ b/quiz-app/src/App.vue
@@ -6,6 +6,8 @@
       <select v-model="locale">
         <option>en</option>
         <option>tr</option>
+        <option>fr</option>
+        <option>ja</option>
       </select>
     </nav>
     <div id="app">
diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index ae358aef2..337b08675 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -1,2815 +1,2815 @@
 [
-	{
-		"title": "Machine Learning for Beginners: Quizzes",
-		"complete": "Congratulations, you completed the quiz!",
-		"error": "Sorry, try again",
-		"quizzes": [
-			{
-				"id": 1,
-				"title": "Introduction to Machine Learning: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Applications of machine learning are all around us",
-						"answerOptions": [
-							{
-								"answerText": "True",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "False",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is the technical difference between classical ML and deep learning?",
-						"answerOptions": [
-							{
-								"answerText": "classical ML was invented first",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the use of neural networks",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "deep learning is used in robots",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Why might a business want to use ML strategies?",
-						"answerOptions": [
-							{
-								"answerText": "to automate the solving of multi-dimensional problems",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "to customize a shopping experience based on the type of customer",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 2,
-				"title": "Introduction to Machine Learning: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Machine learning algorithms are meant to simulate",
-						"answerOptions": [
-							{
-								"answerText": "intelligent machines",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the human brain",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "orangutans",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is an example of a classical ML technique?",
-						"answerOptions": [
-							{
-								"answerText": "natural language processing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "deep learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Neural Networks",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Why should everyone learn the basics of ML?",
-						"answerOptions": [
-							{
-								"answerText": "learning ML is fun and accessible to everyone",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "ML strategies are being used in many industries and domains",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 3,
-				"title": "History of Machine Learning: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Approximately when was the term 'artificial intelligence' coined?",
-						"answerOptions": [
-							{
-								"answerText": "1980s",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "1950s",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "1930s",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Who was one of the early pioneers of machine learning?",
-						"answerOptions": [
-							{
-								"answerText": "Alan Turing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Bill Gates",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Shakey the robot",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is one of the reasons that advancement in AI slowed in the 1970s?",
-						"answerOptions": [
-							{
-								"answerText": "Limited compute power",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Not enough skilled engineers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Conflicts between countries",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 4,
-				"title": "History of Machine Learning: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What's an example of a 'scruffy' AI system?",
-						"answerOptions": [
-							{
-								"answerText": "ELIZA",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "HACKML",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "SSYSTEM",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is an example of a technology that was developed during 'The Golden Years'?",
-						"answerOptions": [
-							{
-								"answerText": "Blocks world",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Jibo",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Robot dogs",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which event was foundational in the creation and expansion of the field of artificial intelligence?",
-						"answerOptions": [
-							{
-								"answerText": "Turing Test",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Dartmouth Summer Research Project",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "AI Winter",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 5,
-				"title": "Fairness and Machine Learning: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Unfairness in Machine Learning can happen",
-						"answerOptions": [
-							{
-								"answerText": "intentionally",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "unintentionally",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "The term 'unfairness' in ML connotes:",
-						"answerOptions": [
-							{
-								"answerText": "harms for a group of people",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "harm to one person",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "harms for the majority of people",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The five main types of harms include",
-						"answerOptions": [
-							{
-								"answerText": "allocation, quality of service, stereotyping, denigration, and over- or under- representation",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "elocation, quality of service, stereotyping, denigration, and over- or under- representation ",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "allocation, quality of service, stereophonics, denigration, and over- or under- representation ",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 6,
-				"title": "Fairness and Machine Learning: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Unfairness in a model can be caused by",
-						"answerOptions": [
-							{
-								"answerText": "overrreliance on historical data",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "underreliance on historical data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "too closely aligning to historical data",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "To mitigate unfairness, you can",
-						"answerOptions": [
-							{
-								"answerText": "identify harms and affected groups",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "define fairness metrics",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Fairlearn is a package that can",
-						"answerOptions": [
-							{
-								"answerText": "compare multiple models by using fairness and performance metrics",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "choose the best model for your needs",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "help you decide what is fair and what is not",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 7,
-				"title": "Tools and Techniques: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "When building a model, you should:",
-						"answerOptions": [
-							{
-								"answerText": "prepare your data, then train your model",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "choose a training method, then prepare your data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "tune parameters, then train your model",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Your data's ___ will impact the quality of your ML model",
-						"answerOptions": [
-							{
-								"answerText": "quantity",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "shape",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "A feature variable is:",
-						"answerOptions": [
-							{
-								"answerText": "a quality of your data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a measurable property of your data",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a row of your data",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 8,
-				"title": "Tools and Techniques: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "You should visualize your data because",
-						"answerOptions": [
-							{
-								"answerText": "you can discover outliers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "you can discover potential cause for bias",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Split your data into:",
-						"answerOptions": [
-							{
-								"answerText": "training and turing sets",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "training and test sets",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "validation and evaluation sets",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A common command to start the training process in various ML libraries is:",
-						"answerOptions": [
-							{
-								"answerText": "model.travel",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "model.train",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "model.fit",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 9,
-				"title": "Introduction to Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which of these variables is a numeric variable?",
-						"answerOptions": [
-							{
-								"answerText": "Height",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Gender",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Hair Color",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these variables is a categorical variable?",
-						"answerOptions": [
-							{
-								"answerText": "Heart Rate",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Blood Type",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Weight",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these problems is a Regression analysis-based problem?",
-						"answerOptions": [
-							{
-								"answerText": "Predicting the final exam marks of a student",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Predicting the blood type of a person",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Predicting whether an email is spam or not",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 10,
-				"title": "Introduction to Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "If your Machine Learning model's training accuracy is 95 % and the testing accuracy is 30 %, then what type of condition it is called?",
-						"answerOptions": [
-							{
-								"answerText": "Overfitting",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Underfitting",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Double Fitting",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The process of identifying significant features from a set of features is called:",
-						"answerOptions": [
-							{
-								"answerText": "Feature Extraction",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Feature Dimensionality Reduction",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Feature Selection",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "The process of splitting a dataset into a certain ratio of training and testing dataset using Scikit Learn's 'train_test_split()' method/function is called:",
-						"answerOptions": [
-							{
-								"answerText": "Cross-Validation",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Hold-Out Validation",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Leave one out Validation",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 11,
-				"title": "Prepare and Visualize Data for Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which of these Python modules is used to plot the visualization of data?",
-						"answerOptions": [
-							{
-								"answerText": "Numpy",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Scikit-learn",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Matplotlib",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "If you want to understand the spread or the other characteristics of data points of your dataset, then perform:",
-						"answerOptions": [
-							{
-								"answerText": "Data Visualization",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Data Preprocessing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Train Test Split",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these is a part of the Data Visualization step in a Machine Learning project?",
-						"answerOptions": [
-							{
-								"answerText": "Incorporating a certain Machine Learning algorithm",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Creating a pictorial representation of data using different plotting methods",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Normalizing the values of a dataset",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 12,
-				"title": "Prepare and Visualize Data for Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which of these code snippets is correct based on this lesson, if you want to check for the presence of missing values in your dataset? Suppose the dataset is stored in a variable named 'dataset' which is a Pandas DataFrame object.",
-						"answerOptions": [
-							{
-								"answerText": "dataset.isnull().sum()",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "findMissing(dataset)",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sum(null(dataset))",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these plotting methods is useful when you would like to understand the spread of different groups of datapoints from your dataset?",
-						"answerOptions": [
-							{
-								"answerText": "Scatter Plot",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Line Plot",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Bar Plot",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What can Data Visualization NOT tell you?",
-						"answerOptions": [
-							{
-								"answerText": "Relationships among datapoints",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "The source from where the dataset is collected",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Finding the presence of outliers in the dataset",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 13,
-				"title": "Linear and Polynomial Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Matplotlib is a ",
-						"answerOptions": [
-							{
-								"answerText": "drawing library",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "data visualization library",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "lending library",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Linear Regression uses the following to plot relationships between variables",
-						"answerOptions": [
-							{
-								"answerText": "a straight line",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a circle",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a curve",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A good Linear Regression model has a ___ Correlation Coefficient",
-						"answerOptions": [
-							{
-								"answerText": "low",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "high",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "flat",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 14,
-				"title": "Linear and Polynomial Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "If your data is nonlinear, try a ___ type of Regression",
-						"answerOptions": [
-							{
-								"answerText": "linear",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "spherical",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "polynomial",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "These are all types of Regression methods",
-						"answerOptions": [
-							{
-								"answerText": "Falsestep, Ridge, Lasso and Elasticnet",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Stepwise, Ridge, Lasso and Elasticnet",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Stepwise, Ridge, Lariat and Elasticnet",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Least-Squares Regression means that all the datapoints surrounding the regression line are:",
-						"answerOptions": [
-							{
-								"answerText": "squared and then subtracted",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "multiplied",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "squared and then added up",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 15,
-				"title": "Logistic Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Use Logistic Regression to predict",
-						"answerOptions": [
-							{
-								"answerText": "whether an apple is ripe or not",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "how many tickets can be sold in a month",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "what color the sky will turn tomorrow at 6 PM",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Types of Logistic Regression include",
-						"answerOptions": [
-							{
-								"answerText": "multinomial and cardinal",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "multinomial and ordinal",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "principal and ordinal",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Your data has weak correlations. The best type of Regression to use is:",
-						"answerOptions": [
-							{
-								"answerText": "Logistic",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Linear",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Cardinal",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 16,
-				"title": "Logistic Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Seaborn is a type of",
-						"answerOptions": [
-							{
-								"answerText": "data visualization library",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "mapping library",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "mathematical library",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A confusion matrix is also known as a:",
-						"answerOptions": [
-							{
-								"answerText": "error matrix",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "truth matrix",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "accuracy matrix",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A good model will have:",
-						"answerOptions": [
-							{
-								"answerText": "a large number of false positives and true negatives in its confusion matrix",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a large number of true positives and true negatives in its confusion matrix",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a large number of true positives and false negatives in its confusion matrix",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 17,
-				"title": "Build a Web App: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What does ONNX stand for?",
-						"answerOptions": [
-							{
-								"answerText": "Over Neural Network Exchange",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Open Neural Network Exchange",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Output Neural Network Exchange",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "How is Flask defined by its creators?",
-						"answerOptions": [
-							{
-								"answerText": "mini-framework",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "large-framework",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "micro-framework",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What does the Pickle module of Python do",
-						"answerOptions": [
-							{
-								"answerText": "Serializes a Python Object",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "De-serializes a Python Object",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Serializes and De-serializes a Python Object",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 18,
-				"title": "Build a Web App: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What are the tools we can use to host a pre-trained model on the web using Python?",
-						"answerOptions": [
-							{
-								"answerText": "Flask",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "TensorFlow.js",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "onnx.js",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What does SaaS stand for?",
-						"answerOptions": [
-							{
-								"answerText": "System as a Service",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Software as a Service",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Security as a Service",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What does Scikit-learn's LabelEncoder library do?",
-						"answerOptions": [
-							{
-								"answerText": "Encodes data alphabetically",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Encodes data numerically",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Encodes data serially",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 19,
-				"title": "Classification 1: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Classification is a form of supervised learning that has a lot in common with",
-						"answerOptions": [
-							{
-								"answerText": "Time Series",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Regression techniques",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What question can classification help answer?",
-						"answerOptions": [
-							{
-								"answerText": "Is this email spam or not?",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Can pigs fly?",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "What is the meaning of life?",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is the first step to using Classification techniques?",
-						"answerOptions": [
-							{
-								"answerText": "creating classes of a dataset",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "cleaning and balancing your data",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "assigning a data point to a group or outcome",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 20,
-				"title": "Classification 1: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is a multiclass question?",
-						"answerOptions": [
-							{
-								"answerText": "the task of classifying data points into multiple classes",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the task of classifying data points into one of several classes",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the task of cleaning data points in multiple ways",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "It's important to clean out recurrent or unhelpful data to help your classifiers solve your problem.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What's the best reason to balance your data?",
-						"answerOptions": [
-							{
-								"answerText": "Imbalanced data looks bad in visualizations",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Balancing your data yields better results because an ML model won't skew towards one class",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Balancing your data gives you more data points",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 21,
-				"title": "Classification 2: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Balanced, clean data yields the best classification results",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "How do you choose the right classifier?",
-						"answerOptions": [
-							{
-								"answerText": "Understand which classifiers work best for which scenarios",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Educated guess and check",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Classification is a type of",
-						"answerOptions": [
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Supervised Learning",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Programming language",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 22,
-				"title": "Classification 2: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is a 'solver'?",
-						"answerOptions": [
-							{
-								"answerText": "the person who double-checks your work",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the algorithm to use in the optimization problem",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a machine learning technique",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which classifier did we use in this lesson?",
-						"answerOptions": [
-							{
-								"answerText": "Logistic Regression",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Decision Trees",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "One-vs-All Multiclass",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "How do you know if the classification algorithm is working as expected?",
-						"answerOptions": [
-							{
-								"answerText": "By checking the accuracy of its predictions",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "By checking it against other algorithms",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "By looking at historical data for how good this algorithm is at solving similar problems",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 23,
-				"title": "Classification 3: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "A good initial classifier to try is:",
-						"answerOptions": [
-							{
-								"answerText": "Linear SVC",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "K-Means",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Logical SVC",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Regularization controls:",
-						"answerOptions": [
-							{
-								"answerText": "the influence of parameters",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the influence of training speed",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the influence of outliers",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "K-Neighbors classifier can be used for:",
-						"answerOptions": [
-							{
-								"answerText": "supervised learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "unsupervised learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 24,
-				"title": "Classification 3: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Support-Vector classifiers can be used for",
-						"answerOptions": [
-							{
-								"answerText": "classification",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "regression",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Random Forest is a ___ type of classifier",
-						"answerOptions": [
-							{
-								"answerText": "Ensemble",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Dissemble",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Assemble",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Adaboost is known for:",
-						"answerOptions": [
-							{
-								"answerText": "focusing on the weights of incorrectly classified items",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "focusing on outliers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "focusing on incorrect data",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 25,
-				"title": "Classification 4: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Recommendation systems might be used for",
-						"answerOptions": [
-							{
-								"answerText": "Recommending a good restaurant",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Recommending fashions to try",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Embedding a model in a web app helps it to be offline-capable",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Onnx Runtime can be used for",
-						"answerOptions": [
-							{
-								"answerText": "Running models in a web app",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Training models",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Hyperparameter tuning",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 26,
-				"title": "Classification 4: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Netron app helps you:",
-						"answerOptions": [
-							{
-								"answerText": "Visualize data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Visualize your model's structure",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Test your web app",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Convert your Scikit-learn model for use with Onnx using:",
-						"answerOptions": [
-							{
-								"answerText": "sklearn-app",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sklearn-web",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sklearn-onnx",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Using your model in a web app is called:",
-						"answerOptions": [
-							{
-								"answerText": "inference",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "interference",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "insurance",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 27,
-				"title": "Introduction to Clustering: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "A real-life example of clustering would be",
-						"answerOptions": [
-							{
-								"answerText": "Setting the dinner table",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Sorting the laundry",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Grocery shopping",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Clustering techniques can be used in these industries",
-						"answerOptions": [
-							{
-								"answerText": "banking",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "e-commerce",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Clustering is a type of:",
-						"answerOptions": [
-							{
-								"answerText": "supervised learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "unsupervised learning",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "reinforcement learning",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 28,
-				"title": "Introduction to Clustering: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Euclidean geometry is arranged along",
-						"answerOptions": [
-							{
-								"answerText": "planes",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "curves",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "spheres",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The density of your clustering data is related to its",
-						"answerOptions": [
-							{
-								"answerText": "noise",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "depth",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "validity",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The best-known clustering algorithm is",
-						"answerOptions": [
-							{
-								"answerText": "k-means",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "k-middle",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "k-mart",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 29,
-				"title": "K-Means Clustering: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "K-Means is derived from:",
-						"answerOptions": [
-							{
-								"answerText": "electrical engineering",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "signal processing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "computational linguistics",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A good Silhouette score means:",
-						"answerOptions": [
-							{
-								"answerText": "clusters are well-separated and well-defined",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "there are few clusters",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "there are many clusters",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Variance is:",
-						"answerOptions": [
-							{
-								"answerText": "the average of the squared differences from the mean",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a problem for clustering if it becomes too high",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 30,
-				"title": "K-Means Clustering: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "A Voronoi diagram shows:",
-						"answerOptions": [
-							{
-								"answerText": "a cluster's variance",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a cluster's seed and its region",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a cluster's inertia",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Inertia is",
-						"answerOptions": [
-							{
-								"answerText": "a measure of how internally coherent clusters are",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a measure of how much clusters move",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a measure of cluster quality",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Using K-Means, you must first determine the value of 'k'",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 31,
-				"title": "Intro to NLP: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What does NLP stand for in these lessons?",
-						"answerOptions": [
-							{
-								"answerText": "Neural Language Processing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "natural language processing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Natural Linguistic Processing",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Eliza was an early bot that acted as a computer",
-						"answerOptions": [
-							{
-								"answerText": "therapist",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "doctor",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "nurse",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Alan Turing's 'Turing Test' tried to determine if a computer was",
-						"answerOptions": [
-							{
-								"answerText": "indistinguishable from a human",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "thinking",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 32,
-				"title": "Intro to NLP: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Joseph Weizenbaum invented the bot",
-						"answerOptions": [
-							{
-								"answerText": "Elisha",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Eliza",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Eloise",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A conversational bot gives output based on",
-						"answerOptions": [
-							{
-								"answerText": "Randomly choosing predefined choices",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Analyzing the input and using machine intelligence",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "How would you make the bot more effective?",
-						"answerOptions": [
-							{
-								"answerText": "By asking it more questions.",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "By feeding it more data and training it accordingly",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "The bot is dumb, it cannot learn :(",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 33,
-				"title": "NLP Tasks: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Tokenization",
-						"answerOptions": [
-							{
-								"answerText": "Splits text by means of punctuation",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Splits text into separate tokens (words)",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Splits text into phrases",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Embeddings",
-						"answerOptions": [
-							{
-								"answerText": "converts text data numerically so words can cluster",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "embeds words into phrases",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "embeds sentences into paragraphs",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Parts-of-Speech Tagging",
-						"answerOptions": [
-							{
-								"answerText": "divides sentences by their parts of speech",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "takes tokenized words and tags them by their part of speech",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "diagrams sentences",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 34,
-				"title": "NLP Tasks: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Build a dictionary of how often words reoccur using:",
-						"answerOptions": [
-							{
-								"answerText": "Word and Phrase Dictionary",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Word and Phrase Frequencies",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Word and Phrase Library",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "N-grams refer to",
-						"answerOptions": [
-							{
-								"answerText": "A text can be split into sequences of words of a set length",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "A word can be split into sequences of characters of a set length",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "A text can be split into paragraphs of a set length",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Sentiment analysis",
-						"answerOptions": [
-							{
-								"answerText": "analyzes a phrase for positivity or negativity",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "analyzes a phrase for sentimentality",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "analyzes a phrase for sadness",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 35,
-				"title": "NLP and Translation: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Naive translation",
-						"answerOptions": [
-							{
-								"answerText": "translates words only",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "translates sentence structure",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "translates sentiment",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A *corpus* of texts refers to",
-						"answerOptions": [
-							{
-								"answerText": "A small number of texts",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "A large number of texts",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "One standard text",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "If a ML model has enough human translations to build a model on, it can",
-						"answerOptions": [
-							{
-								"answerText": "abbreviate translations",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "standardize translations",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "improve the accuracy of translations",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 36,
-				"title": "NLP and Translation: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Underlying TextBlob's translation library is:",
-						"answerOptions": [
-							{
-								"answerText": "Google Translate",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Bing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "A custom ML model",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "To use `blob.translate` you need:",
-						"answerOptions": [
-							{
-								"answerText": "an internet connection",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a dictionary",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "JavaScript",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "To determine sentiment, an ML approach would be to:",
-						"answerOptions": [
-							{
-								"answerText": "apply Regression techniques to manually generated opinions and scores and look for patterns",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "apply NLP techniques to manually generated opinions and scores and look for patterns",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "apply Clustering techniques to manually generated opinions and scores and look for patterns",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 37,
-				"title": "NLP 4: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What information can we get from text that was written or spoken by a human?",
-						"answerOptions": [
-							{
-								"answerText": "patterns and frequencies",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sentiment and meaning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What is sentiment analysis?",
-						"answerOptions": [
-							{
-								"answerText": "a study of whether a family heirloom has sentimental value",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a method of systematically identifying, extracting, quantifying, and studying affective states and subjective information",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the ability to tell whether someone is sad or happy",
-								"isCorrect": "false"
-							}
-						]
-					},					
-					{
-						"questionText": "What question could be answered using a dataset of hotel reviews, Python, and sentiment analysis?",
-						"answerOptions": [
-							{
-								"answerText": "What are the most frequently used words and phrases in reviews?",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Which resort has the best pool?",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Is there valet parking at this hotel?",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 38,
-				"title": "NLP 4: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is the essence of NLP?",
-						"answerOptions": [
-							{
-								"answerText": "categorizing human language into happy or sad",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "interpreting meaning or sentiment without having to have a human do it",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "finding outliers in sentiment and examining them",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What are some things you might look for while cleaning data?",
-						"answerOptions": [
-							{
-								"answerText": "characters in other languages",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "blank rows or columns",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "It is important to understand your data and its foibles before performing operations on it.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 39,
-				"title": "NLP 5: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Why is it important to clean data before analyzing it?",
-						"answerOptions": [
-							{
-								"answerText": "Some columns might have missing or incorrect data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Messy data can lead to false conclusions about the dataset",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What is one example of a strategy for cleaning data?",
-						"answerOptions": [
-							{
-								"answerText": "removing columns/rows that aren't useful for answering a specific question",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "getting rid of verified values that don't fit your hypothesis",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "moving the outliers to a separate table and running the calculations for that table to see if they match",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "It can be useful to categorize data using a Tag column.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 40,
-				"title": "NLP 5: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is the goal of the dataset?",
-						"answerOptions": [
-							{
-								"answerText": "to see how many negative and positive reviews there are for hotels across the world",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "to add sentiment and columns that will help you choose the best hotel",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "to analyze why people leave specific reviews",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What are stop words?",
-						"answerOptions": [
-							{
-								"answerText": "common English words that do not change the sentiment of a sentence",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "words that you can remove to speed up sentiment analysis",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "To test the sentiment analysis, make sure it matches the reviewer's score for the same review.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 41,
-				"title": "Intro to Time Series: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Time Series Forecasting is useful in",
-						"answerOptions": [
-							{
-								"answerText": "determining future costs",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "predicting future pricing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "A time series is a sequence taken at:",
-						"answerOptions": [
-							{
-								"answerText": "successive equally spaced points in space",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "successive equally spaced points in time",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "successive equally spaced points in space and time",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Time series can be used in:",
-						"answerOptions": [
-							{
-								"answerText": "earthquake prediction",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "computer vision",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "color analysis",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 42,
-				"title": "Intro to Time Series: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Time series trends are",
-						"answerOptions": [
-							{
-								"answerText": "Measurable increases and decreases over time",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Quantifying decreases over time",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Gaps between increases and decreases over time",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Outliers are",
-						"answerOptions": [
-							{
-								"answerText": "points close to standard data variance",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "points far away from standard data variance",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "points within standard data variance",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Time Series Forecasting is most useful for",
-						"answerOptions": [
-							{
-								"answerText": "Econometrics",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "History",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Libraries",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 43,
-				"title": "Time Series ARIMA: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "ARIMA stands for",
-						"answerOptions": [
-							{
-								"answerText": "AutoRegressive Integral Moving Average",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "AutoRegressive Integrated Moving Action",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "AutoRegressive Integrated Moving Average",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Stationarity refers to",
-						"answerOptions": [
-							{
-								"answerText": "data whose attributes does not change when shifted in time",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "data whose distribution does not change when shifted in time",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "data whose distribution changes when shifted in time",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Differencing",
-						"answerOptions": [
-							{
-								"answerText": "stabilizes trend and seasonality",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "exacerbates trend and seasonality",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "eliminates trend and seasonality",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 44,
-				"title": "Time Series ARIMA: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "ARIMA is used to make a model fit the special form of time series data",
-						"answerOptions": [
-							{
-								"answerText": "as flat as possible",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "as closely as possible",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "via scatterplots",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Use SARIMAX to",
-						"answerOptions": [
-							{
-								"answerText": "manage seasonal ARIMA models",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "manage special ARIMA models",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "manage statistical ARIMA models",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "'Walk-Forward' validation involves",
-						"answerOptions": [
-							{
-								"answerText": "re-evaluating a model progressively as it is validated",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "re-training a model progressively as it is validated",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "re-configuring a model progressively as it is validated",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 45,
-				"title": "Reinforcement 1: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is reinforcement learning?",
-						"answerOptions": [
-							{
-								"answerText": "teaching someone something over and over again until they understand",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a learning technique that deciphers the optimal behavior of an agent in some environment by running many experiments",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "understanding how to run multiple experiments at once",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is a policy?",
-						"answerOptions": [
-							{
-								"answerText": "a function that returns the action at any given state",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a document that tells you whether or not you can return an item",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a function that is used for a random purpose",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A reward function returns a score for each state of an environment.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 46,
-				"title": "Reinforcement 1: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is Q-Learning?",
-						"answerOptions": [
-							{
-								"answerText": "a mechanism for recording the 'goodness' of each state",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "an algorithm where the policy is defined by a Q-Table",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "For what values does a Q-Table correspond to the random walk policy?",
-						"answerOptions": [
-							{
-								"answerText": "all equal values",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "-0.25",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "all different values",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "It was better to use exploration than exploitation during the learning process in our lesson.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 47,
-				"title": "Reinforcement 2: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Chess and Go are games with continuous states.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What is the CartPole problem?",
-						"answerOptions": [
-							{
-								"answerText": "a process for eliminating outliers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a method for optimizing your shopping cart",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a simplified version of balancing",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What tool can we use to play out different scenarios of potential states in a game?",
-						"answerOptions": [
-							{
-								"answerText": "guess and check",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "simulation environments",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "state transition testing",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 48,
-				"title": "Reinforcement 2: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Where do we define all possible actions in an environment?",
-						"answerOptions": [
-							{
-								"answerText": "methods",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "action space",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "action list",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What pair did we use as the dictionary key-value?",
-						"answerOptions": [
-							{
-								"answerText": "(state, action) as the key, Q-Table entry as the value",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "state as the key, action as the value",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the value of the qvalues function as the key, action as the value",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What are the hyperparameters we used during Q-Learning?",
-						"answerOptions": [
-							{
-								"answerText": "q-table value, current reward, random action",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "learning rate, discount factor, exploration/exploitation factor",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "cumulative rewards, learning rate, exploration factor",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 49,
-				"title": "Real World Applications: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What's an example of an ML application in the Finance industry?",
-						"answerOptions": [
-							{
-								"answerText": "Personalizing the customer journey using NLP",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Wealth management using linear regression",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Energy management using Time Series",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What ML technique can hospitals use to manage readmission?",
-						"answerOptions": [
-							{
-								"answerText": "Clustering",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Time Series",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is an example of using Time Series for energy management?",
-						"answerOptions": [
-							{
-								"answerText": "Motion sensing animals",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Smart parking meters",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Tracking forest fires",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 50,
-				"title": "Real World Applications: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which ML technique can be used to detect credit card fraud?",
-						"answerOptions": [
-							{
-								"answerText": "Regression",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Clustering",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which ML technique is exemplified in forest management?",
-						"answerOptions": [
-							{
-								"answerText": "Reinforcement Learning",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Time Series",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What's an example of an ML application in the Health Care industry?",
-						"answerOptions": [
-							{
-								"answerText": "Predicting student behavior using regression",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Managing clinical trials using classifiers",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Motion sensing of animals using classifiers",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			}
-		]
-	}
+  {
+    "title": "Machine Learning for Beginners: Quizzes",
+    "complete": "Congratulations, you completed the quiz!",
+    "error": "Sorry, try again",
+    "quizzes": [
+      {
+        "id": 1,
+        "title": "Introduction to Machine Learning: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Applications of machine learning are all around us",
+            "answerOptions": [
+              {
+                "answerText": "True",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "False",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the technical difference between classical ML and deep learning?",
+            "answerOptions": [
+              {
+                "answerText": "classical ML was invented first",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the use of neural networks",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "deep learning is used in robots",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Why might a business want to use ML strategies?",
+            "answerOptions": [
+              {
+                "answerText": "to automate the solving of multi-dimensional problems",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "to customize a shopping experience based on the type of customer",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 2,
+        "title": "Introduction to Machine Learning: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Machine learning algorithms are meant to simulate",
+            "answerOptions": [
+              {
+                "answerText": "intelligent machines",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the human brain",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "orangutans",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is an example of a classical ML technique?",
+            "answerOptions": [
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "deep learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Neural Networks",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Why should everyone learn the basics of ML?",
+            "answerOptions": [
+              {
+                "answerText": "learning ML is fun and accessible to everyone",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ML strategies are being used in many industries and domains",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 3,
+        "title": "History of Machine Learning: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Approximately when was the term 'artificial intelligence' coined?",
+            "answerOptions": [
+              {
+                "answerText": "1980s",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "1950s",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "1930s",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Who was one of the early pioneers of machine learning?",
+            "answerOptions": [
+              {
+                "answerText": "Alan Turing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bill Gates",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Shakey the robot",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is one of the reasons that advancement in AI slowed in the 1970s?",
+            "answerOptions": [
+              {
+                "answerText": "Limited compute power",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Not enough skilled engineers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Conflicts between countries",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 4,
+        "title": "History of Machine Learning: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What's an example of a 'scruffy' AI system?",
+            "answerOptions": [
+              {
+                "answerText": "ELIZA",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "HACKML",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SSYSTEM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is an example of a technology that was developed during 'The Golden Years'?",
+            "answerOptions": [
+              {
+                "answerText": "Blocks world",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Jibo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Robot dogs",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which event was foundational in the creation and expansion of the field of artificial intelligence?",
+            "answerOptions": [
+              {
+                "answerText": "Turing Test",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Dartmouth Summer Research Project",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "AI Winter",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 5,
+        "title": "Fairness and Machine Learning: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Unfairness in Machine Learning can happen",
+            "answerOptions": [
+              {
+                "answerText": "intentionally",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "unintentionally",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "The term 'unfairness' in ML connotes:",
+            "answerOptions": [
+              {
+                "answerText": "harms for a group of people",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "harm to one person",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "harms for the majority of people",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The five main types of harms include",
+            "answerOptions": [
+              {
+                "answerText": "allocation, quality of service, stereotyping, denigration, and over- or under- representation",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "elocation, quality of service, stereotyping, denigration, and over- or under- representation ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "allocation, quality of service, stereophonics, denigration, and over- or under- representation ",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 6,
+        "title": "Fairness and Machine Learning: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Unfairness in a model can be caused by",
+            "answerOptions": [
+              {
+                "answerText": "over reliance on historical data",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "under reliance on historical data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "too closely aligning to historical data",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "To mitigate unfairness, you can",
+            "answerOptions": [
+              {
+                "answerText": "identify harms and affected groups",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "define fairness metrics",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Fairlearn is a package that can",
+            "answerOptions": [
+              {
+                "answerText": "compare multiple models by using fairness and performance metrics",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "choose the best model for your needs",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "help you decide what is fair and what is not",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 7,
+        "title": "Tools and Techniques: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "When building a model, you should:",
+            "answerOptions": [
+              {
+                "answerText": "prepare your data, then train your model",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "choose a training method, then prepare your data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "tune parameters, then train your model",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Your data's ___ will impact the quality of your ML model",
+            "answerOptions": [
+              {
+                "answerText": "quantity",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "shape",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "A feature variable is:",
+            "answerOptions": [
+              {
+                "answerText": "a quality of your data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a measurable property of your data",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a row of your data",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 8,
+        "title": "Tools and Techniques: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "You should visualize your data because",
+            "answerOptions": [
+              {
+                "answerText": "you can discover outliers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "you can discover potential cause for bias",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Split your data into:",
+            "answerOptions": [
+              {
+                "answerText": "training and turing sets",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "training and test sets",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "validation and evaluation sets",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A common command to start the training process in various ML libraries is:",
+            "answerOptions": [
+              {
+                "answerText": "model.travel",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.train",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.fit",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 9,
+        "title": "Introduction to Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which of these variables is a numeric variable?",
+            "answerOptions": [
+              {
+                "answerText": "Height",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Gender",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hair Color",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these variables is a categorical variable?",
+            "answerOptions": [
+              {
+                "answerText": "Heart Rate",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Blood Type",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Weight",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these problems is a Regression analysis-based problem?",
+            "answerOptions": [
+              {
+                "answerText": "Predicting the final exam marks of a student",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Predicting the blood type of a person",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Predicting whether an email is spam or not",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 10,
+        "title": "Introduction to Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "If your Machine Learning model's training accuracy is 95 % and the testing accuracy is 30 %, then what type of condition it is called?",
+            "answerOptions": [
+              {
+                "answerText": "Overfitting",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Underfitting",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Double Fitting",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The process of identifying significant features from a set of features is called:",
+            "answerOptions": [
+              {
+                "answerText": "Feature Extraction",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Feature Dimensionality Reduction",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Feature Selection",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "The process of splitting a dataset into a certain ratio of training and testing dataset using Scikit Learn's 'train_test_split()' method/function is called:",
+            "answerOptions": [
+              {
+                "answerText": "Cross-Validation",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hold-Out Validation",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Leave one out Validation",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 11,
+        "title": "Prepare and Visualize Data for Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which of these Python modules is used to plot the visualization of data?",
+            "answerOptions": [
+              {
+                "answerText": "Numpy",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Scikit-learn",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Matplotlib",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "If you want to understand the spread or the other characteristics of data points of your dataset, then perform:",
+            "answerOptions": [
+              {
+                "answerText": "Data Visualization",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Data Preprocessing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Train Test Split",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these is a part of the Data Visualization step in a Machine Learning project?",
+            "answerOptions": [
+              {
+                "answerText": "Incorporating a certain Machine Learning algorithm",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Creating a pictorial representation of data using different plotting methods",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Normalizing the values of a dataset",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 12,
+        "title": "Prepare and Visualize Data for Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which of these code snippets is correct based on this lesson, if you want to check for the presence of missing values in your dataset? Suppose the dataset is stored in a variable named 'dataset' which is a Pandas DataFrame object.",
+            "answerOptions": [
+              {
+                "answerText": "dataset.isnull().sum()",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "findMissing(dataset)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sum(null(dataset))",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these plotting methods is useful when you would like to understand the spread of different groups of datapoints from your dataset?",
+            "answerOptions": [
+              {
+                "answerText": "Scatter Plot",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Line Plot",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Bar Plot",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What can Data Visualization NOT tell you?",
+            "answerOptions": [
+              {
+                "answerText": "Relationships among datapoints",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "The source from where the dataset is collected",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Finding the presence of outliers in the dataset",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 13,
+        "title": "Linear and Polynomial Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Matplotlib is a ",
+            "answerOptions": [
+              {
+                "answerText": "drawing library",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "data visualization library",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "lending library",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Linear Regression uses the following to plot relationships between variables",
+            "answerOptions": [
+              {
+                "answerText": "a straight line",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a circle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a curve",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A good Linear Regression model has a ___ Correlation Coefficient",
+            "answerOptions": [
+              {
+                "answerText": "low",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "high",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "flat",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 14,
+        "title": "Linear and Polynomial Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "If your data is nonlinear, try a ___ type of Regression",
+            "answerOptions": [
+              {
+                "answerText": "linear",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "spherical",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "polynomial",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "These are all types of Regression methods",
+            "answerOptions": [
+              {
+                "answerText": "Falsestep, Ridge, Lasso and Elasticnet",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Stepwise, Ridge, Lasso and Elasticnet",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Stepwise, Ridge, Lariat and Elasticnet",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Least-Squares Regression means that all the datapoints surrounding the regression line are:",
+            "answerOptions": [
+              {
+                "answerText": "squared and then subtracted",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "multiplied",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "squared and then added up",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 15,
+        "title": "Logistic Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Use Logistic Regression to predict",
+            "answerOptions": [
+              {
+                "answerText": "whether an apple is ripe or not",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "how many tickets can be sold in a month",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "what color the sky will turn tomorrow at 6 PM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Types of Logistic Regression include",
+            "answerOptions": [
+              {
+                "answerText": "multinomial and cardinal",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "multinomial and ordinal",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "principal and ordinal",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Your data has weak correlations. The best type of Regression to use is:",
+            "answerOptions": [
+              {
+                "answerText": "Logistic",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Linear",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Cardinal",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 16,
+        "title": "Logistic Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Seaborn is a type of",
+            "answerOptions": [
+              {
+                "answerText": "data visualization library",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "mapping library",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "mathematical library",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A confusion matrix is also known as a:",
+            "answerOptions": [
+              {
+                "answerText": "error matrix",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "truth matrix",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "accuracy matrix",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A good model will have:",
+            "answerOptions": [
+              {
+                "answerText": "a large number of false positives and true negatives in its confusion matrix",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a large number of true positives and true negatives in its confusion matrix",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a large number of true positives and false negatives in its confusion matrix",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 17,
+        "title": "Build a Web App: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What does ONNX stand for?",
+            "answerOptions": [
+              {
+                "answerText": "Over Neural Network Exchange",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Open Neural Network Exchange",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Output Neural Network Exchange",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "How is Flask defined by its creators?",
+            "answerOptions": [
+              {
+                "answerText": "mini-framework",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "large-framework",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "micro-framework",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What does the Pickle module of Python do",
+            "answerOptions": [
+              {
+                "answerText": "Serializes a Python Object",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "De-serializes a Python Object",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Serializes and De-serializes a Python Object",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 18,
+        "title": "Build a Web App: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What are the tools we can use to host a pre-trained model on the web using Python?",
+            "answerOptions": [
+              {
+                "answerText": "Flask",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "TensorFlow.js",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "onnx.js",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What does SaaS stand for?",
+            "answerOptions": [
+              {
+                "answerText": "System as a Service",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Software as a Service",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Security as a Service",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What does Scikit-learn's LabelEncoder library do?",
+            "answerOptions": [
+              {
+                "answerText": "Encodes data alphabetically",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Encodes data numerically",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Encodes data serially",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 19,
+        "title": "Classification 1: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Classification is a form of supervised learning that has a lot in common with",
+            "answerOptions": [
+              {
+                "answerText": "Time Series",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Regression techniques",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What question can classification help answer?",
+            "answerOptions": [
+              {
+                "answerText": "Is this email spam or not?",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Can pigs fly?",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "What is the meaning of life?",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the first step to using Classification techniques?",
+            "answerOptions": [
+              {
+                "answerText": "creating classes of a dataset",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "cleaning and balancing your data",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "assigning a data point to a group or outcome",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 20,
+        "title": "Classification 1: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is a multiclass question?",
+            "answerOptions": [
+              {
+                "answerText": "the task of classifying data points into multiple classes",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the task of classifying data points into one of several classes",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the task of cleaning data points in multiple ways",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "It's important to clean out recurrent or unhelpful data to help your classifiers solve your problem.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What's the best reason to balance your data?",
+            "answerOptions": [
+              {
+                "answerText": "Imbalanced data looks bad in visualizations",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Balancing your data yields better results because an ML model won't skew towards one class",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Balancing your data gives you more data points",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 21,
+        "title": "Classification 2: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Balanced, clean data yields the best classification results",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "How do you choose the right classifier?",
+            "answerOptions": [
+              {
+                "answerText": "Understand which classifiers work best for which scenarios",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Educated guess and check",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Classification is a type of",
+            "answerOptions": [
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Supervised Learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Programming language",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 22,
+        "title": "Classification 2: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is a 'solver'?",
+            "answerOptions": [
+              {
+                "answerText": "the person who double-checks your work",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the algorithm to use in the optimization problem",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a machine learning technique",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which classifier did we use in this lesson?",
+            "answerOptions": [
+              {
+                "answerText": "Logistic Regression",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Decision Trees",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "One-vs-All Multiclass",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "How do you know if the classification algorithm is working as expected?",
+            "answerOptions": [
+              {
+                "answerText": "By checking the accuracy of its predictions",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "By checking it against other algorithms",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "By looking at historical data for how good this algorithm is at solving similar problems",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 23,
+        "title": "Classification 3: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "A good initial classifier to try is:",
+            "answerOptions": [
+              {
+                "answerText": "Linear SVC",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "K-Means",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Logical SVC",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Regularization controls:",
+            "answerOptions": [
+              {
+                "answerText": "the influence of parameters",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the influence of training speed",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the influence of outliers",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "K-Neighbors classifier can be used for:",
+            "answerOptions": [
+              {
+                "answerText": "supervised learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "unsupervised learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 24,
+        "title": "Classification 3: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Support-Vector classifiers can be used for",
+            "answerOptions": [
+              {
+                "answerText": "classification",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Random Forest is a ___ type of classifier",
+            "answerOptions": [
+              {
+                "answerText": "Ensemble",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Dissemble",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Assemble",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Adaboost is known for:",
+            "answerOptions": [
+              {
+                "answerText": "focusing on the weights of incorrectly classified items",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "focusing on outliers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "focusing on incorrect data",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 25,
+        "title": "Classification 4: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Recommendation systems might be used for",
+            "answerOptions": [
+              {
+                "answerText": "Recommending a good restaurant",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Recommending fashions to try",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Embedding a model in a web app helps it to be offline-capable",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Onnx Runtime can be used for",
+            "answerOptions": [
+              {
+                "answerText": "Running models in a web app",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Training models",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hyperparameter tuning",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 26,
+        "title": "Classification 4: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Netron app helps you:",
+            "answerOptions": [
+              {
+                "answerText": "Visualize data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Visualize your model's structure",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Test your web app",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Convert your Scikit-learn model for use with Onnx using:",
+            "answerOptions": [
+              {
+                "answerText": "sklearn-app",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-web",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-onnx",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Using your model in a web app is called:",
+            "answerOptions": [
+              {
+                "answerText": "inference",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "interference",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "insurance",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 27,
+        "title": "Introduction to Clustering: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "A real-life example of clustering would be",
+            "answerOptions": [
+              {
+                "answerText": "Setting the dinner table",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Sorting the laundry",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Grocery shopping",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Clustering techniques can be used in these industries",
+            "answerOptions": [
+              {
+                "answerText": "banking",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "e-commerce",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Clustering is a type of:",
+            "answerOptions": [
+              {
+                "answerText": "supervised learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "unsupervised learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "reinforcement learning",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 28,
+        "title": "Introduction to Clustering: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Euclidean geometry is arranged along",
+            "answerOptions": [
+              {
+                "answerText": "planes",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "curves",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "spheres",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The density of your clustering data is related to its",
+            "answerOptions": [
+              {
+                "answerText": "noise",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "depth",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "validity",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The best-known clustering algorithm is",
+            "answerOptions": [
+              {
+                "answerText": "k-means",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "k-middle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "k-mart",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 29,
+        "title": "K-Means Clustering: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "K-Means is derived from:",
+            "answerOptions": [
+              {
+                "answerText": "electrical engineering",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "signal processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "computational linguistics",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A good Silhouette score means:",
+            "answerOptions": [
+              {
+                "answerText": "clusters are well-separated and well-defined",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "there are few clusters",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "there are many clusters",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Variance is:",
+            "answerOptions": [
+              {
+                "answerText": "the average of the squared differences from the mean",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a problem for clustering if it becomes too high",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 30,
+        "title": "K-Means Clustering: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "A Voronoi diagram shows:",
+            "answerOptions": [
+              {
+                "answerText": "a cluster's variance",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a cluster's seed and its region",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a cluster's inertia",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Inertia is",
+            "answerOptions": [
+              {
+                "answerText": "a measure of how internally coherent clusters are",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a measure of how much clusters move",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a measure of cluster quality",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Using K-Means, you must first determine the value of 'k'",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 31,
+        "title": "Intro to NLP: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What does NLP stand for in these lessons?",
+            "answerOptions": [
+              {
+                "answerText": "Neural Language Processing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Natural Linguistic Processing",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Eliza was an early bot that acted as a computer",
+            "answerOptions": [
+              {
+                "answerText": "therapist",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "doctor",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "nurse",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Alan Turing's 'Turing Test' tried to determine if a computer was",
+            "answerOptions": [
+              {
+                "answerText": "indistinguishable from a human",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "thinking",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 32,
+        "title": "Intro to NLP: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Joseph Weizenbaum invented the bot",
+            "answerOptions": [
+              {
+                "answerText": "Elisha",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Eliza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Eloise",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A conversational bot gives output based on",
+            "answerOptions": [
+              {
+                "answerText": "Randomly choosing predefined choices",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Analyzing the input and using machine intelligence",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "How would you make the bot more effective?",
+            "answerOptions": [
+              {
+                "answerText": "By asking it more questions.",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "By feeding it more data and training it accordingly",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "The bot is dumb, it cannot learn :(",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 33,
+        "title": "NLP Tasks: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Tokenization",
+            "answerOptions": [
+              {
+                "answerText": "Splits text by means of punctuation",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Splits text into separate tokens (words)",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Splits text into phrases",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Embeddings",
+            "answerOptions": [
+              {
+                "answerText": "converts text data numerically so words can cluster",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "embeds words into phrases",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "embeds sentences into paragraphs",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Parts-of-Speech Tagging",
+            "answerOptions": [
+              {
+                "answerText": "divides sentences by their parts of speech",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "takes tokenized words and tags them by their part of speech",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "diagrams sentences",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 34,
+        "title": "NLP Tasks: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Build a dictionary of how often words reoccur using:",
+            "answerOptions": [
+              {
+                "answerText": "Word and Phrase Dictionary",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Word and Phrase Frequencies",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Word and Phrase Library",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "N-grams refer to",
+            "answerOptions": [
+              {
+                "answerText": "A text can be split into sequences of words of a set length",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "A word can be split into sequences of characters of a set length",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "A text can be split into paragraphs of a set length",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Sentiment analysis",
+            "answerOptions": [
+              {
+                "answerText": "analyzes a phrase for positivity or negativity",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "analyzes a phrase for sentimentality",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "analyzes a phrase for sadness",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 35,
+        "title": "NLP and Translation: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Naive translation",
+            "answerOptions": [
+              {
+                "answerText": "translates words only",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "translates sentence structure",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "translates sentiment",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A *corpus* of texts refers to",
+            "answerOptions": [
+              {
+                "answerText": "A small number of texts",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "A large number of texts",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "One standard text",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "If a ML model has enough human translations to build a model on, it can",
+            "answerOptions": [
+              {
+                "answerText": "abbreviate translations",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "standardize translations",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "improve the accuracy of translations",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 36,
+        "title": "NLP and Translation: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Underlying TextBlob's translation library is:",
+            "answerOptions": [
+              {
+                "answerText": "Google Translate",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "A custom ML model",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "To use `blob.translate` you need:",
+            "answerOptions": [
+              {
+                "answerText": "an internet connection",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a dictionary",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "JavaScript",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "To determine sentiment, an ML approach would be to:",
+            "answerOptions": [
+              {
+                "answerText": "apply Regression techniques to manually generated opinions and scores and look for patterns",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "apply NLP techniques to manually generated opinions and scores and look for patterns",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "apply Clustering techniques to manually generated opinions and scores and look for patterns",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 37,
+        "title": "NLP 4: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What information can we get from text that was written or spoken by a human?",
+            "answerOptions": [
+              {
+                "answerText": "patterns and frequencies",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sentiment and meaning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What is sentiment analysis?",
+            "answerOptions": [
+              {
+                "answerText": "a study of whether a family heirloom has sentimental value",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a method of systematically identifying, extracting, quantifying, and studying affective states and subjective information",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the ability to tell whether someone is sad or happy",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What question could be answered using a dataset of hotel reviews, Python, and sentiment analysis?",
+            "answerOptions": [
+              {
+                "answerText": "What are the most frequently used words and phrases in reviews?",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Which resort has the best pool?",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Is there valet parking at this hotel?",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 38,
+        "title": "NLP 4: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is the essence of NLP?",
+            "answerOptions": [
+              {
+                "answerText": "categorizing human language into happy or sad",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "interpreting meaning or sentiment without having to have a human do it",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "finding outliers in sentiment and examining them",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What are some things you might look for while cleaning data?",
+            "answerOptions": [
+              {
+                "answerText": "characters in other languages",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "blank rows or columns",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "It is important to understand your data and its foibles before performing operations on it.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 39,
+        "title": "NLP 5: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Why is it important to clean data before analyzing it?",
+            "answerOptions": [
+              {
+                "answerText": "Some columns might have missing or incorrect data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Messy data can lead to false conclusions about the dataset",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What is one example of a strategy for cleaning data?",
+            "answerOptions": [
+              {
+                "answerText": "removing columns/rows that aren't useful for answering a specific question",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "getting rid of verified values that don't fit your hypothesis",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "moving the outliers to a separate table and running the calculations for that table to see if they match",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "It can be useful to categorize data using a Tag column.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 40,
+        "title": "NLP 5: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is the goal of the dataset?",
+            "answerOptions": [
+              {
+                "answerText": "to see how many negative and positive reviews there are for hotels across the world",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "to add sentiment and columns that will help you choose the best hotel",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "to analyze why people leave specific reviews",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What are stop words?",
+            "answerOptions": [
+              {
+                "answerText": "common English words that do not change the sentiment of a sentence",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "words that you can remove to speed up sentiment analysis",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "To test the sentiment analysis, make sure it matches the reviewer's score for the same review.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 41,
+        "title": "Intro to Time Series: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Time Series Forecasting is useful in",
+            "answerOptions": [
+              {
+                "answerText": "determining future costs",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "predicting future pricing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "A time series is a sequence taken at:",
+            "answerOptions": [
+              {
+                "answerText": "successive equally spaced points in space",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "successive equally spaced points in time",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "successive equally spaced points in space and time",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Time series can be used in:",
+            "answerOptions": [
+              {
+                "answerText": "earthquake prediction",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "computer vision",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "color analysis",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 42,
+        "title": "Intro to Time Series: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Time series trends are",
+            "answerOptions": [
+              {
+                "answerText": "Measurable increases and decreases over time",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Quantifying decreases over time",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Gaps between increases and decreases over time",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Outliers are",
+            "answerOptions": [
+              {
+                "answerText": "points close to standard data variance",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "points far away from standard data variance",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "points within standard data variance",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Time Series Forecasting is most useful for",
+            "answerOptions": [
+              {
+                "answerText": "Econometrics",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "History",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Libraries",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 43,
+        "title": "Time Series ARIMA: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "ARIMA stands for",
+            "answerOptions": [
+              {
+                "answerText": "AutoRegressive Integral Moving Average",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Average",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Stationarity refers to",
+            "answerOptions": [
+              {
+                "answerText": "data whose attributes does not change when shifted in time",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "data whose distribution does not change when shifted in time",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "data whose distribution changes when shifted in time",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Differencing",
+            "answerOptions": [
+              {
+                "answerText": "stabilizes trend and seasonality",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "exacerbates trend and seasonality",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "eliminates trend and seasonality",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 44,
+        "title": "Time Series ARIMA: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "ARIMA is used to make a model fit the special form of time series data",
+            "answerOptions": [
+              {
+                "answerText": "as flat as possible",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "as closely as possible",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "via scatterplots",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Use SARIMAX to",
+            "answerOptions": [
+              {
+                "answerText": "manage seasonal ARIMA models",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "manage special ARIMA models",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "manage statistical ARIMA models",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "'Walk-Forward' validation involves",
+            "answerOptions": [
+              {
+                "answerText": "re-evaluating a model progressively as it is validated",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "re-training a model progressively as it is validated",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "re-configuring a model progressively as it is validated",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 45,
+        "title": "Reinforcement 1: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is reinforcement learning?",
+            "answerOptions": [
+              {
+                "answerText": "teaching someone something over and over again until they understand",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a learning technique that deciphers the optimal behavior of an agent in some environment by running many experiments",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "understanding how to run multiple experiments at once",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is a policy?",
+            "answerOptions": [
+              {
+                "answerText": "a function that returns the action at any given state",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a document that tells you whether or not you can return an item",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a function that is used for a random purpose",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A reward function returns a score for each state of an environment.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 46,
+        "title": "Reinforcement 1: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is Q-Learning?",
+            "answerOptions": [
+              {
+                "answerText": "a mechanism for recording the 'goodness' of each state",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "an algorithm where the policy is defined by a Q-Table",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "For what values does a Q-Table correspond to the random walk policy?",
+            "answerOptions": [
+              {
+                "answerText": "all equal values",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "-0.25",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "all different values",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "It was better to use exploration than exploitation during the learning process in our lesson.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 47,
+        "title": "Reinforcement 2: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Chess and Go are games with continuous states.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the CartPole problem?",
+            "answerOptions": [
+              {
+                "answerText": "a process for eliminating outliers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a method for optimizing your shopping cart",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a simplified version of balancing",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What tool can we use to play out different scenarios of potential states in a game?",
+            "answerOptions": [
+              {
+                "answerText": "guess and check",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "simulation environments",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "state transition testing",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 48,
+        "title": "Reinforcement 2: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Where do we define all possible actions in an environment?",
+            "answerOptions": [
+              {
+                "answerText": "methods",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "action space",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "action list",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What pair did we use as the dictionary key-value?",
+            "answerOptions": [
+              {
+                "answerText": "(state, action) as the key, Q-Table entry as the value",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "state as the key, action as the value",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the value of the qvalues function as the key, action as the value",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What are the hyperparameters we used during Q-Learning?",
+            "answerOptions": [
+              {
+                "answerText": "q-table value, current reward, random action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "learning rate, discount factor, exploration/exploitation factor",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "cumulative rewards, learning rate, exploration factor",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 49,
+        "title": "Real World Applications: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What's an example of an ML application in the Finance industry?",
+            "answerOptions": [
+              {
+                "answerText": "Personalizing the customer journey using NLP",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Wealth management using linear regression",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Energy management using Time Series",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What ML technique can hospitals use to manage readmission?",
+            "answerOptions": [
+              {
+                "answerText": "Clustering",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Time Series",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is an example of using Time Series for energy management?",
+            "answerOptions": [
+              {
+                "answerText": "Motion sensing animals",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Smart parking meters",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Tracking forest fires",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 50,
+        "title": "Real World Applications: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which ML technique can be used to detect credit card fraud?",
+            "answerOptions": [
+              {
+                "answerText": "Regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Clustering",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which ML technique is exemplified in forest management?",
+            "answerOptions": [
+              {
+                "answerText": "Reinforcement Learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Time Series",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What's an example of an ML application in the Health Care industry?",
+            "answerOptions": [
+              {
+                "answerText": "Predicting student behavior using regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Managing clinical trials using classifiers",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Motion sensing of animals using classifiers",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  }
 ]
diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
new file mode 100644
index 000000000..9b946ab57
--- /dev/null
+++ b/quiz-app/src/assets/translations/fr.json
@@ -0,0 +1,2811 @@
+[
+							{
+    "title": "Machine Learning pour les Débutants: Quiz",
+    "complete": "Félicitations, vous avez terminé le quiz!",
+    "error": "Désolé, essayez à nouveau",
+    "quizzes": [
+							{
+            "id": 1,
+            "title": "Introduction au machine learning: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les applications de machine learning sont toutes autour de nous",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est la différence technique entre le ml classique et le deep learning?",
+                    "answerOptions": [
+							{
+							"answerText": "ML classique a été inventé en premier",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'utilisation de réseaux de neurones",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Le deep learning est utilisé dans les robots",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pourquoi une entreprise pourrait-elle vouloir utiliser des stratégies ML?",
+                    "answerOptions": [
+							{
+							"answerText": "Pour automatiser la résolution de problèmes multidimensionnels",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Pour personnaliser une expérience de magasinage basée sur le type de client",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 2,
+            "title": "Introduction au machine learning: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Les algorithmes de machine learning sont destinés à simuler",
+                    "answerOptions": [
+							{
+							"answerText": "Des machines intelligentes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Le cerveau humain",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Des orangutans",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce qu'un exemple de technique classique de ML?",
+                    "answerOptions": [
+							{
+							"answerText": "Le traitement des langues naturelles",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Le deep learning",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Des neural networks",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pourquoi tout le monde devrait-il apprendre les bases du ML?",
+                    "answerOptions": [
+							{
+							"answerText": "L'apprentissage ml est amusant et accessible à tout le monde",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les stratégies ML sont utilisées dans de nombreuses industries et domaines",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 3,
+            "title": "Historique du machine learning: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Quand approximativement le terme 'intelligence artificielle' a-t-il été inventé ?",
+                    "answerOptions": [
+							{
+							"answerText": "1980s",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "années 1950",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "années 1930",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qui était l'un des premiers pionniers du machine learning?",
+                    "answerOptions": [
+							{
+							"answerText": "Alan Turing",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bill Gates",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Shakey the Robot",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est l'une des raisons pour lesquelles l'avancement de l'AI a ralenti dans les années 1970?",
+                    "answerOptions": [
+							{
+							"answerText": "Puissance de calcul limitée",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Pas assez d'ingénieurs qualifiés",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Conflits entre pays",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 4,
+            "title": "Historique du machine learning: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce qu'un exemple de système d'IA \" Scruffy \" AI?",
+                    "answerOptions": [
+							{
+							"answerText": "ELIZA",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "HACKML",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "SSYSTEM",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est l'exemple d'une technologie qui a été développée pendant les « années d'or » ?",
+                    "answerOptions": [
+							{
+							"answerText": "Blocks World",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Jibo",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Robot Dogs",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel événement était fondé sur la création et l'expansion du domaine de l'intelligence artificielle?",
+                    "answerOptions": [
+							{
+							"answerText": "Turing Test",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Projet de recherche d'été de Dartmouth",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "AI Winter",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 5,
+            "title": "L'équité et le machine learning: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "L'injustice dans le machine learning peut arriver",
+                    "answerOptions": [
+							{
+							"answerText": "Intentionnellement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Involontairement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le terme \" injustice \" en ml connotes:",
+                    "answerOptions": [
+							{
+							"answerText": "Préjudices pour un groupe de personnees",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "préjudice à une personne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Préjudices pour la majorité des gens",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les cinq principaux types de préjudices incluent",
+                    "answerOptions": [
+							{
+							"answerText": "Allocation, qualité de service, stéréotypage, dénigration et sous-représentation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Elocation, qualité de service, stéréotypage, dénigration et sous-représentation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Allocation, qualité de service, stéréophonie, dénigration et sous-représentation",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 6,
+            "title": "L'équité et le machine learning: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "L'injustice dans un modèle peut être causée par",
+                    "answerOptions": [
+							{
+							"answerText": "Dépendance excessive de données historiques",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "sous-dépendance sur les données historiques",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Alignement trop étroit sur les données historiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour atténuer l'injustice, vous pouvez",
+                    "answerOptions": [
+							{
+							"answerText": "Identifier les préjudices et les groupes affectés",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Définir les métriques d'équité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Fairlearn est un paquet qui peut",
+                    "answerOptions": [
+							{
+							"answerText": "Comparer plusieurs modèles en utilisant des métriques d'équité et de performance",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Choisissez le meilleur modèle pour vos besoins",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Vous aider à décider de ce qui est juste et ce qui ne l'est pas",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 7,
+            "title": "Outils et techniques: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Lors de la construction d'un modèle, vous devriez:",
+                    "answerOptions": [
+							{
+							"answerText": "Préparez vos données, puis formez votre modèle",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Choisissez une méthode de formation, puis préparez vos données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Tune Paramètres, puis formez votre modèle",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Vos données ___ vont avoir une incidence sur la qualité de votre modèle ML",
+                    "answerOptions": [
+							{
+							"answerText": "Quantité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Forme",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une variable de fonctionnalité est la suivante:",
+                    "answerOptions": [
+							{
+							"answerText": "une qualité de vos données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une propriété mesurable de vos données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Une ligne de vos données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 8,
+            "title": "Outils et techniques: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Vous devez visualiser vos données car",
+                    "answerOptions": [
+							{
+							"answerText": "Vous pouvez découvrir des valeurs aberrantes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Vous pouvez découvrir une cause potentielle de biais",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Sélectionnez vos données en:",
+                    "answerOptions": [
+							{
+							"answerText": "Entraînement et ensembles de Turing",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Entraînement et ensembles de test",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Ensembles de validation et d'évaluation",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une commande commune de démarrer le processus de formation dans diverses bibliothèques ML est la suivante:",
+                    "answerOptions": [
+							{
+							"answerText": "Model.travel",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Model.train",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Model.fit",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 9,
+            "title": "Introduction à la régression: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Laquelle de ces variables est une variable numérique?",
+                    "answerOptions": [
+							{
+							"answerText": "Hauteur",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Genre",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Couleur des cheveux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Laquelle de ces variables est une variable catégorique?",
+                    "answerOptions": [
+							{
+							"answerText": "rythme cardiaque",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Type de sang",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Poids",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Lequel de ces problèmes est un problème basé sur l'analyse de régression?",
+                    "answerOptions": [
+							{
+							"answerText": "Prédire les marques d'examen final d'un étudiant",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Prédire le type de sang d'une personne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Prédire si un email est spam ou non",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 10,
+            "title": "Introduction à la régression: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Si la précision de la formation du modèle d'apprentissage de votre machine est de 95% et que la précision des tests est de 30%, quel type de condition est appelé?",
+                    "answerOptions": [
+							{
+							"answerText": "Surapprentissage",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Insuffisance",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Double ajustement",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le processus d'identification des fonctionnalités significatives d'un ensemble de fonctionnalités est appelé:",
+                    "answerOptions": [
+							{
+							"answerText": "Extraction de fonctionnalités",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Réduction de la dimensionnalité de fonctionnalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sélection de fonctionnalités",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le processus de division d'un ensemble de données en un certain rapport d'ensemble de données d'entraînement et de test à l'aide de la méthode/fonction 'train_test_split ()' de Scikit Learn est appelé une:",
+                    "answerOptions": [
+							{
+							"answerText": "Validation croisée",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Validation d'attentn",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Validation \"Oubliez-en un\" ",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 11,
+            "title": "Préparer et visualiser des données pour la régression: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Lequel de ces modules Python est utilisé pour tracer la visualisation des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Numpy",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Scikit-learn",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Matplotlib",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Si vous souhaitez comprendre la propagation ou les autres caractéristiques des points de données de votre ensemble de données, alors effectuez:",
+                    "answerOptions": [
+							{
+							"answerText": "Une visualisation des données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un pré-traitement des données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un Train Test Splitn",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Lequel d'entre eux fait partie de l'étape de visualisation des données dans un projet de machine learning?",
+                    "answerOptions": [
+							{
+							"answerText": "Intégrer un algorithme d'apprentissage de certains machines",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Créer une représentation picturale des données à l'aide de différentes méthodes de tracé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Normaliser les valeurs d'un jeu de données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 12,
+            "title": "Préparer et visualiser des données pour la régression: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Lequel de ces extraits de code est correct d'après cette leçon, si vous souhaitez vérifier la présence de valeurs manquantes dans votre ensemble de données ? Supposons que l'ensemble de données soit stocké dans une variable nommée \"ensemble de données\", qui est un objet Pandas DataFrame.",
+                    "answerOptions": [
+                        {
+                            "answerText": "dataset.isnull().sum()",
+                            "isCorrect": "true"
+                        },
+                        {
+                            "answerText": "findMissing(dataset)",
+                            "isCorrect": "false"
+                        },
+                        {
+                            "answerText": "sum(null(dataset))",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Laquelle de ces méthodes de traçage est utile lorsque vous souhaitez comprendre la propagation de différents groupes de fichiers de données de votre jeu de données?",
+                    "answerOptions": [
+							{
+							"answerText": "Nuage de pointsn",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Graphique linéaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Graphique à barres",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Que peut ne pas vous dire la visualisation des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Relations entre DataPoints",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La source de l'endroit où le jeu de données est collecté",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Trouver la présence de valeurs aberrantes dans l'ensemble de données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 13,
+            "title": "Régression linéaire et polynomiale: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Matplotlib est une",
+                    "answerOptions": [
+							{
+							"answerText": "Bibliothèque de dessin",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Bibliothèque de visualisation de données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bibliothèque de prêt",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La régression linéaire utilise ce qui suit pour tracer des relations entre variables",
+                    "answerOptions": [
+							{
+							"answerText": "Une ligne droite",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un cercle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une courbe",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bon modèle de régression linéaire a un coefficient de corrélation ___",
+                    "answerOptions": [
+							{
+							"answerText": "Bas",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Elevé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Plat",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 14,
+            "title": "Régression linéaire et polynomiale: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Si vos données sont non linéaires, essayez un type ___  de régression",
+                    "answerOptions": [
+							{
+							"answerText": "linéaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "sphérique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "polynômial",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Ce sont tous types de méthodes de régression",
+                    "answerOptions": [
+							{
+							"answerText": "Falsestep, Ridge, Lasso et Elasticnet",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Stepwise, Ridge, Lasso et Elasticnet",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Stepwise, Ridge, Lariat et Elasticnet",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La régression des moindres carrés signifie que toutes les données de données entourant la ligne de régression sont:",
+                    "answerOptions": [
+							{
+							"answerText": "carré puis soustrait",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "multiplié",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "carré puis ajouté",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 15,
+            "title": "Régression logistique: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Utilisez la régression logistique à prédire",
+                    "answerOptions": [
+							{
+							"answerText": "Si une pomme est mûre ou non",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Combien de billets peuvent être vendus dans un mois",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "De quelle couleur le ciel tournera demain à 18 heures",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les types de régression logistique incluent",
+                    "answerOptions": [
+							{
+							"answerText": "multinomial et cardinal",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "multinomial et ordinal",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Principal et ordinal",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Vos données ont des corrélations faibles. Le meilleur type de régression à utiliser est:",
+                    "answerOptions": [
+							{
+							"answerText": "Logistique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "linéaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "cardinale",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 16,
+            "title": "Régression logistique: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Seaborn est un type de",
+                    "answerOptions": [
+							{
+							"answerText": "Bibliothèque de visualisation de données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bibliothèque de mappage",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Bibliothèque mathématique",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une matrice de confusion est également connue sous le nom de:",
+                    "answerOptions": [
+							{
+							"answerText": "Matrice d'erreur",
+                            "isCorrect": "true"
+                        },
+                        {
+                            "answerText": "Matrice de vérité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Matrice de précision",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bon modèle aura:",
+                    "answerOptions": [
+							{
+							"answerText": "Un grand nombre de faux positifs et de vrais négatifs dans sa matrice de confusion",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un grand nombre de vrais positifs et vrais négatifs dans sa matrice de confusion",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un grand nombre de vrais positifs et de faux négatifs dans sa matrice de confusion",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }, {
+            "id": 17,
+            "title": "Construire une application Web: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce que ONNX signifie?",
+                    "answerOptions": [
+							{
+							"answerText": "Over Neural Network Exchange",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Open Neural Network Exchange",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Output Neural Network Exchange",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment Flask est-il défini par ses créateurs?",
+                    "answerOptions": [
+							{
+							"answerText": "mini-framework",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "grand-framework",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "micro-framework",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Que fait le module de cornichon de Python",
+                    "answerOptions": [
+							{
+							"answerText": "Serialiser un objet Python",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Dé-sérialiser un objet Python",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sérialiser et Dé-sérialiser un objet Python",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 18,
+            "title": "Construire une application Web: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quels sont les outils que nous pouvons utiliser pour héberger un modèle pré-formé sur le Web à l'aide de Python?",
+                    "answerOptions": [
+							{
+							"answerText": "Flask",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Tensorflow.js",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "onnx.JS",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce que SaaS signifie?",
+                    "answerOptions": [
+							{
+							"answerText": "Système en tant que service",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Logiciel en tant que service",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Sécurité en tant que service",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce que la bibliothèque de labelencoder de Scikit-apprendre?",
+                    "answerOptions": [
+							{
+							"answerText": "Encode les données par ordre alphabétique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Encode les données numériquement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Encode des données en série",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 19,
+            "title": "Classification 1: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "La classification est une forme d'apprentissage supervisé qui a beaucoup en commun avec",
+                    "answerOptions": [
+							{
+							"answerText": "Série chronologique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Techniques de régression",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle question peut aider la classification à répondre?",
+                    "answerOptions": [
+							{
+							"answerText": "Est-ce que ce courrier électronique ou pas?",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Les cochons peuvent voler?",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Quel est le sens de la vie?",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est la première étape pour utiliser des techniques de classification?",
+                    "answerOptions": [
+							{
+							"answerText": "Création de cours d'un jeu de données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Nettoyer et équilibrer vos données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Affectation d'un point de données à un groupe ou à un résultat",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 20,
+            "title": "Classification 1: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce qu'une question multiclasse?",
+                    "answerOptions": [
+							{
+							"answerText": "La tâche de classer les points de données dans plusieurs classes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La tâche de classifier les points de données dans l'une des plusieurs classes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La tâche de nettoyer les points de données de plusieurs manières",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il est important de nettoyer des données récurrentes ou inutiles pour aider vos classificateurs à résoudre votre problème.",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est la meilleure raison d'équilibrer vos données?",
+                    "answerOptions": [
+							{
+							"answerText": "Les données déséquilibrées ont l'air mauvais dans les visualisations",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'équilibrage de vos données donne des résultats meilleurs, car un modèle ML n'enfraigne pas vers une classe",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'équilibrage de vos données vous donne plus de points de données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 21,
+            "title": "Classification 2: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les données équilibrées et propres ont produit les meilleurs résultats de la classification",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment choisissez-vous le bon classificateur?",
+                    "answerOptions": [
+							{
+							"answerText": "Comprend quel classificateurs fonctionnent le mieux pour quels scénarios",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Devineuse éduquée et chèque",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La classification est un type de",
+                    "answerOptions": [
+							{
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Apprentissage supervisé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Langage de programmation",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 22,
+            "title": "Classification 2: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce qu'un \"solveur\" ?",
+                    "answerOptions": [
+							{
+							"answerText": "La personne qui vérifie votre travail",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'algorithme à utiliser dans le problème d'optimisation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Une technique de machine learning",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel classificateur avons-nous utilisé dans cette leçon?",
+                    "answerOptions": [
+							{
+							"answerText": "Régression logistique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Arbres de décision",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Multiclasse un-contre-tous",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment savez-vous si l'algorithme de classification fonctionne comme prévu?",
+                    "answerOptions": [
+							{
+							"answerText": "En vérifiant la précision de ses prévisions",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "En le contrôlant contre d'autres algorithmes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "En regardant des données historiques pour la qualité de cet algorithme de résoudre des problèmes similaires",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 23,
+            "title": "Classification 3: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Un bon classificateur initial à essayer est:",
+                    "answerOptions": [
+							{
+							"answerText": "SVC linéaire",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "K-Means",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "SVC logique",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Contrôles de régularisation:",
+                    "answerOptions": [
+							{
+							"answerText": "L'influence des paramètres",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'influence de la vitesse de formation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'influence des valeurs aberrantes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le classificateur K-voisins peut être utilisé pour:",
+                    "answerOptions": [
+							{
+							"answerText": "Apprentissage supervisé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'apprentissage non supervisé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 24,
+            "title": "Classification 3: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Les classificateurs de support-vectoriel peuvent être utilisés pour",
+                    "answerOptions": [
+							{
+							"answerText": "La classification",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La régression",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Forêt aléatoire est un type de classificateur ___",
+                    "answerOptions": [
+							{
+							"answerText": "Ensembliste",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Disensembliste",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Assembliste",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Adaboost est connu pour:",
+                    "answerOptions": [
+							{
+							"answerText": "Se concentrer sur les poids des éléments incorrectement classifiés",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Se concentrer sur des valeurs aberrantes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Se concentrer sur des données incorrectes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 25,
+            "title": "Classification 4: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les systèmes de recommandation peuvent être utilisés pour",
+                    "answerOptions": [
+							{
+							"answerText": "Recommander un bon restaurant",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Recommander des modes à essayer",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'intégration d'un modèle dans une application Web l'aide à être compatible hors ligne",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Onnx Runtime peut être utilisé pour",
+                    "answerOptions": [
+							{
+							"answerText": "Exécution de modèles dans une application Web",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Modèles de formation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Réglage des hyperparamètres",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 26,
+            "title": "Classification 4: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "L'application Netron vous aide:",
+                    "answerOptions": [
+							{
+							"answerText": "Visualiser les données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Visualisez la structure de votre modèle",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Testez votre application Web",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Convertissez votre modèle Scikit-learnL pour une utilisation avec Onnx en utilisant:",
+                    "answerOptions": [
+							{
+							"answerText": "Sklearn-app",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sklearn-web",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sklearn-onnX",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'utilisation de votre modèle dans une application Web s'appelle:",
+                    "answerOptions": [
+							{
+							"answerText": "Inférence",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Interférence",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Assurance",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 27,
+            "title": "Introduction au Clustering (regroupement): Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Un exemple de vie réel de regroupement serait",
+                    "answerOptions": [
+							{
+							"answerText": "Définir la table du dîner",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Tri du linge",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Shopping de l'épicerie",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les techniques de clustering peuvent être utilisées dans ces industries",
+                    "answerOptions": [
+							{
+							"answerText": "Banking",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "e-commerce",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La Clustering est un type :",
+                    "answerOptions": [
+							{
+							"answerText": "D'apprentissage supervisé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "D'apprentissage non supervisé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "D'apprentissage de renforcement",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 28,
+            "title": "Introduction au Clustering (regroupement): Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "La géométrie Euclidienne est disposée le long",
+                    "answerOptions": [
+							{
+							"answerText": "De plans",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "De courbes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "De sphères",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La densité de vos données de clustering est liée à son / sa",
+                    "answerOptions": [
+							{
+							"answerText": "Bruit",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Profondeur",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Validité",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'algorithme de regroupement le plus connu est",
+                    "answerOptions": [
+							{
+							"answerText": "k-means",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "K-middle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "K-mart",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 29,
+            "title": "K-Means Clustering: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "K-Means est dérivé de:",
+                    "answerOptions": [
+							{
+							"answerText": "Génie électrique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Traitement du signal",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Linguistics informatiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bon score de silhouette signifie:",
+                    "answerOptions": [
+							{
+							"answerText": "Les grappes sont bien séparées et bien définies",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Il y a peu de grappes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Il y a beaucoup de clusters",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La variance est:",
+                    "answerOptions": [
+							{
+							"answerText": "La moyenne des différences carrées de la moyenne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un problème de regroupement s'il devient trop élevé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 30,
+            "title": "K-Means Clustering: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Un diagramme de Voronoi montre:",
+                    "answerOptions": [
+							{
+							"answerText": "Une variance d'une cluster",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La graine d'une grappe et sa région",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'inertie d'une cluster",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'inertie est",
+                    "answerOptions": [
+							{
+							"answerText": "Une mesure de la manière dont les clusters cohérents internes sont",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Une mesure de la quantité de grappes déplacées",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une mesure de la qualité des grappes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "en utilisant k-moyen, vous devez d'abord déterminer la valeur de 'k'",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 31,
+            "title": "Intro aux NLP: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Que signifie NLP pour ces leçons?",
+                    "answerOptions": [
+							{
+							"answerText": "Neural Language Processing (Traitement des langues neurales)",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Natural Language Processing (Traitement des langues naturelles)",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Natural Linguistic Processing (Traitement linguistique naturel)",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Eliza était un bot précoce qui a agi comme un ordinateur",
+                    "answerOptions": [
+							{
+							"answerText": "Thérapeute",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Docteur",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Infirmière",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le test Turing d'Alan Turing a essayé de déterminer si un ordinateur était",
+                    "answerOptions": [
+							{
+							"answerText": "Indiscernable d'un humain",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Pensif",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 32,
+            "title": "Intro aux NLP: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Joseph Weizenbaum a inventé le bot",
+                    "answerOptions": [
+							{
+							"answerText": "Elisha",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Eliza",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Eloise",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bot conversationnel donne une sortie basée sur",
+                    "answerOptions": [
+							{
+							"answerText": "Un choix de choix prédéfinis au hasard",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Analyse de l'entrée et de l'utilisation de l'intelligence de la machine",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment feriez-vous pour que le bot soit plus efficace?",
+                    "answerOptions": [
+							{
+							"answerText": "En le demandant plus de questions.",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "En lui fournissant plus de données et en le formant en conséquence",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Le bot est stupide, il ne peut pas apprendre :(",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 33,
+            "title": "Tâches NLP: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "La tokenization",
+                    "answerOptions": [
+							{
+							"answerText": "Divise le texte au moyen de la ponctuation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Divise le texte en jetons séparés (mots)",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Divise le texte en phrases",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'Embeddings",
+                    "answerOptions": [
+							{
+							"answerText": "Convertit numériquement les données de texte afin que les mots puissent se classer",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Intégre des mots en phrases",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Intégre des phrases dans les paragraphes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le balisage des parties du discours (Parts-of-Speech Tagging)",
+                    "answerOptions": [
+							{
+							"answerText": "Divise les phrases en fonction de leurs parties du discours",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "prend les mots tokenisés et les marque selon leur partie du discours",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "schématise des phrases",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 34,
+            "title": "Tâches NLP: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Construisez un dictionnaire de la fréquence à laquelle les mots se reproduisent en utilisant:",
+                    "answerOptions": [
+							{
+							"answerText": "Dictionnaire de mots et d'expressions",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Fréquences de mots et de phrases",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bibliothèque de mots et de phrases",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "N-grams fait référence à",
+                    "answerOptions": [
+							{
+							"answerText": "Un texte pouvant être divisé en séquences de mots d'une longueur définie",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un mot pouvant être divisé en séquences de caractères d'une longueur de jeu",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un texte pouvant être divisé en paragraphes d'une longueur définie",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Analyse du sentiment",
+                    "answerOptions": [
+							{
+							"answerText": "analyse une phrase pour la positivité ou la négativité",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "analyse une phrase pour sentimentalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "analyse une phrase pour la tristesse",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 35,
+            "title": "NLP et traduction: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "La traduction naïve",
+                    "answerOptions": [
+							{
+							"answerText": "Traduit uniquement les mots",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Traduit la structure de la phrase",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Traduit le sentiment",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un *corpus* de textes fait référence à",
+                    "answerOptions": [
+							{
+							"answerText": "Un petit nombre de textes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un grand nombre de textes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un texte standard",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Si un modèle ML a suffisamment de traductions humaines pour construire un modèle, il peut",
+                    "answerOptions": [
+							{
+							"answerText": "Abréger des traductions",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Normaliser les traductions",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Améliorer la précision des traductions",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 36,
+            "title": "NLP et traduction: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "La bibliothèque de traduction de texte sous-jacente est:",
+                    "answerOptions": [
+							{
+							"answerText": "Google Translate",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bing",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un modèle ML personnalisé",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour utiliser `blob.translate` vous avez besoin de:",
+                    "answerOptions": [
+							{
+							"answerText": "Une connexion Internet",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un dictionnaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "JavaScript",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour déterminer un sentiment, une approche ML serait d':",
+                    "answerOptions": [
+							{
+							"answerText": "Appliquer des techniques de régression pour générer manuellement des opinions et des scores et rechercher des modèles",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Appliquer des techniques de PNL pour générer manuellement des opinions et des scores et rechercher des modèles",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Appliquer des techniques de regroupement pour des opinions et des scores générés manuellement et rechercher des modèles",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 37,
+            "title": "NLP 4: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Quelles informations pouvons-nous obtenir du texte écrit ou parlé par un humain?",
+                    "answerOptions": [
+							{
+							"answerText": "motifs et fréquences",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sentiment et signification",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce que l'analyse du sentiment?",
+                    "answerOptions": [
+							{
+							"answerText": "Une étude sur la question de savoir si un héritage de famille a une valeur sentimentale",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une méthode d'identification systématique, d'extraction, de quantification et d'étude des états affectifs et des informations subjectives",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La capacité de savoir si quelqu'un est triste ou heureux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle question pourrait être répondue à l'aide d'un jeu de données de critiques hôteliers, de python et d'analyse de sentiment?",
+                    "answerOptions": [
+							{
+							"answerText": "Quels sont les mots et expressions les plus fréquemment utilisés dans les critiques?",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Quel hôtel a la meilleure piscine?",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Y a-t-il un service de voiturier dans cet hôtel?",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 38,
+            "title": "NLP 4: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quelle est l'essence de la NLP?",
+                    "answerOptions": [
+							{
+							"answerText": "catégoriser la langue humaine en joyeuse ou triste",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Interprétation de sens ou de sentiment sans avoir un humain pour le faire",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Trouver des valeurs aberrantes dans le sentiment et les examiner",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelles sont certaines choses que vous pourriez rechercher lors du nettoyage des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Personnages dans d'autres langues",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Lignes vierges ou colonnes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il est important de comprendre votre donnée et ses faiblesses avant d'effectuer des opérations à ce sujet.",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 39,
+            "title": "NLP 5: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Pourquoi est-il important de nettoyer les données avant de l'analyser?",
+                    "answerOptions": [
+							{
+							"answerText": "Certaines colonnes pourraient avoir des données manquantes ou incorrectes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les données en désordre peuvent conduire à de fausses conclusions sur le jeu de données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est un exemple d'une stratégie de nettoyage des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Supprimer des colonnes / rangées qui ne sont pas utiles pour répondre à une question spécifique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Se débarrasser des valeurs vérifiées qui ne correspondent pas à votre hypothèse",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Déplacement des valeurs aberrantes vers une table séparée et exécutant les calculs de cette table pour voir s'ils correspondent",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il peut être utile de classer les données à l'aide d'une colonne Tag.",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 40,
+            "title": "NLP 5: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quel est l'objectif de l'ensemble de données?",
+                    "answerOptions": [
+							{
+							"answerText": "Voir combien de critiques négatives et positives il y a pour les hôtels à travers le monde",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Ajouter du sentiment et des colonnes qui vous aideront à choisir le meilleur hôtel",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Analyser pourquoi les gens laissent des critiques spécifiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quels sont les mots d'arrêt?",
+                    "answerOptions": [
+							{
+							"answerText": "Mots anglais communs qui ne changent pas le sentiment d'une phrase",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Mots que vous pouvez supprimer pour accélérer l'analyse du sentiment",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour tester l'analyse du sentiment, assurez-vous qu'il correspond au score du critique pour le même examen.",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 41,
+            "title": "Introduction aux Séries chronologiques (Time Series) : Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "La prévision de série chronologique est utile pour",
+                    "answerOptions": [
+							{
+							"answerText": "Déterminer les coûts futurs",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Prédire les prix futurs",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux à la fois",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une série chronologique est une séquence prise à:",
+                    "answerOptions": [
+							{
+							"answerText": "points successifs également espacés dans l'espace",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "points successifs également espacés dans le temps",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "points successifs également espacés dans l'espace et le temps",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La série chronologique peut être utilisée dans les cas de:",
+                    "answerOptions": [
+							{
+							"answerText": "Prévision de tremblement de terre",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Vision informatique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Analyse des couleurs",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 42,
+            "title": "Introduction aux séries chronologiques : Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Les tendances de série chronologique sont",
+                    "answerOptions": [
+							{
+							"answerText": "des augmentations et des diminutions mesurables au fil du temps",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La quantification des diminutions au fil du temps",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Des lacunes entre augmentations et diminution au fil du temps",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les valeurs aberrantes sont des",
+                    "answerOptions": [
+							{
+							"answerText": "Points proches de la variance de données standard",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Points loin de la variance de données standard",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Points dans la variance des données standard",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La prévision de séries chronologiques est utile pour",
+                    "answerOptions": [
+							{
+							"answerText": "L'économétrie",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'histoire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les bibliothèques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 43,
+            "title": "Les séries chronologiques ARIMA: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "ARIMA signifie",
+                    "answerOptions": [
+							{
+							"answerText": "AutoRegressive Integral Moving Average",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "AutoRegressive Integrated Moving Action",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "AutoRegressive Integrated Moving Average",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La stationnarité fait référence à",
+                    "answerOptions": [
+							{
+							"answerText": "Les données dont les attributs ne changent pas lors de la décalage",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les données dont la distribution ne change pas lors de la décalage de temps",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Les données dont la distribution change lors de la décalage",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La différenciation",
+                    "answerOptions": [
+							{
+							"answerText": "Stabilise la tendance et la saisonnalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Exacerbe la tendance et la saisonnalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Élimine la tendance et la saisonnalité",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 44,
+            "title": "Les séries chronologiques ARIMA: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Arima est utilisé pour créer un modèle adapté à la forme spéciale des données de la série chronologique",
+                    "answerOptions": [
+							{
+							"answerText": "aussi plat que possible",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "aussi étroitement que possible",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "via ScatterPlots",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Utilisez Sarimax pour",
+                    "answerOptions": [
+							{
+							"answerText": "Gérer les modèles d'ARIMA saisonniers",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Gérer des modèles spéciaux Arima",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Gérer les modèles statistiques ARIMA",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": " La validation « Walk-Forward » implique de",
+                    "answerOptions": [
+							{
+							"answerText": "Réévaluer un modèle progressivement tel qu'il est validé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Re-entraîner un modèle progressivement tel qu'il est validé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Re-configurer un modèle progressivement tel qu'il est validé",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }, {
+            "id": 45,
+            "title": "Renforcement 1: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce que l'apprentissage du renforcement?",
+                    "answerOptions": [
+							{
+							"answerText": "Enseigner à quelqu'un quelque chose encore et encore jusqu'à ce qu'ils comprennent",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une technique d'apprentissage qui déchiffre le comportement optimal d'un agent dans certains environnements en exécutant de nombreuses expériences",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Comprendre comment exécuter plusieurs expériences à la fois",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce qu'une politique?",
+                    "answerOptions": [
+							{
+							"answerText": "une fonction qui renvoie l'action à tout état donné",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un document qui vous dit si vous pouvez renvoyer ou non un article",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une fonction utilisée à des fins aléatoires",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une fonction de récompense renvoie un score pour chaque état d'environnement.",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }, {
+            "id": 46,
+            "title": "Renforcement 1: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce que le Q-Learning?",
+                    "answerOptions": [
+							{
+							"answerText": "Un mécanisme d'enregistrement de la \"bonté\" de chaque État",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un algorithme où la politique est définie par une table Q",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour quelles valeurs une Q-Table correspond à la stratégie de marche aléatoire?",
+                    "answerOptions": [
+							{
+							"answerText": "toutes les valeurs égales",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "-0,25",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "toutes les valeurs différentes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il valait mieux utiliser l'exploration que l'exploitation pendant le processus d'apprentissage de notre leçon.",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 47,
+            "title": "Renforcement 2: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les échecs et le go sont des jeux avec des états continus",
+                    "answerOptions": [
+							{
+							"answerText": "Vrai",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est le problème CartPole ?",
+                    "answerOptions": [
+							{
+							"answerText": "Un processus d'élimination des valeurs aberrantes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une méthode d'optimisation de votre panier",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une version simplifiée d'équilibrage",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel outil pouvons-nous utiliser pour jouer à différents scénarios d'états potentiels dans un jeu?",
+                    "answerOptions": [
+							{
+							"answerText": "Devinez et chèque",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Environnements de simulation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Test de transition de l'état",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 48,
+            "title": "Renforcement 2: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Où définissons-nous toutes les actions possibles dans un environnement?",
+                    "answerOptions": [
+							{
+							"answerText": "Méthodes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "espace d'action",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Liste d'action",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle paire avons-nous utilisée comme valeur de la clé de dictionnaire?",
+                    "answerOptions": [
+							{
+							"answerText": "(état, action) comme clé, l'entrée Q-Table comme valeur",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'état comme clé, action en tant que valeur",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La valeur de la fonction QValues ​​est la clé, l'action en tant que valeur",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quels sont les hyperparamètres que nous avons utilisés pendant le Q-Learning?",
+                    "answerOptions": [
+							{
+							"answerText": "Valeur de la table Q, récompense actuelle, action aléatoire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Taux d'apprentissage, facteur de réduction, facteur d'exploration / d'exploitation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Récompenses cumulatives, taux d'apprentissage, facteur d'exploration",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 49,
+            "title": "Applications du monde réel: Quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Quel est un exemple d'application ML dans l'industrie des finances?",
+                    "answerOptions": [
+							{
+							"answerText": "Personnaliser le voyage client à l'aide de NLP",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Gestion de la richesse à l'aide de la régression linéaire",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Gestion de l'énergie à l'aide de séries chronologiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle technique ML peut utiliser les hôpitaux pour gérer la réadmission?",
+                    "answerOptions": [
+							{
+							"answerText": "Le Clustering (Regroupement)",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Les séries chronologiques",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Le NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est un exemple d'utilisation des séries chronologiques pour la gestion de l'énergie?",
+                    "answerOptions": [
+							{
+							"answerText": "Animaux de détection de mouvement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Parkings intelligents",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Suivi des incendies de forêt",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 50,
+            "title": "Applications du monde réel: Quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quelle technique ML peut être utilisée pour détecter la fraude par carte de crédit?",
+                    "answerOptions": [
+							{
+							"answerText": "régression",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Clustering",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle technique ML est illustrée dans la gestion forestière?",
+                    "answerOptions": [
+							{
+							"answerText": "Apprentissage du renforcement",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Série chronologique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est un exemple d'application ML dans l'industrie des soins de santé?",
+                    "answerOptions": [
+							{
+							"answerText": "Prédire le comportement des étudiants en utilisant la régression",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Gestion des essais cliniques à l'aide de classificateurs",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Sensation de mouvement des animaux utilisant des classificateurs",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}]
\ No newline at end of file
diff --git a/quiz-app/src/assets/translations/index.js b/quiz-app/src/assets/translations/index.js
index e4abf6eb9..e6fd21605 100644
--- a/quiz-app/src/assets/translations/index.js
+++ b/quiz-app/src/assets/translations/index.js
@@ -1,12 +1,18 @@
 // index.js
 import en from './en.json';
 import tr from './tr.json';
+import fr from './fr.json';
+import ja from './ja.json';
+import it from './it.json';
 
 //export const defaultLocale = 'en';
 
 const messages = {
 	en: en[0],
 	tr: tr[0],
+	fr: fr[0],
+	ja: ja[0],
+	it: it[0]
 };
 
 export default messages;
diff --git a/quiz-app/src/assets/translations/it.json b/quiz-app/src/assets/translations/it.json
new file mode 100644
index 000000000..581bc2532
--- /dev/null
+++ b/quiz-app/src/assets/translations/it.json
@@ -0,0 +1,2815 @@
+[
+  {
+    "title": "Machine Learning per principianti: Quiz",
+    "complete": "Congratulazioni, il quiz è stato completato!",
+    "error": "Mi dispiace, riprova",
+    "quizzes": [
+      {
+        "id": 1,
+        "title": "Introduzione a Machine Learning: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Applicazioni di machine learning sono tutte intorno a noi",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è la differenza tecnica tra ML classico e deep learning?",
+            "answerOptions": [
+              {
+                "answerText": "ML classico è stato inventato prima",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "l'uso di reti neurali",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "deep learning è usato nei robot",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Perché un'azienda potrebbe voler usare le strategie ML?",
+            "answerOptions": [
+              {
+                "answerText": "Per automatizzare la risoluzione dei problemi multidimensionali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Per personalizzare un'esperienza di acquisto in base al tipo di cliente",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 2,
+        "title": "Introduzione a Machine Learning: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Gli algoritmi di machine learning sono destinati a simulare",
+            "answerOptions": [
+              {
+                "answerText": "macchine intelligenti",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "il cervello umano",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "gli orangutan",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è un esempio di una tecnica ML classica?",
+            "answerOptions": [
+              {
+                "answerText": "elaborazione del linguaggio naturale",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "deep learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "reti neurali",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Perché tutti dovrebbero imparare le basi di ML?",
+            "answerOptions": [
+              {
+                "answerText": "L'apprendimento di ml è divertente e accessibile a tutti",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Le strategie ML vengono utilizzate in molte industrie e campi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 3,
+        "title": "Storia di Machine Learning: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quando venne approssimativamente coniato il termine 'intelligenza artificiale (AI)?",
+            "answerOptions": [
+              {
+                "answerText": "Negli anni 80",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Negli anni 50",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Negli anni 30",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Chi fu uno dei primi pionieri di machine learning?",
+            "answerOptions": [
+              {
+                "answerText": "Alan Turing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bill Gates",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Shakey il robot",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è uno dei motivi per cui il progresso in AI ha rallentato negli anni '70?",
+            "answerOptions": [
+              {
+                "answerText": "Potenza di calcolo limitata",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Non abbastanza ingegneri qualificati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Conflitti tra i paesi",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 4,
+        "title": "Storia di Machine Learning: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Qual è un esempio di sistema AI 'scruffy'?",
+            "answerOptions": [
+              {
+                "answerText": "ELIZA",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "HACKML",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SSYSTEM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è un esempio di una tecnologia sviluppata durante gli 'anni d'oro'?",
+            "answerOptions": [
+              {
+                "answerText": "Blocks world (Il mondo dei blocchi)",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Jibo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Cani robot",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale evento è stato fondamentale nella creazione e nell'espansione del campo dell'intelligenza artificiale?",
+            "answerOptions": [
+              {
+                "answerText": "Test di Turing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Progetto di Ricerca Estivo Dartmouth",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "L'inverno della AI",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 5,
+        "title": "Equità e Machine Learning: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "L'iniquità in Machine Learning può accadere",
+            "answerOptions": [
+              {
+                "answerText": "intenzionalmente",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "involontariamente",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa connota il termine 'iniquità' in ML:",
+            "answerOptions": [
+              {
+                "answerText": "danneggia un gruppo di persone",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "danneggia una persona",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "danneggia la maggior parte delle persone",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "I cinque principali tipi di danno includono",
+            "answerOptions": [
+              {
+                "answerText": "assegnazione, qualità del servizio, stereotipi, denigrazione e sovra o sotto rappresentazione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "allocazione, qualità del servizio, stereotipi, denigrazione sovra o sotto rappresentazione",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Assegnazione, qualità del servizio, stereofonica, denigrazione e sovra o sotto rappresentazione ",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 6,
+        "title": "Equità e Machine Learning: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "L'iniquità in un modello può essere causata da",
+            "answerOptions": [
+              {
+                "answerText": "eccessivo affidamento sui dati storici",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "scarso affidamento sui dati storici",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "allineamento troppo preciso ai dati storici",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Per mitigare l'iniquità si può:",
+            "answerOptions": [
+              {
+                "answerText": "Identificare danni e gruppi interessati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "definire metriche di equità",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Fairlearn è un pacchetto che può",
+            "answerOptions": [
+              {
+                "answerText": "confrontare più modelli utilizzando metriche di equità e prestazioni",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "scegliere il modello migliore per le proprie esigenze",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "aiutare a decidere cosa è equo e cosa no",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 7,
+        "title": "Strumenti e Tecniche: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quando si crea un modello, si dovrebbe:",
+            "answerOptions": [
+              {
+                "answerText": "preparare i propri dati, quindi addestrare il modello",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "scegliere un metodo di addestramento, quindi preparare i propri dati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "mettere a punto i parametri, quindi addestrare il proprio modello",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La ___ dei propri dati avrà impatto sulla qualità del modello ML",
+            "answerOptions": [
+              {
+                "answerText": "quantità",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "forma",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Una variabile di caratteristica è:",
+            "answerOptions": [
+              {
+                "answerText": "una qualità dei dati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "una proprietà misurabile dei dati",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "una riga dei dati",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 8,
+        "title": "Strumenti e Tecniche: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Si dovrebbero visualizzare i dati perché",
+            "answerOptions": [
+              {
+                "answerText": "si possono scoprire i valori anomali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "si possono scoprire potenziali cause di pregiudizio",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Si dividono i dati in:",
+            "answerOptions": [
+              {
+                "answerText": "insiemi di addestramento e insiemi di turing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "insiemi di addestramento e test",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "insiemi di validazione ed esecuzione",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un comando comune per avviare il processo di addestramento in varie librerie ML è:",
+            "answerOptions": [
+              {
+                "answerText": "model.travel",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.train",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.fit",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 9,
+        "title": "Introduzione alla Regressione: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quale di queste variabili è una variabile numerica?",
+            "answerOptions": [
+              {
+                "answerText": "Altezza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Genere",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Colore dei Capelli",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale di queste variabili è una variabile di categoria?",
+            "answerOptions": [
+              {
+                "answerText": "Frequenza cardiaca",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Gruppo sanguigno",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Peso",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale di questi problemi è un problema basato sull'analisi di regressione?",
+            "answerOptions": [
+              {
+                "answerText": "Prevedere i voti di esame finale di uno studente",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Prevedere il gruppo sanguigno di una persona",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Prevedendo se una email è spam o no",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 10,
+        "title": "Introduzione alla Regressione: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Se l'accuratezza del modello di addestramento di Machine Learning è del 95 % e l'accuratezza del test è del 30 %, come viene chiamata questa condizione?",
+            "answerOptions": [
+              {
+                "answerText": "Sovraaddestramento.",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "In",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Doppio adattamento",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Il processo di identificazione delle caratteristiche significative da una serie di caratteristiche è chiamata:",
+            "answerOptions": [
+              {
+                "answerText": "Estrazione di caratteristica",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Riduzione della dimensionalità della caratteristica",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Selezione della caratteristica",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Il processo di divisione di un insieme di dati in un determinato rapporto di addestramento e test dell'insieme di dati utilizzando il metodo/funzione di Scikit Learn  'train_test_split()' è chiamato:",
+            "answerOptions": [
+              {
+                "answerText": "Cross-Validation",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hold-Out Validation",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Leave one out Validation",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 11,
+        "title": "Preparare e Visualizzare Dati per la Regression: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quale di questi moduli Python viene utilizzato per tracciare la visualizzazione dei dati?",
+            "answerOptions": [
+              {
+                "answerText": "Numpy",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Scikit-learn",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Matplotlib",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Se si vuole capire la diffusione o le altre caratteristiche dei punti dati in un insieme, si esegue:",
+            "answerOptions": [
+              {
+                "answerText": "Visualizzazione dati",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Pre-elaborazione dati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Divisione insieme tra addestramento e test",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale di questi è una parte del passaggio di visualizzazione dei dati in un progetto di machine learning?",
+            "answerOptions": [
+              {
+                "answerText": "Incorporazione un determinato algoritmo di Machine Learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Creazione di una rappresentazione pittorica dei dati utilizzando diversi metodi di tracciatura",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Normalizzare i valori di un insieme di dati",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 12,
+        "title": "Preparare e Visualizzare Dati per la Regression: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quale di questi brani di codice è corretto in base a questa lezione, se si vuole verificare la presenza di valori mancanti in un insieme di dati? Si supponga che l'insieme di dati sia memorizzato in una variabile di tipo oggetto Pandas DataFrame chiamata 'insieme di dati' ",
+            "answerOptions": [
+              {
+                "answerText": "insieme di dati.isnull().sum()",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "findMissing(dataset)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sum(null(dataset))",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale di questi metodi di tracciatura è utile quando si desidera capire la diffusione di diversi gruppi di punti dati da un insieme di dati?",
+            "answerOptions": [
+              {
+                "answerText": "Grafico a dispersione (Scatter Plot)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Grafico a Linee (Line Plot)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Grafico a Barre (Bar Plot)",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa NON rivela la visualizzazione dei dati?",
+            "answerOptions": [
+              {
+                "answerText": "Relazioni tra i punti dati.",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "La fonte da dove viene raccolto l'insieme di dati",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Trovare la presenza di valori anomali nell'insieme di dati",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 13,
+        "title": "Regressione Lineare e Polinomiale: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Matplotlib è una:",
+            "answerOptions": [
+              {
+                "answerText": "Libreria di disegno",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Libreria di visualizzazione dati",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Libreria di prestito",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La Regressione Lineare usa il seguente per tracciare relazioni tra variabili",
+            "answerOptions": [
+              {
+                "answerText": "una linea retta",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "un cerchio",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "una curva",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un buon modello di Regressione Lineare coefficiente di correlazione ___",
+            "answerOptions": [
+              {
+                "answerText": "basso",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "alto",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "piatto",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 14,
+        "title": "Regressione Lineare e Polinomiale: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Se i dati non sono lineari si prova un tipo di regressione ___ ",
+            "answerOptions": [
+              {
+                "answerText": "lineare",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sferica",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "polinomiale",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "I seguenti sono tutti tipi di metodi di regressione",
+            "answerOptions": [
+              {
+                "answerText": "Falsestep, Ridge, Lasso e Elasticnet",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Stepwise, Ridge, Lasso e Elasticnet",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Stepwise, Ridge, Lariat e Elasticnet",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Regressione dei minimi quadrati (Least-Sqares Regression) significa che tutti i punti dati che circondano la riga di regressione sono:",
+            "answerOptions": [
+              {
+                "answerText": "elevati al quadrato e poi sottratti",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "moltiplicato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "elevati al quadrato e poi aggiunti",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 15,
+        "title": "Regressione Logistica: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Si usa la Regressione Logistica per prevedere",
+            "answerOptions": [
+              {
+                "answerText": "se una mela sia matura o no",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "quanti biglietti si possono vendere in un mese",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "di che colore sarà il cielo domani alle 6 del pomeriggio",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Tipi di Regressione Logistica includono",
+            "answerOptions": [
+              {
+                "answerText": "multinomiale e cardinale",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "multinomiale e ordinale",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "principale e ordinale",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "I dati hanno una correlazione debole. Il miglior tipo di regressione da usare è:",
+            "answerOptions": [
+              {
+                "answerText": "Logistica",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Lineare",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Cardinale",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 16,
+        "title": "Regressione Logistica: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Seaborn è un tipo di",
+            "answerOptions": [
+              {
+                "answerText": "libreria di visualizzazione dei dati",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "libreria di mappatura",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "libreria matematica",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Una matrice di confusione è anche nota come una:",
+            "answerOptions": [
+              {
+                "answerText": "matrice di errore",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "matrice di verità",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "matrice di accuratezza",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un buon modello avrà:",
+            "answerOptions": [
+              {
+                "answerText": "un grande numero di falsi positivi e veri negativi nella sua matrice di confusione",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "un grande numero di veri positivi e di veri negativi nella sua matrice di confusione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "un grande numero di veri positivi e falsi negativi nella sua matrice di confusione",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 17,
+        "title": "Costruire un'app web: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Cosa significa ONNX?",
+            "answerOptions": [
+              {
+                "answerText": "Over Neural Network Exchange",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Open Neural Network Exchange",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Output Neural Network Exchange",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Com'è definito Flask dai suoi creatori?",
+            "answerOptions": [
+              {
+                "answerText": "mini-framework",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "large-framework",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "micro-framework",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa fa il modulo Pickle di Python",
+            "answerOptions": [
+              {
+                "answerText": "Serializza un oggetto Python",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "De-serializza un oggetto Python",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Serializza e De-serializza un oggetto Python",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 18,
+        "title": "Costruire un'app web: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quali sono gli strumenti che si possono usare per ospitare sul web un modello pre-addestrato usando Python?",
+            "answerOptions": [
+              {
+                "answerText": "Flask",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "TensorFlow.js",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "onnx.js",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa significa SaaS?",
+            "answerOptions": [
+              {
+                "answerText": "System as a Service (sistema come servizio)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Software as a Service (software come servizio)",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Security as a Service (sicurezza come servizio)",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa fa la libreria LabelEncoder di Scikit-learn?",
+            "answerOptions": [
+              {
+                "answerText": "Codifica i dati alfabeticamente",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Codifica i dati numericamente",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Codifica i dati in modo seriale",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 19,
+        "title": "Classificazione 1: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "La classificazione è una forma di apprendimento supervisionato che ha molto in comune con",
+            "answerOptions": [
+              {
+                "answerText": "Serie temporali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Tecniche di regressione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A quali domande la classificazione può aiutare a rispondere?",
+            "answerOptions": [
+              {
+                "answerText": "Questa email è spam o no?",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "I maiali possono volare?",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Qual è il senso della vita?",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è il primo passo per usare tecniche diclassificazione?",
+            "answerOptions": [
+              {
+                "answerText": "creare classi di un insieme di dati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "pulire e bilanciare i dati",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "assegnare un punto dati a un gruppo o risultato",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 20,
+        "title": "Classificazione 1: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Cos'è una domanda multiclasse?",
+            "answerOptions": [
+              {
+                "answerText": "l'attività di classificazione di punti dati in multiple classi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "l'attività di classificazione di punti dati in una di parecchie classi",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "l'attività di pulizia dei punti dati in svariati modi",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "È importante pulire i dati ricorrenti o inutili per aiutare i classificatori a risolvere il problema.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è la ragione migliore per bilanciare i dati?",
+            "answerOptions": [
+              {
+                "answerText": "I dati squilibrati rendono male nelle visualizzazioni",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Bilanciare i dati consente migliori risultati poichè un modello ML non sarà sbilanciato verso una classe",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bilanciare i dati fornisce maggiori punti dati",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 21,
+        "title": "Classificazione 2: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "I dati bilanciati e puliti producono i migliori risultati di classificazione",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Come si sceglie il classificatore giusto?",
+            "answerOptions": [
+              {
+                "answerText": "Capire quali classificatori funzionano meglio per i quali scenari",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Ipotesi plausibile e verifica",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "La classificazione è un tipo di",
+            "answerOptions": [
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Apprendimento supervisionato",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Linguaggio di programmazione",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 22,
+        "title": "Classificazione 2: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Cosa è un 'solver'?",
+            "answerOptions": [
+              {
+                "answerText": "la persona che controlla il proprio lavoro",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "l'algoritmo da utilizzare in un problema di ottimizzazione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "una tecnica di machine learning",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale classificatore è stato usato in questa lezione?",
+            "answerOptions": [
+              {
+                "answerText": "Regressione Logistica",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Alberi Decisionali (Decision Trees)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Multiclasse uno-contro-tutti (One-vs-All Multiclass)",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Come si capisce se un algoritmo di classificazione funziona come previsto?",
+            "answerOptions": [
+              {
+                "answerText": "Verificando l'accuratezza delle sue previsioni",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Confrontandolo con altri algoritmi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Guardando i dati storici per verificare quanto sia valido per risolvere problemi simili",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 23,
+        "title": "Classificazione 3: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Un buon classificatore di partenza da provare è:",
+            "answerOptions": [
+              {
+                "answerText": "SVC Lineare",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "K-Means",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SVC Logico",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La regolarizzazione controlla:",
+            "answerOptions": [
+              {
+                "answerText": "l'influenza dei parametri",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "l'influenza della velocità di addestramento",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "l'influenza dei valori anomali",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Il classificatore K-Neighbors può essere usato per:",
+            "answerOptions": [
+              {
+                "answerText": "Apprendimento supervisionato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Apprendimento non supervisionato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 24,
+        "title": "Classificazione 3: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "I classificatori Support-Vector possono essere usati per",
+            "answerOptions": [
+              {
+                "answerText": "classificazione",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "regressione",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Random Forest è un tipo di classificatore ___ ",
+            "answerOptions": [
+              {
+                "answerText": "Ensemble",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Dissemble",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Assemble",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Adaboost è noto per:",
+            "answerOptions": [
+              {
+                "answerText": "focalizzarsi sui pesi di elemento classificati incorrettamente",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "focalizzarsi su valori anomali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "focalizzarsi su dati non corretti",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 25,
+        "title": "Classificazione 4: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "I sistemi di raccomandazione sono usati per",
+            "answerOptions": [
+              {
+                "answerText": "Consigliare un buon ristorante",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Consigliare quale moda seguire",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Incorporare un modello in una app web la aiuta a funzionare offline",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Onnx Runtime can be used for",
+            "answerOptions": [
+              {
+                "answerText": "Eseguire modelli in un'app web",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Addestrare modelli",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Messa a punto degli iperparametri",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 26,
+        "title": "Classificazione 4: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "L'app Netron aiuta a:",
+            "answerOptions": [
+              {
+                "answerText": "Visualizzare dati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Visualizzare la struttura del proprio modello",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Testare la propria app web",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un modello Scikit-learn si converte all'utilizzo con Onnx usando:",
+            "answerOptions": [
+              {
+                "answerText": "sklearn-app",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-web",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-onnx",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "L'utilizzo di un modello in un'app web viene chiamato:",
+            "answerOptions": [
+              {
+                "answerText": "inferenza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "interferenza",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "assicurazione",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 27,
+        "title": "Introduzione al Clustering: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Un esempio di vita reale di clustering sarebbe",
+            "answerOptions": [
+              {
+                "answerText": "Apparecchiare la tavola",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Ordinare il bucato",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Fare la spesa",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Le tecniche di clustering possono essere usate in queste industrie",
+            "answerOptions": [
+              {
+                "answerText": "bancaria",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "e-commerce",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambe",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Clustering è un tipo di:",
+            "answerOptions": [
+              {
+                "answerText": "Apprendimento supervisionato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Apprendimento non supervisionato",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Apprendimento rinforzato",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 28,
+        "title": "Introduzione al Clustering: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "La geometria euclidea è disposta lungo",
+            "answerOptions": [
+              {
+                "answerText": "piani",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "curve",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sfere",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La densità dei dati di clustering è in relazione alla sua",
+            "answerOptions": [
+              {
+                "answerText": "rumorosità",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "profondità",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "validità",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "L'algoritmo di clustering più noto è",
+            "answerOptions": [
+              {
+                "answerText": "k-means",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "k-middle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "k-mart",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 29,
+        "title": "K-Means Clustering: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "K-Means deriva da:",
+            "answerOptions": [
+              {
+                "answerText": "ingegneria elettrico",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "elaborazione del segnale",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "linguistica computazionale",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un buon punteggio Silhouette significa:",
+            "answerOptions": [
+              {
+                "answerText": "i clusters sono ben separati e ben definiti",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ci sono pochi clusters",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ci sono molti clusters",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La varianza è:",
+            "answerOptions": [
+              {
+                "answerText": "la media delle differenze quadrate dalla media",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Un problema per il clustering se diventa troppo alta",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 30,
+        "title": "K-Means Clustering: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Un diagramma di Voronoi mostra:",
+            "answerOptions": [
+              {
+                "answerText": "la varianza di un cluster",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "il seme di un cluster e la sua regione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "l'inerzia di un cluster",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "L'inerzia è",
+            "answerOptions": [
+              {
+                "answerText": "la misura della coerenza interna dei cluster",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "la misura di come si spostano i clusters",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "la misura della qualità dei cluster",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Usando K-Means, si deve prima determinare il valore di 'k'",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 31,
+        "title": "Introduzione a NLP: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Cosa significa NLP in queste lezioni?",
+            "answerOptions": [
+              {
+                "answerText": "Neural Language Processing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Natural Linguistic Processing",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Eliza era un bot primitivo che agiva come un computer",
+            "answerOptions": [
+              {
+                "answerText": "terapista",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "dottore",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "infermiere",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Il 'Turing Test' di Alan Turing cercava di determinare se un computer fosse",
+            "answerOptions": [
+              {
+                "answerText": "indistinguibile da un essere umano",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "pensante",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 32,
+        "title": "Introduzione a NLP: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Joseph Weizenbaum inventò il bot",
+            "answerOptions": [
+              {
+                "answerText": "Elisha",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Eliza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Eloise",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un bot conversazionale fornisce le risposte in base a",
+            "answerOptions": [
+              {
+                "answerText": "scelte casuali di risposte predefinite",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "analisi dell'input e utilizzo dell'intelligenza della macchina",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa renderebbe un bot più efficace?",
+            "answerOptions": [
+              {
+                "answerText": "porgli maggiori domande.",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "passargli più dati e addestrarlo di conseguenza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "il bot è stupido, non può imparare :(",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 33,
+        "title": "NLP Tasks: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Tokenizzazione",
+            "answerOptions": [
+              {
+                "answerText": "Divide il testo per mezzo della punteggiatura",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Divide il testo in token separati (parole)",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Divide il testo in frasi",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "L'embedding",
+            "answerOptions": [
+              {
+                "answerText": "converte dati testuali in numerici in modo che le parole possano essere raggruppate",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "incorpora parole in frasi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "incorpora frasi in paragrafi",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "L'etichettatura Parts-of-Speech",
+            "answerOptions": [
+              {
+                "answerText": "Divide le frasi con le loro parti del discorso",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "prende parole tokenizzate e le etichetta nella loro parte del discorso",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "diagramma frasi",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 34,
+        "title": "NLP Tasks: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Si costruisce un dizionario di quanto spesso le parole si ripetono usando:",
+            "answerOptions": [
+              {
+                "answerText": "un dizionario di parole e frasi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "una frequenza di parole e frasi",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "una libreria di parole e frasi",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "N-grams fa riferimento a",
+            "answerOptions": [
+              {
+                "answerText": "Un testo che può essere diviso in frasi di una lunghezza stabilita",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Una parola che può essere divisa in sequenze di caratteri di una data lunghezza",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Un testo che può essere diviso in paragrafi di una lunghezza stabilita",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Analisi del sentimento",
+            "answerOptions": [
+              {
+                "answerText": "analizza una frase per positività o negatività",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Analizza una frase per il sentimentalismo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "analizza una frase per la tristezza",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 35,
+        "title": "NLP e Traduzione: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Una traduzione ingenua",
+            "answerOptions": [
+              {
+                "answerText": "traduce solo le parole",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "traduce la struttura della frase",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "traduce il sentimento.",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un *corpus* di testi si riferisce a",
+            "answerOptions": [
+              {
+                "answerText": "Un piccolo numero di testi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Un gran numero di testi",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Un testo standard.",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Se un modello ML ha traduzioni umane sufficienti con le quali costruire un modello, può",
+            "answerOptions": [
+              {
+                "answerText": "abbreviare le traduzioni",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "standardizzare le traduzioni",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "migliorare l'accuratezza delle traduzioni",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 36,
+        "title": "NLP e Traduzione: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Sottostante alla libreria TextBlob's si trova:",
+            "answerOptions": [
+              {
+                "answerText": "Google Translate",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Un modello ML personalizzato",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Per usare `blob.translate` serve:",
+            "answerOptions": [
+              {
+                "answerText": "una connessione Internet.",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "un dizionario",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "JavaScript",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Un approccio ML per determinare il sentimento sarebbe:",
+            "answerOptions": [
+              {
+                "answerText": "applicare tecniche di regressione per generare manualmente opinioni e punteggi e cercare modelli",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Applicare tecniche NLP per generare manualmente opinioni e punteggi e cercare modelli",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Applicare tecniche di clustering per generare manualmente opinioni e punteggi e cercare modelli",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 37,
+        "title": "NLP 4: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quali informazioni si possonp ottenere dal testo che è stato scritto o parlato da un essere umano?",
+            "answerOptions": [
+              {
+                "answerText": "modelli e frequenze",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sentimento e significato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è l'analisi del sentimento?",
+            "answerOptions": [
+              {
+                "answerText": "Uno studio se un cimelio di famiglia ha un valore sentimentale",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "un metodo di identificare sistematicamente, estrarre, quantificare e studiare stati affettivi e informazioni soggettive",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "la capacità di dire se qualcuno è triste o felice",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A quale domanda si potrebbe rispondere usando un insieme di dati di recensioni di hotel, Python, e analisi del sentimento?",
+            "answerOptions": [
+              {
+                "answerText": "Quali sono le parole e frasi più frequenti usate nelle recensioni?",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Quale resort ha la migliore piscina?",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "C'è un parcheggio custodito in questo hotel?",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 38,
+        "title": "NLP 4: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Qual è l'essenza di NLP?",
+            "answerOptions": [
+              {
+                "answerText": "categorizzare il linguaggio umano in felice o triste",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "interpretare il significato o il sentimento senza che sia un umano a doverlo fare",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "trovare ed esaminare valori anomali nel sentimento",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quali sono alcune cose che si potrebbero cercare durante la pulizia dei dati?",
+            "answerOptions": [
+              {
+                "answerText": "caratteri in altre lingue",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "righe o colonne vuote",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "È importante capire i dati e le loro debolezze prima di eseguire operazioni su di essi.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 39,
+        "title": "NLP 5: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Perché è importante pulire i dati prima di analizzarli?",
+            "answerOptions": [
+              {
+                "answerText": "Alcune colonne potrebbero avere dati mancanti o errati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "I dati disordinati possono portare a false conclusioni sull'insieme di dati",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è un esempio di una strategia per la pulizia dei dati?",
+            "answerOptions": [
+              {
+                "answerText": "rimuovere colonne/righe non utili per rispondere a una specifica domanda",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "sbarazzarsi di valori verificati che non si adattano alla propria ipotesi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "spostare i valori anomali in una tabella separata ed eseguire il calcolo per quella tabella per vedere se corrispondono",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Può essere utile categorizzare i dati usando la colonna Tag.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 40,
+        "title": "NLP 5: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Qual è l'obiettivo di un insieme di dati?",
+            "answerOptions": [
+              {
+                "answerText": "Per vedere quante recensioni negative e positive ci sono per hotel in tutto il mondo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Per aggiungere sentimento e colonne che aiuteranno a scegliere il miglior hotel",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Per analizzare perché le persone lasciano recensioni specifiche",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Cosa sono le stop words?",
+            "answerOptions": [
+              {
+                "answerText": "parole inglesi comuni che non cambiano il sentimento di una frase",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "parole che si possono rimuovere per velocizzare l'analisi del sentimento",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Per verificare l'analisi del sentimento, ci si assicura che corrisponda con il punteggio del recensore per la stessa recensione.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 41,
+        "title": "Introduzione alle Serie Temporali: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "La previsione delle Serie Temporali è utile per",
+            "answerOptions": [
+              {
+                "answerText": "determinare i costi futuri",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "prevedere i prezzi futuri",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Una Serie Temporale è una sequenza presa a:",
+            "answerOptions": [
+              {
+                "answerText": "successivi punti equamente distanziati nello spazio",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "successivi punti equamente distanziati nel tempo",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "successivi punti equamente distanziati nello spazio e nel tempo",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Le Serie Temporali possono essere usate nella:",
+            "answerOptions": [
+              {
+                "answerText": "previsione dei terremoti",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "visione computerizzata",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Analisi del colore",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 42,
+        "title": "Introduzione alle Serie Temporali: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Le tendenze di Serie Temporali sono",
+            "answerOptions": [
+              {
+                "answerText": "Aumenti e diminuzioni misurabili nel tempo",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Quantificazioni di diminuzioni nel tempo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Differenze tra aumenti e diminuzioni nel tempo",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "I valori anomali sono",
+            "answerOptions": [
+              {
+                "answerText": "punti vicino alla varianza dei dati standard",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "punti molto lontani dalla varianza dei dati standard",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Punti all'interno della varianza dei dati standard",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La previsione delle Serie Temporali è maggiormente utile per",
+            "answerOptions": [
+              {
+                "answerText": "Econometria",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Storia",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Biblioteche",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 43,
+        "title": "Serie Temporali ARIMA: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "ARIMA sta per",
+            "answerOptions": [
+              {
+                "answerText": "AutoRegressive Integral Moving Average",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Average",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "La stazionarietà fa riferimento a",
+            "answerOptions": [
+              {
+                "answerText": "dati i cui attributi non cambiano quando sono spostati nel tempo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "dati la cui distribuzione non cambia quando è spostata nel tempo",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "dati la cui distribuzione cambia quando si è spostata nel tempo",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Differenziazione",
+            "answerOptions": [
+              {
+                "answerText": "Stabilizza tendenza e stagionalità",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "esacerba tendenza e stagionalità",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Elimina tendenza e stagionalità",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 44,
+        "title": "Serie Temporali ARIMA: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "ARIMA è usato per creare un modello che si adatta alla forma speciale di dati di serie temporali",
+            "answerOptions": [
+              {
+                "answerText": "il più piatto possibile",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "il più vicino possibile",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "tramite grafici a dispersione.",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Si usa SARIMAX per",
+            "answerOptions": [
+              {
+                "answerText": "gestire modelli ARIMA stagionali",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "gestire modelli speciali ARIMA",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "gestire statistiche su ARIMA",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "La validazione 'Walk-Forward' coinvolge",
+            "answerOptions": [
+              {
+                "answerText": "la rivalutazione progressiva di un modello mentre viene convalidato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "il riaddestramento progressivo di un modello mentre viene convalidato",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "la riconfigurazione progressiva di un modello mentre viene valutato",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 45,
+        "title": "Reinforcement 1: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Cos'è il reinforcement learning?",
+            "answerOptions": [
+              {
+                "answerText": "insegnare a qualcuno qualcosa più e più volte finché non capisce",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "una tecnica di apprendimento che decifra il comportamento ottimale di un agente in qualche ambiente eseguendo molti esperimenti",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "capire come eseguire più esperimenti contemporaneamente",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Cos'è una policy?",
+            "answerOptions": [
+              {
+                "answerText": "una funzione che restituisce l'azione in qualsiasi stato",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Un documento che indica se si può restituire o meno un oggetto",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "una funzione utilizzata per uno scopo casuale",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Una funzione di ricompensa ritorna un punteggio per ogni stato di un ambiente.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 46,
+        "title": "Reinforcement 1: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Cos'è Q-Learning?",
+            "answerOptions": [
+              {
+                "answerText": "un meccanismo per la registrazione della 'bontà' di ogni stato",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "un algoritmo in cui la politica è definita da una Q-Table",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "entrambi i precedenti",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Per quale valore una Q-Table corrisponde alla policy random walk?",
+            "answerOptions": [
+              {
+                "answerText": "tutti valori sono uguali",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "-0.25",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "tutti i valori sono diversi",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "E' meglio usare l'esplorazione piuttosto che lo sfruttamento durante il processo di apprendimento in questa lezione.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 47,
+        "title": "Reinforcement 2: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Gli scacchi e Go sono giochi con stati continui.",
+            "answerOptions": [
+              {
+                "answerText": "Vero",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Falso",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Cos'è il problema CartPole?",
+            "answerOptions": [
+              {
+                "answerText": "un processo per l'eliminazione dei valori anomali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "un metodo per ottimizzare il carrello della spesa",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "una versione semplificata di bilanciamento",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale strumento si può usare per giocare diversi scenari di potenziali stati in un gioco?",
+            "answerOptions": [
+              {
+                "answerText": "Indovina e controlla",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ambienti di simulazione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "test di transizione dello stato",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 48,
+        "title": "Reinforcement 2: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Dove si definiscono tutte le possibili azioni in un ambiente?",
+            "answerOptions": [
+              {
+                "answerText": "metodi",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "spazio di azione",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "elenco di azioni",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale coppia è stata usata come chiave-valore del dizionario?",
+            "answerOptions": [
+              {
+                "answerText": "(state, action) come chiave, voci Q-Table come valore",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "state come chiave, action come valore",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "il valore di qvalues come chiave, action come valore",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quali sono gli iperparametri usati durante Q-Learning?",
+            "answerOptions": [
+              {
+                "answerText": "valore q-table, ricompensa corrente, azione casuale",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "rapporto di apprendimento, fattore di sconto, fattore di esplorazione/sfruttamento",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ricompense comulative, rapporto di apprendimento, fattore di esplorazione",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 49,
+        "title": "Applicazioni del mondo reale: Quiz Pre-Lezione",
+        "quiz": [
+          {
+            "questionText": "Qual è un esempio di applicazione ML nel mondo della finanza?",
+            "answerOptions": [
+              {
+                "answerText": "Personalizzazione dell'esplorazione utente usando NLP",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Gestione patrimoniale usando la Regressione Lineare",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Gestione energia tramite Serie Temporali",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale tecnica ML possono usare gli ospedali per gestire le riammissioni?",
+            "answerOptions": [
+              {
+                "answerText": "Clustering",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Serie Temporali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è un esempio di utilizzo di Serie Temporali per la gestione dell'energia?",
+            "answerOptions": [
+              {
+                "answerText": "Sensori di movimento di animali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Parchimetri intelligenti",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Tracciamento dei fuochi boschivi",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 50,
+        "title": "Real World Applications: Quiz Post-Lezione",
+        "quiz": [
+          {
+            "questionText": "Quale tecnica ML può essere usata per rilevare le frodi sulle carte di credito?",
+            "answerOptions": [
+              {
+                "answerText": "Regressione",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Clustering",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Quale tecnica ML è esemplificata con la gestione della foresta?",
+            "answerOptions": [
+              {
+                "answerText": "Reinforcement Learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Serie Temporali",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Qual è un esempio di una applicazione ML nell'industria dell'assistenza sanitaria?",
+            "answerOptions": [
+              {
+                "answerText": "Prevedere il comportamento dello studente usando la regressione",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Gestione degli studi clinici usando classificatori",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Sensori di movimento degli animali usando i classificatori",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  }
+]
diff --git a/quiz-app/src/assets/translations/ja.json b/quiz-app/src/assets/translations/ja.json
new file mode 100644
index 000000000..4696347f3
--- /dev/null
+++ b/quiz-app/src/assets/translations/ja.json
@@ -0,0 +1,2815 @@
+[
+  {
+    "title": "初心者のための機械学習: 小テスト",
+    "complete": "おめでとうございます、小テストを完了しました！",
+    "error": "すみません、もう一度試してみてください。",
+    "quizzes": [
+      {
+        "id": 1,
+        "title": "機械学習への導入: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習の応用は身近にある",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "古典的機械学習と深層学習の技術的な違いは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "古典的機械学習のほうが先に発明された",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ニューラルネットワークを使用するかどうか",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "深層学習はロボットに使用されている",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "なぜ企業は機械学習の戦略を使いたいと思うのでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "多元的な問題の解決を自動化するため",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "顧客の種類に応じてショッピング体験をカスタマイズするため",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 2,
+        "title": "機械学習への導入: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習のアルゴリズムがシミュレートするのは",
+            "answerOptions": [
+              {
+                "answerText": "賢いマシン",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "人間の脳",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "オランウータン",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "古典的機械学習の手法の例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "自然言語処理",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "深層学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ニューラルネットワーク",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "なぜ全員が機械学習の基礎を学ぶべきなのでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "機械学習を学ぶのは誰でも始めやすくて楽しいから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "機械学習の戦略は多くの産業や領域で使用されているから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 3,
+        "title": "機械学習の歴史: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "「人工知能」という言葉が生まれたのはいつ頃でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "1980年代",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "1950年代",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "1930年代",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "機械学習における先駆者のうちのひとりは誰でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "アラン・チューリング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ビル・ゲイツ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "シェーキー",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "1970年代にAIの進歩が鈍化した理由のひとつは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "計算能力の限界",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "熟練したエンジニアの不足",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "国家間の紛争",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 4,
+        "title": "機械学習の歴史: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "「みすぼらしい」AIシステムの例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "ELIZA",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "HACKML",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SSYSTEM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "「黄金期」に開発された技術の例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "Blocks world",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Jibo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ロボット犬",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "人工知能の分野において誕生と発展の礎になった出来事はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "チューリングテスト",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ダートマス夏期研究会",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "AIの冬",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 5,
+        "title": "公平性と機械学習: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習において不公平性が起こりうるのは",
+            "answerOptions": [
+              {
+                "answerText": "故意で",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "過失で",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方で",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "機械学習において「不公平性」が意味するのは",
+            "answerOptions": [
+              {
+                "answerText": "あるグループの人々に対する弊害",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ひとりの人に対する弊害",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "大多数の人々に対する弊害",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "5つの主な弊害は",
+            "answerOptions": [
+              {
+                "answerText": "割り当て・サービスの質・偏見・誹謗中傷・表現の過不足",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "移住・サービスの質・偏見・誹謗中傷・表現の過不足",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "割り当て・サービスの質・ステレオ・誹謗中傷・表現の過不足",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 6,
+        "title": "公平性と機械学習: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "モデルに不公平性が発生する原因のひとつは",
+            "answerOptions": [
+              {
+                "answerText": "過去のデータに対する依存度が高すぎること",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "過去のデータに対する依存度が低すぎること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "過去のデータとの整合性が高すぎること",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "不公平性を緩和するためにできることは",
+            "answerOptions": [
+              {
+                "answerText": "弊害とそれを受けるグループの特定",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "公平性に関する指標の定義",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Fairlearnパッケージができることは",
+            "answerOptions": [
+              {
+                "answerText": "複数のモデル間で公平性とパフォーマンスの指標を比較",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ニーズに応じた最適なモデルの選択",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "何が公平で、何がそうでないかの判断を助けること",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 7,
+        "title": "ツールと手法: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "モデルを構築する際にすべきなのは",
+            "answerOptions": [
+              {
+                "answerText": "データを準備してから学習すること",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "学習方法を選んでからデータを準備すること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "パラメータを調整してから学習すること",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "データの〇〇が機械学習モデルの質に影響を与える",
+            "answerOptions": [
+              {
+                "answerText": "量",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "形",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "特徴量とは",
+            "answerOptions": [
+              {
+                "answerText": "データの質",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データの測定可能な特性",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データの列",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 8,
+        "title": "ツールと手法: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "データを可視化すべき理由は",
+            "answerOptions": [
+              {
+                "answerText": "外れ値を発見できるから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "バイアスの原因を発見できるから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "分割するデータの種類は",
+            "answerOptions": [
+              {
+                "answerText": "訓練データとチューリングデータ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "訓練データとテストデータ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "検証データと評価データ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "様々な機械学習ライブラリで学習プロセスを開始する一般的なコマンドは",
+            "answerOptions": [
+              {
+                "answerText": "model.travel",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.train",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.fit",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 9,
+        "title": "回帰への導入: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "次の変数のうち、数値の変数はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "身長",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "性別",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "髪の色",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次の変数のうち、カテゴリーの変数はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "心拍数",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "血液型",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "体重",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次の問題のうち、回帰分析に基づく問題はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "学生の期末試験の点数を予測する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ある人物の血液型を予測する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "メールがスパムかどうかを判定する",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 10,
+        "title": "回帰への導入: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習モデルの学習精度が95%でテスト精度が30%の場合、どんな状態であると呼ばれるでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "過学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "未学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "二重学習",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "特徴量の中から重要なものを特定するプロセスの名前は",
+            "answerOptions": [
+              {
+                "answerText": "特徴量抽出",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "特徴量の次元削減",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "特徴量選択",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Scikit Learn の 'train_test_split()' メソッド/関数を使用して、データセットを一定の割合で訓練データセットとテストデータセットに分割する処理の名前は",
+            "answerOptions": [
+              {
+                "answerText": "交差検証",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ホールドアウト検証",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ひとつ抜き検証",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 11,
+        "title": "回帰のためにデータを準備して可視化する: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "次のPythonモジュールのうち、データを可視化するために使用されるものはどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "Numpy",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Scikit-learn",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Matplotlib",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データセットの広がり方やその他の特性を理解するために実行するのは",
+            "answerOptions": [
+              {
+                "answerText": "データの可視化",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データの前処理",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "訓練データとテストデータの分割",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次のうち、機械学習プロジェクトにおいてデータ可視化ステップの一部であるものはどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "特定の機械学習アルゴリズムを取り入れる",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "様々なプロット方法を使ってデータの図解表現を作成する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データセットの値を正規化する",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 12,
+        "title": "回帰のためにデータを準備して可視化する: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "次のコードスニペットのうち、データセットに欠損値が含まれているかどうかを確認するものとして、このレッスンにおいて正しいのはどれでしょうか？なお、データセットはPandasのDataFrameオブジェクトである 'dataset' という変数に格納されているものとします。",
+            "answerOptions": [
+              {
+                "answerText": "dataset.isnull().sum()",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "findMissing(dataset)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sum(null(dataset))",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次のプロット方法のうち、データセットの異なるデータグループの広がり方を理解するために有効なものはどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "散布図",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "折れ線グラフ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "棒グラフ",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データ可視化が教えてくれないことは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "データポイント間の関係",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データセットの収集源",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データセットに外れ値が含まれているかどうか",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 13,
+        "title": "線形および多項式回帰: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "Matplotlibは",
+            "answerOptions": [
+              {
+                "answerText": "描画ライブラリ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データ可視化ライブラリ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "図書館",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "線形回帰が変数間の関係をプロットする方法は",
+            "answerOptions": [
+              {
+                "answerText": "直線",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "円J",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "曲線",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "優れた線形回帰モデルは〇〇相関係数を持つ",
+            "answerOptions": [
+              {
+                "answerText": "低い",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "高い",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "平坦な",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 14,
+        "title": "線形および多項式回帰: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "データが線形でない場合、〇〇回帰を試すと良い",
+            "answerOptions": [
+              {
+                "answerText": "線形",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "球面",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "多項式",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "すべて回帰法の種類なのは",
+            "answerOptions": [
+              {
+                "answerText": "フォルスステップ・リッジ・ラッソ・エラスティックネット",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ステップワイズ・リッジ・ラッソ・エラスティックネット",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ステップワイズ・リッジ・ラリアット・エラスティックネット",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "最小二乗回帰は回帰直線のまわりのすべてのデータポイントが",
+            "answerOptions": [
+              {
+                "answerText": "二乗してから減算されている",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "乗算されている",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "二乗してから加算されている",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 15,
+        "title": "ロジスティック回帰: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "ロジスティック回帰が予測するのは",
+            "answerOptions": [
+              {
+                "answerText": "りんごが熟しているかどうか",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "チケットが月にいくつ売れるか",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "明日の午後6時に空が何色になるか",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ロジスティック回帰の種類に含まれるのは",
+            "answerOptions": [
+              {
+                "answerText": "多項と基本",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "多項と順序",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "主要と順序",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "あなたのデータには弱い相関があります。最適な回帰の種類は",
+            "answerOptions": [
+              {
+                "answerText": "ロジスティック",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "線形",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "基本",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 16,
+        "title": "ロジスティック回帰: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "Seabornは",
+            "answerOptions": [
+              {
+                "answerText": "データ可視化ライブラリ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "地図ライブラリ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "数学ライブラリ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "混同行列の別名は",
+            "answerOptions": [
+              {
+                "answerText": "誤差行列",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "真理行列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "精度行列",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "良いモデルは",
+            "answerOptions": [
+              {
+                "answerText": "混同行列に多くの偽陽性と真陰性を含む",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "混同行列に多くの真陽性と真陰性を含む",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "混同行列に多くの真陽性と偽陰性を含む",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 17,
+        "title": "Webアプリを構築する: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "ONNXは何の略でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "Over Neural Network Exchange",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Open Neural Network Exchange",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Output Neural Network Exchange",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Flaskは作成者にどのように定義されているでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "ミニフレームワーク",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ラージフレームワーク",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "マイクロフレームワーク",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "PythonのPickleモジュールが行うのは",
+            "answerOptions": [
+              {
+                "answerText": "パイソンオブジェクトのシリアライズ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Pythonオブジェクトのデシリアライズ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Pythonオブジェクトのシリアライズとデシリアライズ",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 18,
+        "title": "Webアプリを構築する: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "事前学習済みモデルをWeb上にホスティングするために使えるPythonのツールは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "Flask",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "TensorFlow.js",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "onnx.js",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "SaaSは何の略でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "System as a Service",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Software as a Service",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Security as a Service",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Scikit-learn の LabelEncoder ライブラリが行うことは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "データをアルファベットにエンコードすること",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データを数値にエンコードすること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データをシリアルにエンコードすること",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 19,
+        "title": "分類 1: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "分類は教師あり学習の一種であり、多くの共通点を持つのは",
+            "answerOptions": [
+              {
+                "answerText": "時系列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "回帰手法",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "自然言語処理",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類はどのような疑問に答えられるでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "このメールはスパムでしょうか？",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "豚は飛べるでしょうか？",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "人生の意味とは何でしょうか？",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類手法を使うための最初のステップは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "データのクラスを作成すること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データのクリーニングとバランシング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データポイントをグループまたは結果に割り当てること",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 20,
+        "title": "分類 1: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "多クラス問題とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "データポイントを複数のクラスに分類するタスク",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データポイントを複数のクラスのどれかに分類するタスク",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データポイントを複数の方法でクリーニングするタスク",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類器が問題を解決するためには、何度も現れるデータや役に立たないデータを除くことが重要である",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "データをバランシングする一番の理由は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "不均衡データは可視化すると見栄えが悪いから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データをバランシングすると機械学習モデルがひとつのクラスに偏らず、良い結果が得られるから",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データをバランシングするとより多くのデータポイントが得られるから",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 21,
+        "title": "分類 2: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "バランシングおよびクリーニングされたデータが最も良い分類結果につながる",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "正しい分類器はどのように選ぶでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "どの分類器がどの場面に最適かを理解する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "経験に基づく推測と確認",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "分類は一種の",
+            "answerOptions": [
+              {
+                "answerText": "自然言語処理",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "教師あり学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "プログラミング言語",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 22,
+        "title": "分類 2: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "「ソルバ」とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "自分の仕事をダブルチェックしてくれる人",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "最適化問題で使用するアルゴリズム",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "機械学習の手法",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "レッスンで使用した分類器はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "ロジスティック回帰",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "決定木",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "一対他の多クラス",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類アルゴリズムが期待通りに動作しているかどうかは、どのようにして知ることができるでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "予測の精度を確認する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "他のアルゴリズムと比較する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "似た問題を解決した際にどれだけ優れていたかを過去のデータから確認する",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 23,
+        "title": "分類 3: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "最初に試すのに適した分類器は",
+            "answerOptions": [
+              {
+                "answerText": "線形サポートベクター分類器",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "K-Means",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "論理サポートベクター分類器",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "正則化がコントロールするのは",
+            "answerOptions": [
+              {
+                "answerText": "パラメータの影響",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "学習スピードの影響",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "外れ値の影響",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "k近傍分類器が使えるのは",
+            "answerOptions": [
+              {
+                "answerText": "教師あり学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "教師なし学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 24,
+        "title": "分類 3: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "サポートベクター分類器が使えるのは",
+            "answerOptions": [
+              {
+                "answerText": "分類",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "回帰",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ランダムフォレストは〇〇な分類器の一種である",
+            "answerOptions": [
+              {
+                "answerText": "アンサンブル",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ディセンブル",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "アセンブル",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "アダブーストは次のように知られている",
+            "answerOptions": [
+              {
+                "answerText": "誤って分類された要素の重みに着目する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "外れ値に着目する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "誤ったデータに着目する",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 25,
+        "title": "分類 4: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "推薦システムが使えるのは",
+            "answerOptions": [
+              {
+                "answerText": "良いレストランの推薦",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "試すべきファッションの推薦",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Webアプリにモデルを埋め込むことでオフライン対応が可能になる",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Onnx Runtime が使えるのは",
+            "answerOptions": [
+              {
+                "answerText": "Webアプリの中でモデルを実行する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "モデルを学習する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ハイパーパラメータのチューニング",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 26,
+        "title": "分類 4: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "Netronアプリが役立つのは",
+            "answerOptions": [
+              {
+                "answerText": "データの可視化",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "モデル構造の可視化",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Webアプリのテスト",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Scikit-learn モデルをOnnxで扱えるようにするために使うのは",
+            "answerOptions": [
+              {
+                "answerText": "sklearn-app",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-web",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-onnx",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Webアプリでモデルを使うことは、通称",
+            "answerOptions": [
+              {
+                "answerText": "推論",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "干渉",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "保険",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 27,
+        "title": "クラスタリングへの導入: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "クラスタリングの実例は",
+            "answerOptions": [
+              {
+                "answerText": "食卓の準備",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "洗濯物の分類",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "食料品の買い物",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "クラスタリングの手法が使える産業は",
+            "answerOptions": [
+              {
+                "answerText": "銀行",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "電子商取引",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "クラスタリングは一種の",
+            "answerOptions": [
+              {
+                "answerText": "教師あり学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "教師なし学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "強化学習",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 28,
+        "title": "クラスタリングへの導入: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "ユークリッド幾何学が配置されるのは",
+            "answerOptions": [
+              {
+                "answerText": "平面",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "曲面",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "球面",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "クラスタリングデータの密度が関係するのは",
+            "answerOptions": [
+              {
+                "answerText": "ノイズ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "深さ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "妥当性",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "最も有名なクラスタリングアルゴリズムは",
+            "answerOptions": [
+              {
+                "answerText": "k-means",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "k-middle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "k-mart",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 29,
+        "title": "K-Means法: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "K-Means の派生元は",
+            "answerOptions": [
+              {
+                "answerText": "電気工学",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "信号処理",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "計算言語学",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "良いシルエットスコアとは",
+            "answerOptions": [
+              {
+                "answerText": "クラスタがよく分離されていて、よく定義されている",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "クラスタの数が少ない",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタの数が多い",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分散とは",
+            "answerOptions": [
+              {
+                "answerText": "平均との差を二乗した値の平均",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタリングにおいて高くなりすぎると問題になるもの",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 30,
+        "title": "K-Means法: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "ボロノイ図が表すのは",
+            "answerOptions": [
+              {
+                "answerText": "クラスタの分散",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタのシードとその領域",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "クラスタの慣性",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "慣性とは",
+            "answerOptions": [
+              {
+                "answerText": "どれだけ内部にまとまっているクラスタかを表す指標",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "クラスタがどれだけ動くかを表す指標",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタの質を表す指標",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "K-Means を使う際は、最初に 'k' の値を決める必要がある",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 31,
+        "title": "自然言語処理への導入: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "レッスンにおいてNLPは何の略でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "Neural Language Processing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Natural Linguistic Processing",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "初期のボットであるElizaが演じていたのは",
+            "answerOptions": [
+              {
+                "answerText": "療法士",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "医者",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "看護師",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "アラン・チューリングの「チューリングテスト」が判定しようとしていたのは、コンピュータが",
+            "answerOptions": [
+              {
+                "answerText": "人間と見分けがつかないかどうか",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "思考しているかどうか",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 32,
+        "title": "自然言語処理への導入: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "ジョセフ・ワイゼンバウムが発明したボットは",
+            "answerOptions": [
+              {
+                "answerText": "Elisha",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Eliza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Eloise",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "会話型のボットが出力する方法は",
+            "answerOptions": [
+              {
+                "answerText": "あらかじめ決められた選択肢からランダムに選ぶ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "入力を分析して機械知能を使う",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ボットをより効果的にするにはどうすれば良いでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "ボットに対してより多くの質問をする",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ボットにより多くのデータを与えて、それに応じた学習をさせる",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ボットは頭が悪いので学習できない :(",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 33,
+        "title": "自然言語処理のタスク: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "トークン化は",
+            "answerOptions": [
+              {
+                "answerText": "文章を句読点で分割する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "文章をトークン（単語）に分割する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "文章をフレーズに分割する",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "埋め込みは",
+            "answerOptions": [
+              {
+                "answerText": "単語をクラスタ化するために文章を数値に変換する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "単語をフレーズに埋め込む",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "文を段落に埋め込む",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "品詞タグ付けは",
+            "answerOptions": [
+              {
+                "answerText": "文を品詞で分ける",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "トークン化された単語を品詞でタグ付けする",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "文を図で表す",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 34,
+        "title": "自然言語処理のタスク: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "単語の出現頻度に関する辞書を作る際に使うのは",
+            "answerOptions": [
+              {
+                "answerText": "単語とフレーズの辞書",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "単語とフレーズの出現頻度",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "単語とフレーズのライブラリ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "N-grams とは",
+            "answerOptions": [
+              {
+                "answerText": "一定の長さの単語列に分割できる文章",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "一定の長さの文字列に分割できる単語",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "一定の長さの段落に分割できる文章",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "感情分析は",
+            "answerOptions": [
+              {
+                "answerText": "フレーズがポジティブかネガティブかを分析する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "フレーズが感傷的かどうかを分析する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "フレーズの悲しさを分析する",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 35,
+        "title": "自然言語処理と翻訳: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "単純な翻訳は",
+            "answerOptions": [
+              {
+                "answerText": "単語のみを翻訳する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "文章構造を翻訳する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情を翻訳する",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "文章の「コーパス」とは",
+            "answerOptions": [
+              {
+                "answerText": "少量の文章",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "大量の文章",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ひとつの標準的な文章",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "もしモデルを構築するのに十分な人間の翻訳があれば、機械学習モデルは",
+            "answerOptions": [
+              {
+                "answerText": "翻訳を省略できる",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "翻訳を標準化できる",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "翻訳の精度を高められる",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 36,
+        "title": "自然言語処理と翻訳: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "TextBlob の翻訳ライブラリの基盤は",
+            "answerOptions": [
+              {
+                "answerText": "Google翻訳",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "独自の機械学習モデル",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "`blob.translate` を使用するために必要なのは",
+            "answerOptions": [
+              {
+                "answerText": "インターネット接続",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "辞書",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "JavaScript",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "感情を判定するために機械学習のアプローチで行うのは",
+            "answerOptions": [
+              {
+                "answerText": "手で作成した意見やスコアに回帰の手法を適用して、パターンを探す",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "手で作成した意見やスコアに自然言語処理の手法を適用して、パターンを探す",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "手で作成した意見やスコアにクラスタリングの手法を適用して、パターンを探す",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 37,
+        "title": "自然言語処理 4: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "人間が書いたり話した文章から得られる情報は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "パターンと頻度",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情と意味",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "感情分析とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "家宝に感傷的な価値があるかどうかの研究",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情の状態や主観的な情報を体系的に識別・抽出・定量化・研究する方法",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ある人物が悲しいのか楽しいのかを見分ける能力",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ホテルのレビューのデータセット・Python・感情分析を使うことで答えられる質問は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "レビューで最もよく使われる単語やフレーズは何でしょうか？",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "どのリゾートに最も良いプールがあるでしょうか？",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "このホテルにはバレーパーキングがあるでしょうか？",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 38,
+        "title": "自然言語処理 4: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "自然言語処理の本質とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "人間の言葉を楽しいものと悲しいものに分類すること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "人の手を借りずに意味や感情を読み取ること",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "感情の異常を見つけて調べること",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "データをクリーニングする際に気を付けたほうが良いことは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "他の言語の文字",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "空の行や列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データを操作する前にデータとその弱点を理解するのが重要である",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 39,
+        "title": "自然言語処理 5: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "分析する前にデータをクリーニングすることが重要なのはなぜでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "データが欠損していたり不正だったりする列があるかもしれないから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "汚いデータはデータセットに関する誤った結論につながる可能性があるから",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データクリーニング戦略の例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "特定の質問に答えるために有用でない列や行の削除",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "仮説に合わない検証値の排除",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "外れ値を別の表に移し、その表で計算を行って、一致するかどうかを確認",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Tag列を使ってデータを分類すると便利なことがある",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 40,
+        "title": "自然言語処理 5: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "データセットの目的は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "世界中のホテルに対する否定的および肯定的なレビューがいくつあるかを確認すること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "最も良いホテルを選ぶために役立つ意見と列を追加すること",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "人々がなぜ特定のレビューを残すのかを分析すること",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ストップワードとは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "文章の印象を変えない一般的な単語",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情分析を高速化するために除去できる単語",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "感情分析をテストするには、同じレビューに対するレビュアーのスコアが一致していることを確認する",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 41,
+        "title": "時系列への導入: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "時系列予測が役立つのは",
+            "answerOptions": [
+              {
+                "answerText": "将来のコストを決めるとき",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "将来の価格を予測するとき",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "時系列とは、次のような列である",
+            "answerOptions": [
+              {
+                "answerText": "空間的に連続した等間隔の点",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "時間的に連続した等間隔の点",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時間的および空間的に連続した等間隔の点",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "時系列が使用できるのは",
+            "answerOptions": [
+              {
+                "answerText": "地震予測",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "コンピュータビジョン",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "色解析",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 42,
+        "title": "時系列への導入: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "時系列のトレンドとは",
+            "answerOptions": [
+              {
+                "answerText": "時間の経過に伴う測定可能な増加と減少",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時間の経過に伴う減少の定量化",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "時間の経過に伴う増加と減少の差",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "外れ値とは",
+            "answerOptions": [
+              {
+                "answerText": "標準的なデータの分散に近い点",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "標準的なデータの分散から離れた点",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "標準的なデータの分散の中にある点",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "時系列予測が最も有効なのは",
+            "answerOptions": [
+              {
+                "answerText": "計量経済学",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "歴史",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "図書館",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 43,
+        "title": "時系列ARIMA: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "ARIMAは次の略である",
+            "answerOptions": [
+              {
+                "answerText": "AutoRegressive Integral Moving Average",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Average",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "定常性とは",
+            "answerOptions": [
+              {
+                "answerText": "時間をずらしても属性が変わらないデータ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "時間をずらしても分布が変わらないデータ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時間をずらすと分布が変わるデータ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "差分変換は",
+            "answerOptions": [
+              {
+                "answerText": "トレンドや季節性を安定させる",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "トレンドや季節性を悪化させる",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "トレンドや季節性を排除する",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 44,
+        "title": "時系列ARIMA: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "ARIMAは、時系列データの特殊な形に対してモデルを次のように適合させるために使われる",
+            "answerOptions": [
+              {
+                "answerText": "できるだけ平坦に",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "できるだけ近く",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "散布図によって",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "SARIMAXを使うのは",
+            "answerOptions": [
+              {
+                "answerText": "季節性ARIMAモデルを管理するため",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "特別なARIMAモデルを管理するため",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "統計的なARIMAモデルを管理するため",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "「ウォークフォワード」検証では",
+            "answerOptions": [
+              {
+                "answerText": "モデルを検証しながら段階的に再評価する",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "モデルを検証しながら段階的に再学習する",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "モデルを検証しながら段階的に再構成する",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 45,
+        "title": "強化 1: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "強化学習とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "理解するまで何度も教えること",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "何回も試行することで、ある環境におけるエージェントの最適な行動を解読する学習手法",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "複数の試行を一度に行う方法を理解すること",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "方策とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "任意の状態で行動を返す関数",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "返品できるかどうかを示す書類",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ランダムな目的で使用される関数",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "報酬関数はある環境における各状態に対するスコアを返す",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 46,
+        "title": "強化 1: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "Q学習とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "各状態の「良さ」を記録する仕組み",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Qテーブルによって方策が定義されているアルゴリズム",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上の両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ランダムウォークに対応するQテーブルの値は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "すべて同じ値",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "-0.25",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "すべて違う値",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "レッスンの学習プロセスでは搾取よりも探索を行ったほうが良かった",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 47,
+        "title": "強化 2: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "チェスや囲碁は連続した状態を持つゲームである",
+            "answerOptions": [
+              {
+                "answerText": "正しい",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "正しくない",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "カートポール問題とは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "外れ値を排除するプロセス",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "買い物かごを最適化する方法",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "バランシングの簡易版",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ゲームの中で起こりうる状態における様々なシナリオを行うために使えるツールは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "推測と確認",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "シミュレーション環境",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "状態遷移テスト",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 48,
+        "title": "強化 2: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "ある環境で起こりうるすべての状態を定義する場所はどこでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "メソッド",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "アクションスペース",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "アクションリスト",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "辞書のキーバリューとして使ったペアは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "キーに(state, action)、バリューにQテーブルのエントリ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "キーにstate、バリューにaction",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "キーにqvalues関数の値、バリューにaction",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Q学習で使用したハイパーパラメータは何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "Qテーブルの値・現在の報酬・ランダムなアクション",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "学習率・割引率・探索/搾取率",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "累積報酬・学習率・探索率",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 49,
+        "title": "実世界への応用: 講義前の小テスト",
+        "quiz": [
+          {
+            "questionText": "金融業界における機械学習の応用例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "自然言語処理を使ったカスタマージャーニーのパーソナライズ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "線形回帰を使った健康管理",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時系列を使ったエネルギー管理",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "再入院を管理するために病院で使える機械学習の手法は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "クラスタリング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時系列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "自然言語処理",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "エネルギー管理に時系列を使用する例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "動物のモーションセンシング",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "スマートパーキングメーター",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "森林火災の追跡",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 50,
+        "title": "実世界への応用: 講義後の小テスト",
+        "quiz": [
+          {
+            "questionText": "クレジットカードの不正利用を検出するために使用できる機械学習の手法はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "回帰",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタリング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "自然言語処理",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "森林管理で例示されている機械学習の手法はどれでしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "強化学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時系列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "自然言語処理",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ヘルスケア業界における機械学習の応用例は何でしょうか？",
+            "answerOptions": [
+              {
+                "answerText": "回帰を使った学生の行動予測",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "分類器を使った臨床試験の管理",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "分類器を使った動物のモーションセンシング",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  }
+]
diff --git a/quiz-app/src/assets/translations/tr.json b/quiz-app/src/assets/translations/tr.json
index 050bbd2a2..aa479e281 100644
--- a/quiz-app/src/assets/translations/tr.json
+++ b/quiz-app/src/assets/translations/tr.json
@@ -412,7 +412,7 @@
 							},
 							{
 								"answerText": "önyargı için potansiyel bir sebebi keşfedebilirsiniz",
-								"isCorrect": "true"
+								"isCorrect": "false"
 							},
 							{
 								"answerText": "bunların her ikisi",
@@ -1092,7 +1092,7 @@
 						"answerOptions": [
 							{
 								"answerText": "veri noktalarını birden çok sınıfa sınıflandırma görevi",
-								"isCorrect": "true"
+								"isCorrect": "false"
 							},
 							{
 								"answerText": "veri noktalarını birkaç sınıftan birine sınıflandırma görevi",
diff --git a/translations/README.it.md b/translations/README.it.md
new file mode 100644
index 000000000..d8e37383f
--- /dev/null
+++ b/translations/README.it.md
@@ -0,0 +1,123 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# Machine Learning per Principianti - Un Programma di Studio
+
+> 🌍 Viaggio intorno al mondo esplorando Machine Learning per mezzo delle culture mondiali 🌍
+
+Azure Cloud Advocates in Microsoft sono lieti di offrire un programma di studi di 12 settimane, 24 lezioni (più una!) tutto su **Machine Learning**. In questo programma di studi, si imparerà di ciò che a volte è chiamato **machine learning classico**, usando principalmente Scikit-learn come libreria ed evitando il deep learning, che è coperto nel nostro prossimo programma di studi "AI per principianti". Queste lezioni si accoppiano anche con il programma di studi di prossima uscita "Data Science per principianti"!
+
+Si viaggerà insieme in tutto il mondo mentre si applicano queste tecniche classiche ai dati da molte aree del mondo. Ogni lezione include quiz pre e post lezione, istruzioni scritte per completare la lezione, una soluzione, un compito e altro ancora. La pedagogia basata su progetto consente di imparare durante la costruzione, un modo comprovato per memorizzare nuove conoscenze.
+
+**✍️ Un grazie di cuore ai nostri autori** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan, e Amy Boyd
+
+**🎨 Grazie anche ai nostri illustratori** Tomomi Imura, Dasani Madipalli, e Jen Looper
+
+**🙏 Un ringraziamento speciale 🙏 agli autori di Microsoft Student Ambassador, revisori e collaboratori per i contenuti**, in particolare Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, e Snigdha Agarwal
+
+**🤩 Un grazie supplementare al Microsoft Student Ambassador Eric Wanjau per le nostre lezioni su R!** 
+
+---
+
+# Per Iniziare
+
+**Studenti**, per utilizzare questo programma di studi, eseguire il fork dell'intero repo sul proprio account GitHub e completare gli esercizi da soli o in gruppo:
+
+- Iniziare con un quiz pre-lezione.
+- Leggere la lezione e completare le attività, facendo una pausa di riflessione in ogni controllo della conoscenza.
+- Provare a creare i progetti capendo le lezioni piuttosto che eseguire il codice della soluzione; comunque quel codice è disponibile nelle cartelle `/solution` in ogni lezione orientata al progetto.
+- Fare il quiz post-lezione.
+- Completare la sfida.
+- Completare il compito.
+- Dopo il completamento di un gruppo di lezioni, visitare il  [Forum di discussione](https://github.com/microsoft/ML-For-Beginners/discussions) e imparare ad alta voce riempiendo la rubrica di Pat appropriata. Un 'PAT' è uno Strumento di valutazione del progresso che è una rubrica che si compila per promuovere il proprio apprendimento. Si può interagire anche in altri PAT in modo da imparare assieme.
+
+> Per ulteriori approfondimenti, si raccomanda di sequire i seguenti moduli e percorsi di apprendimento [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa).
+
+**Insegnanti**, sono stati [inclusi alcuni suggerimenti](for-teachers.md) su come usare questo programma di studi.
+
+---
+
+## Incontrare la squadra
+
+[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+
+> 🎥 Fare click sull'immagine qui sopra per un video sul progetto e sulle persone che lo hanno creato!
+
+---
+
+## Pedagogia
+
+Sono stati scelti due principi pedagogici durante la creazione di questo programma di studi: assicurandosi che sia pratico **basato su progetto** e che includa **quiz frequenti**. Inoltre, questo programma di studi ha un **tema** comune per conferirgli coesione.
+
+Assicurandosi che il contenuto si allinei con i progetti, il processo è reso più coinvolgente per gli studenti e la conservazione dei concetti sarà aumentata. Inoltre, un quiz di poca difficoltà prima di una lezione imposta l'intenzione dello studente verso l'apprendimento di un argomento, mentre un secondo quiz dopo la lezione garantisce ulteriore ritenzione. Questo programma di studi è stato progettato per essere flessibile e divertente e può essere seguito in tutto o in parte. I progetti iniziano piccoli e diventano sempre più complessi entro la fine del ciclo di 12 settimane. Questo programma di studi include anche un poscritto sulle applicazioni del mondo reale di ML, che può essere utilizzata come credito extra o come base per la discussione.
+
+> Consultare Le linee guida del [Codice di Condotta](CODE_OF_CONDUCT.md), per [Collaborare](CONTRIBUTING.md), e [Tradurre](TRANSLATIONS.md). Un feedback costruttivo sarà accolto con piacere!
+
+## Ogni lezione include:
+
+- sketchNote opzionale
+- video supplementare opzionale
+- quiz di riscaldamento pre-lezione
+- lezione scritta
+- per lezioni basate su progetto, guide passo-passo su come costruire il progetto
+- controlli della conoscenza
+- una sfida
+- lettura supplementare
+- compito
+- quiz post-lezione
+
+> **Una nota sui quiz**: Tutti i quiz sono contenuti [in questa app](https://white-water-09ec41f0f.azurestaticapps.net/), per un totale di 50 quiz con tre domande ciascuno. Sono collegati all'interno delle lezioni ma l'app può essere eseguita localmente; seguire le istruzioni nella cartella `quiz-app`.
+
+| Numero Lezione |                           Argomento                            |                   Gruppo Lezioni                   | Obiettivi di Apprendimento                                                                                                             |                     Lezioni Collegate                     |     Autore     |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |              Introduzione a machine learning              |      [Introduzione](../1-Introduction/translations/README.it.md)       | Apprendere i concetti di base dietro machine learning                                                                                |   [lezione](../1-Introduction/1-intro-to-ML/translations/README.it.md)    |    Muhammad    |
+|      02       |              La storia di machine learning               |      [Introduzione](../1-Introduction/translations/README.it.md)       | Apprendere la storia alla base di questo campo                                                                                         |  [lezione](../1-Introduction/2-history-of-ML/translations/README.it.md)   |  Jen e Amy   |
+|      03       |               Equità e machine learning                |      [Introduzione](../1-Introduction/translations/README.it.md)       | Quali sono gli importanti quesiti filosofici attorno all'equità che gli studenti dovrebbero prendere in considerazione quando si costruiscono e applicano i modelli ML? |     [lezione](../1-Introduction/3-fairness/translations/README.it.md)     |     Tomomi     |
+|      04       |              Tecniche di machine learning               |      [Introduzione](../1-Introduction/translations/README.it.md)       | Quali tecniche usano i ricercatori ML per costruire modelli ML?                                                                       | [lezione](../1-Introduction/4-techniques-of-ML/translations/README.it.md) | Chris e Jen  |
+|      05       |                 Introduzione alla regressione                 |        [Regressione](../2-Regression/translations/README.it.md)         | Iniziare con Python e Scikit-learn per i modelli di regressione                                                                  |       [lezione](../2-Regression/1-Tools/translations/README.it.md)        |      Jen       |
+|      06       |              Prezzi della zucca del Nord America 🎃               |        [Regressione](../2-Regression/translations/README.it.md)         | Visualizzare e pulire i dati in preparazione per ML                                                                                  |        [lezione](../2-Regression/2-Data/translations/README.it.md)        |      Jen       |
+|      07       |              Prezzi della zucca del Nord America 🎃               |        [Regressione](../2-Regression/translations/README.it.md)         | Costruire modelli di regressione lineare e polinomiale                                                                                   |       [lezione](../2-Regression/3-Linear/translations/README.it.md)       |      Jen       |
+|      08       |              Prezzi della zucca del Nord America 🎃               |        [Regressione](../2-Regression/translations/README.it.md)         | Costruire un modello di regressione logistica                                                                                               |      [lezione](../2-Regression/4-Logistic/translations/README.it.md)      |      Jen       |
+|      09       |                        Una App web 🔌                         |           [App Web](../3-Web-App/translations/README.it.md)            | Costruire un'App web per utilizzare il proprio modello addestrato                                                                                       |        [lezione](../3-Web-App/1-Web-App/translations/README.it.md)        |      Jen       |
+|      10       |               Introduzione alla classificazione               |    [Classificazione](../4-Classification/translations/README.it.md)     | Pulire, preparare e visualizzare i dati; introduzione alla classificazione                                                            |  [lezione](../4-Classification/1-Introduction/translations/README.it.md)  | Jen e Cassie |
+|      11       |           Deliziose cucine asiatiche e indiane 🍜            |    [Classificazione](../4-Classification/translations/README.it.md)     | Introduzione ai classificatori                                                                                                     | [lezione](../4-Classification/2-Classifiers-1/translations/README.it.md)  | Jen e Cassie |
+|      12       |           Deliziose cucine asiatiche e indiane 🍜            |    [Classificazione](../4-Classification/translations/README.it.md)     | Ancora classificatori                                                                                                                | [lezione](../4-Classification/3-Classifiers-2/translations/README.it.md)  | Jen e Cassie |
+|      13       |           Deliziose cucine asiatiche e indiane 🍜            |    [Classificazione](../4-Classification/translations/README.it.md)     | Costruire un'App web di raccomandazione usando il proprio modello                                                                                    |    [lezione](../4-Classification/4-Applied/translations/README.it.md)     |      Jen       |
+|      14       |                 Introduzione al clustering.                 |        [Clustering](../5-Clustering/translations/README.it.md)         | Pulire, preparare e visualizzare i dati; introduzione al clustering.                                                                |     [lezione](../5-Clustering/1-Visualize/translations/README.it.md)      |      Jen       |
+|      15       |            Esplorare i gusti musicali nigeriani 🎧             |        [Clustering](../5-Clustering/translations/README.it.md)         | Esplorare il metodo di clustering K-Means                                                                                           |      [lezione](../5-Clustering/2-K-Means/translations/README.it.md)       |      Jen       |
+|      16       |       Introduzione all'elaborazione naturale del linguaggio ☕️        |   [Elaborazione del linguaggio naturale](../6-NLP/translations/README.it.md)    | Imparare le basi di NLP costruendo un semplice bot                                                                             |    [lezione](../6-NLP/1-Introduction-to-NLP/translations/README.it.md)    |    Stephen     |
+|      17       |                     Attività NLP comuni ☕️                     |   [Elaborazione del linguaggio naturale](../6-NLP/translations/README.it.md)    | Approfondire la conoscenza dell'NLP comprendendo i compiti comuni richiesti quando si tratta di gestire strutture linguistiche                          |           [lezione](../6-NLP/2-Tasks/translations/README.it.md)           |    Stephen     |
+|      18       |            Traduzione e analisi del sentimento ♥️            |   [Elaborazione del linguaggio naturale](../6-NLP/translations/README.it.md)    | Traduzione e analisi del sentimento con Jane Austen                                                                             |   [lezione](../6-NLP/3-Translation-Sentiment/translations/README.it.md)   |    Stephen     |
+|      19       |                Hotel romantici dell'Europa ♥️                 |   [Elaborazione del linguaggio naturale](../6-NLP/translations/README.it.md)    | Analisi del sentimento con le recensioni di hotel 1                                                                                         |      [lezione](../6-NLP/4-Hotel-Reviews-1/translations/README.it.md)      |    Stephen     |
+|      20       |                Hotel romantici dell'Europa ♥️                 |   [Elaborazione del linguaggio naturale](../6-NLP/translations/README.it.md)    | Analisi del sentimento con le recensioni di hotel 2                                                                                         |      [lezione](../6-NLP/5-Hotel-Reviews-2/translations/README.it.md)      |    Stephen     |
+|      21       |          Introduzione alle previsioni delle serie temporali           |        [Time series](../7-TimeSeries/translations/README.it.md)        | Introduzione alle previsioni delle serie temporali                                                                                         |    [lezione](../7-TimeSeries/1-Introduction/translations/README.it.md)    |   Francesca    |
+|      22       | ⚡️ Utilizzo energetico mondiale ⚡️ - previsione di serie temporali con ARIMA |        [Time series](../7-TimeSeries/translations/README.it.md)        | Previsione di serie temporali con ARIMA                                                                                              |       [lezione](../7-TimeSeries/2-ARIMA/translations/README.it.md)        |   Francesca    |
+|      23       |           Introduzione al reinforcement learning           | [Reinforcement learning](../8-Reinforcement/translations/README.it.md) | Introduzione al reinforcement learning con Q-Learning                                                                          |    [lezione](../8-Reinforcement/1-QLearning/translations/README.it.md)    |     Dmitry     |
+|      24       |                Aiutare Pierino a evitare il lupo! 🐺                | [Reinforcement learning](../8-Reinforcement/translations/README.it.md) | Reinforcement learning Gym                                                                                                      |       [lezione](../8-Reinforcement/2-Gym/translations/README.it.md)       |     Dmitry     |
+|  Poscritto   |          Scenari e applicazioni ML del mondo reale          |      [ML in natura](../9-Real-World/translations/README.it.md)       | Applicazioni interessanti e rivelanti applicazioni di ML classico del mondo reale                                                               |    [lezione](../9-Real-World/1-Applications/translations/README.it.md)    |      Team      |
+
+## Accesso offline
+
+Si può eseguire questa documentazione offline usando [Docsify](https://docsify.js.org/#/). Effettuare il fork di questo repo, [installare Docsify](https://docsify.js.org/#/quickstart) sulla propria macchina locale, quindi nella cartella radice di questo repo digitare `docsify serve`. Il sito web sarà servito sulla porta 3000 di localhost: `localhost:3000`.
+
+## PDF
+
+Si può trovare un pdf con il programma di studio e collegamenti [qui](pdf/readme.pdf).
+
+## Cercasi aiuto!
+
+Si vorrebbe contribuire a una traduzione? Per favore leggere le [linee guida di traduzione](TRANSLATIONS.md) e aggiungere eventuale input [qui](https://github.com/microsoft/ML-For-Beginners/issues/71).
+
+## Altri Programmi di Studi\
+
+Il nostro team produce altri programmi di studi! Dare un occhiatat:
+
+- [Sviluppo Web per Principianti](https://aka.ms/webdev-beginners)
+- [IoT per Principianti](https://aka.ms/iot-beginners)
diff --git a/translations/README.ja.md b/translations/README.ja.md
new file mode 100644
index 000000000..80ae38e9e
--- /dev/null
+++ b/translations/README.ja.md
@@ -0,0 +1,121 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# 初心者のための機械学習 - カリキュラム
+
+> 🌍 世界の文化に触れながら機械学習を探求する旅 🌍
+
+マイクロソフトの Azure Cloud Advocates では、12週間、24レッスンの**機械学習**に関するカリキュラムを提供しています。このカリキュラムでは、今後公開する予定の「初心者のためのAI」で扱う深層学習を避け、主に Scikit-learn ライブラリを使用した**古典的機械学習**と呼ばれるものについて学びます。同様に公開予定の「初心者のためのデータサイエンス」と合わせてご活用ください！
+
+世界各地のデータに古典的な手法を適用しながら、一緒に世界を旅してみましょう。各レッスンには、レッスン前後の小テストや、レッスンを完了するための指示・解答・課題などが含まれています。新しいスキルを「定着」させるものとして実証されているプロジェクトベースの教育法によって、構築しながら学ぶことができます。
+
+**✍️ 著者の皆様に心から感謝いたします** Jen Looper さん、Stephen Howell さん、Francesca Lazzeri さん、Tomomi Imura さん、Cassie Breviu さん、Dmitry Soshnikov さん、Chris Noring さん、Ornella Altunyan さん、そして Amy Boyd さん
+
+**🎨 イラストレーターの皆様にも心から感謝いたします** Tomomi Imura さん、Dasani Madipalli さん、そして Jen Looper さん
+
+**🙏 Microsoft Student Ambassador の著者・査読者・コンテンツ提供者の皆様に特に感謝いたします 🙏** 特に、Rishit Dagli さん、Muhammad Sakib Khan Inan さん、Rohan Raj さん、Alexandru Petrescu さん、Abhishek Jaiswal さん、Nawrin Tabassum さん、Ioan Samuila さん、そして Snigdha Agarwal さん
+
+---
+
+# はじめに
+
+**学生の皆さん**、このカリキュラムを利用するには、自分のGitHubアカウントにリポジトリ全体をフォークして、一人もしくはグループで演習を完了させてください。
+
+- 講義前の小テストから始めてください。
+- 知識を確認するたびに立ち止まったり振り返ったりしながら、講義を読んで各アクティビティを完了させてください。
+- 解答のコードをただ実行するのではなく、レッスンを理解してプロジェクトを作成するようにしてください。なお、解答のコードは、プロジェクトに紐づく各レッスンの `/solution` フォルダにあります。
+- 講義後の小テストを受けてください。
+- チャレンジを完了させてください。
+- 課題を完了させてください。
+- レッスングループの完了後は [Discussionボード](https://github.com/microsoft/ML-For-Beginners/discussions) にアクセスし、適切なPAT表に記入することで「声に出して学習」してください。"PAT" とは Progress Assessment Tool（進捗評価ツール）の略で、学習を促進するために記入する表のことです。他のPATにリアクションすることもできるので、共に学ぶことが可能です。
+
+> さらに学習を進める場合は、[Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) のラーニングパスに従うことをお勧めします。
+
+**先生方**、このカリキュラムをどのように使用するか、[いくつかの提案](../for-teachers.md) があります。
+
+---
+
+## チームの紹介
+
+[![プロモーションビデオ](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "プロモーションビデオ")
+
+> 🎥 上の画像をクリックすると、このプロジェクトと、プロジェクトを作った人たちについてのビデオを観ることができます！
+
+---
+
+## 教育法
+
+このカリキュラムを構築するにあたり、私たちは2つの教育方針を選びました。**プロジェクトベース**の体験と、**頻繁な小テスト**を含むことです。さらにこのカリキュラムには、まとまりを持たせるための共通の**テーマ**があります。
+
+内容とプロジェクトとの整合性を保つことで、学生にとって学習プロセスがより魅力的になり、概念の定着度が高まります。さらに、授業前の軽い小テストは学生の学習意欲を高め、授業後の2回目の小テストはより一層の定着につながります。このカリキュラムは柔軟かつ楽しいものになるようデザインされており、すべて、もしくは一部を受講することが可能です。プロジェクトは小さなものから始まり、12週間の間に少しずつ複雑なものになっていきます。また、このカリキュラムには機械学習の実世界への応用に関するあとがきも含んでおり、追加の単位あるいは議論の題材として使用できます。
+
+> [行動規範](../CODE_OF_CONDUCT.md)、[貢献](../CONTRIBUTING.md)、[翻訳](../TRANSLATIONS.md) のガイドラインをご覧ください。建設的なご意見をお待ちしております！
+
+## 各レッスンの内容
+
+- オプションのスケッチノート
+- オプションの補足ビデオ
+- 講義前の小テスト
+- 成文のレッスン
+- プロジェクトベースのレッスンを行うため、プロジェクトの構築方法に関する段階的なガイド
+- 知識の確認
+- チャレンジ
+- 副読本
+- 課題
+- 講義後の小テスト
+
+> **小テストに関する注意**: すべての小テストは [このアプリ](https://white-water-09ec41f0f.azurestaticapps.net/) に含まれており、各3問からなる50個の小テストがあります。これらはレッスン内からリンクされていますが、アプリをローカルで実行することもできます。`quiz-app` フォルダ内の指示に従ってください。
+
+| レッスン番号 |                   トピック                   |                    レッスングループ                    | 学習の目的                                                                                 |                            関連するレッスン                             |      著者      |
+| :----------: | :------------------------------------------: | :----------------------------------------------------: | ------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------: | :------------: |
+|      01      |               機械学習への導入               |  [導入](../1-Introduction/translations/README.ja.md)   | 機械学習の基本的な概念を学ぶ                                                               |  [レッスン](../1-Introduction/1-intro-to-ML/translations/README.ja.md)  |    Muhammad    |
+|      02      |                機械学習の歴史                |  [導入](../1-Introduction/translations/README.ja.md)   | この分野の背景にある歴史を学ぶ                                                             | [レッスン](../1-Introduction/2-history-of-ML/translations/README.ja.md) |  Jen and Amy   |
+|      03      |               公平性と機械学習               |  [導入](../1-Introduction/translations/README.ja.md)   | 機械学習モデルを構築・適用する際に学生が考慮すべき、公平性に関する重要な哲学的問題は何か？ |   [レッスン](../1-Introduction/3-fairness/translations/README.ja.md)    |     Tomomi     |
+|      04      |                機械学習の手法                |  [導入](../1-Introduction/translations/README.ja.md)   | 機械学習の研究者はどのような手法でモデルを構築しているか？                                 |         [レッスン](1-Introduction/4-techniques-of-ML/README.md)         | Chris and Jen  |
+|      05      |                 回帰への導入                 |           [回帰](../2-Regression/README.md)            | 回帰モデルをPythonと Scikit-learn で始める                                                 |      [レッスン](../2-Regression/1-Tools/translations/README.ja.md)      |      Jen       |
+|      06      |            北米のカボチャの価格 🎃            |           [回帰](../2-Regression/README.md)            | 機械学習に向けてデータを可視化してクリーニングする                                         |      [レッスン](../2-Regression/2-Data/translations/README.ja.md)       |      Jen       |
+|      07      |            北米のカボチャの価格 🎃            |           [回帰](../2-Regression/README.md)            | 線形および多項式回帰モデルを構築する                                                       |               [レッスン](2-Regression/3-Linear/README.md)               |      Jen       |
+|      08      |            北米のカボチャの価格 🎃            |           [回帰](../2-Regression/README.md)            | ロジスティック回帰モデルを構築する                                                         |            [レッスン](../2-Regression/4-Logistic/README.md)             |      Jen       |
+|      09      |                 Webアプリ 🔌                  |          [Web アプリ](../3-Web-App/README.md)          | 学習したモデルを使用するWebアプリを構築する                                                |              [レッスン](../3-Web-App/1-Web-App/README.md)               |      Jen       |
+|      10      |                 分類への導入                 |         [分類](../4-Classification/README.md)          | データをクリーニング・前処理・可視化する。分類への導入                                     |        [レッスン](../4-Classification/1-Introduction/README.md)         | Jen and Cassie |
+|      11      |       美味しいアジア料理とインド料理 🍜       |         [分類](../4-Classification/README.md)          | 分類器への導入                                                                             |        [レッスン](../4-Classification/2-Classifiers-1/README.md)        | Jen and Cassie |
+|      12      |       美味しいアジア料理とインド料理 🍜       |         [分類](../4-Classification/README.md)          | その他の分類器                                                                             |        [レッスン](../4-Classification/3-Classifiers-2/README.md)        | Jen and Cassie |
+|      13      |       美味しいアジア料理とインド料理 🍜       |         [分類](../4-Classification/README.md)          | モデルを使用して推薦Webアプリを構築する                                                    |           [レッスン](../4-Classification/4-Applied/README.md)           |      Jen       |
+|      14      |            クラスタリングへの導入            |      [クラスタリング](../5-Clustering/README.md)       | データをクリーニング・前処理・可視化する。クラスタリングへの導入                           |            [レッスン](../5-Clustering/1-Visualize/README.md)            |      Jen       |
+|      15      |       ナイジェリアの音楽的嗜好を探る 🎧       |      [クラスタリング](../5-Clustering/README.md)       | K-Means法を探る                                                                            |             [レッスン](../5-Clustering/2-K-Means/README.md)             |      Jen       |
+|      16      |            自然言語処理への導入 ☕️            |           [自然言語処理](../6-NLP/README.md)           | 単純なボットを構築して自然言語処理の基礎を学ぶ                                             |          [レッスン](../6-NLP/1-Introduction-to-NLP/README.md)           |    Stephen     |
+|      17      |        自然言語処理の一般的なタスク ☕️        |           [自然言語処理](../6-NLP/README.md)           | 言語構造を扱う際に必要となる一般的なタスクを理解することで、自然言語処理の知識を深める     |                 [レッスン](../6-NLP/2-Tasks/README.md)                  |    Stephen     |
+|      18      |               翻訳と感情分析 ♥️               |           [自然言語処理](../6-NLP/README.md)           | ジェーン・オースティンの翻訳と感情分析                                                     |         [レッスン](../6-NLP/3-Translation-Sentiment/README.md)          |    Stephen     |
+|      19      |      ヨーロッパのロマンチックなホテル ♥️      |           [自然言語処理](../6-NLP/README.md)           | ホテルのレビューの感情分析 1                                                               |            [レッスン](../6-NLP/4-Hotel-Reviews-1/README.md)             |    Stephen     |
+|      20      |      ヨーロッパのロマンチックなホテル ♥️      |           [自然言語処理](../6-NLP/README.md)           | ホテルのレビューの感情分析 2                                                               |            [レッスン](../6-NLP/5-Hotel-Reviews-2/README.md)             |    Stephen     |
+|      21      |              時系列予測への導入              |        [Time series](../7-TimeSeries/README.md)        | 時系列予測への導入                                                                         |          [レッスン](../7-TimeSeries/1-Introduction/README.md)           |   Francesca    |
+|      22      | ⚡️ 世界の電力使用量 ⚡️ - ARIMAによる時系列予測 |        [Time series](../7-TimeSeries/README.md)        | ARIMAによる時系列予測                                                                      |              [レッスン](../7-TimeSeries/2-ARIMA/README.md)              |   Francesca    |
+|      23      |               強化学習への導入               | [Reinforcement learning](../8-Reinforcement/README.md) | Q学習を使った強化学習への導入                                                              |          [レッスン](../8-Reinforcement/1-QLearning/README.md)           |     Dmitry     |
+|      24      | ピーターが狼を避けるのを手伝ってください！ 🐺 | [Reinforcement learning](../8-Reinforcement/README.md) | 強化学習ジム                                                                               |             [レッスン](../8-Reinforcement/2-Gym/README.md)              |     Dmitry     |
+|  Postscript  |        実世界の機械学習シナリオと応用        |      [ML in the Wild](../9-Real-World/README.md)       | 興味深くて意義のある、古典的機械学習の実世界での応用                                       |          [レッスン](../9-Real-World/1-Applications/README.md)           |      Team      |
+
+## オフラインアクセス
+
+[Docsify](https://docsify.js.org/#/) を使うと、このドキュメントをオフラインで実行できます。このリポジトリをフォークして、ローカルマシンに [Docsify をインストール](https://docsify.js.org/#/quickstart) し、このリポジトリのルートフォルダで `docsify serve` と入力してください。ローカルホストの3000番ポート、つまり `localhost:3000` でWebサイトが起動します。
+
+## PDF
+
+カリキュラムのPDFへのリンクは [こちら](../pdf/readme.pdf)。
+
+## ヘルプ募集！
+
+翻訳をしてみませんか？[翻訳ガイドライン](../TRANSLATIONS.md) をご覧の上、[こちら](https://github.com/microsoft/ML-For-Beginners/issues/71) でお知らせください。
+
+## その他のカリキュラム
+
+私たちはその他のカリキュラムも提供しています！ぜひチェックしてみてください。
+
+- [初心者のためのWeb開発](https://aka.ms/webdev-beginners)
+- [初心者のためのIoT](https://aka.ms/iot-beginners)
diff --git a/translations/README.ko.md b/translations/README.ko.md
new file mode 100644
index 000000000..b83153076
--- /dev/null
+++ b/translations/README.ko.md
@@ -0,0 +1,123 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# Machine Learning for Beginners (입문자를 위한 머신러닝) - 커리큘럼
+
+> 🌍 세계의 문화로 머신러닝을 알아가면서 전 세계를 여행합니다 🌍
+
+Microsoft의 Azure Cloud Advocates는 **Machine Learning**에 대한 모든 12-주, 24-강의 (하나 더!) 커리큘럼을 제공해서 만족합니다. 이 커리큘럼에서는, 곧 만들어질 'AI for Beginners'에서 커버하지 않는 딥러닝을 제외한, **classic machine learning**이라고 불리는 것을 Scikit-learn 라이브러리 위주로 배우게 됩니다. 이 강의에서 곧 만들어질 'Data Science for Beginners' 커리큘럼과 같이 봅니다!
+
+월드의 많은 영역에 데이터를 적용하면서 이러한 classic 기술로 전 세계를 여행합니다. 각 강의에는 강의 전과 후에 진행하는 퀴즈, 강의를 마치기 위한 설명, 솔루션, 과제 등 있습니다. 새로운 스킬을 'stick'할 수 있다고 증명된 프로젝트-기반 교육학에 의하여 만들면서 배울 수 있습니다.
+
+**✍️ Hearty thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan, and Amy Boyd
+
+**🎨 Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
+
+**🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+
+**🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!** 
+
+---
+
+# 시작하기
+
+**학생**은, 이 커리큘럼을 사용하기 위해서, 전체 저장소를 자신의 GitHub 계정으로 포크하고 혼자하거나 그룹으로 같이 연습합니다:
+
+- 강의 전 퀴즈를 시작합니다.
+- 강의를 읽고, 각 지식 점검에서 멈추고 습득해서 활동을 끝냅니다.
+- 솔루션 코드를 실행하는 것보다 강의를 이해해서 프로젝트를 만들어봅니다; 그러나 코드는 각 프로젝트-지향 강의마다 `/solution` 폴더에 존재합니다.
+- 강의 후 퀴즈를 해봅니다.
+- 도전을 끝내봅니다.
+- 과제를 끝내봅니다.
+- 강의 그룹을 끝내면, [Discussion board](https://github.com/microsoft/ML-For-Beginners/discussions)를 방문하고 적절한 PAT rubric를 채워서 "learn out loud" 합니다. 'PAT'은 심화적으로 배우려고 작성하는 rubric인 Progress Assessment 도구 입니다. 같이 배울 수 있게 다른 PAT으로도 할 수 있습니다.
+
+> 더 배우기 위해서, [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 모듈과 학습 경로를 따르는 것을 추천합니다.
+
+**선생님**은, 이 커리큘럼으로 사용하기 위해서 [included some suggestions](../for-teachers.md)를 준비했습니다.
+
+---
+
+## Team 만나기
+
+[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+
+> 🎥 프로젝트와 이 내용을 작성한 사람들에 대한 영상을 보려면 위 이미지를 클릭합니다!
+
+---
+
+## 교육학
+
+이 커리큘럼을 만드는 동안 2가지 교육학 원칙을 선택했습니다: **project-based**에서 실습하고 **frequent quizzes**가 포함되었는지 확인합니다. 추가적으로, 이 커리큘럼은 통합적으로 보이기 위해서 공통적인 **theme**가 있습니다.
+
+컨텐츠가 프로젝트와 맞게 유지되므로, 프로세스는 학생들이 더 끌리고 개념의 집중도가 높아집니다. 추가적으로, 강의 전 가벼운 퀴즈는 학생들이 공부에 집중하게 해주고, 강의 후 두 번째 퀴즈는 계속 집중하게 합니다. 이 커리큘럼은 유연하고 재밌게 디자인되었으며 다 배우거나 일부만 배울 수 있습니다. 프로젝트는 작게 시작해서 12주 사이클로 끝날 때까지 점점 복잡해집니다. 이 커리큘럼은 추가 크레딧이나 토론의 기초로 사용할 수 있는, ML의 현실에 적용한 postscript도 포함되어 있습니다.
+
+> [Code of Conduct](../CODE_OF_CONDUCT.md), [Contributing](../CONTRIBUTING.md)과, [Translation](../TRANSLATIONS.md) 가이드라인을 확인해봅니다. 건설적인 피드백을 환영합니다!
+
+## 각 강의에 포함된 내용:
+
+- 취사선택 스케치노트
+- 취사선택 추가 영상
+- 강의 전 준비 퀴즈
+- 강의 내용
+- 프로젝트-기반 강의라면, 프로젝트 제작 방식 step-by-step 지도
+- 지식 점검
+- 도전
+- 보충 내용
+- 과제
+- 강의 후 퀴즈
+
+> **퀴즈 참고사항**: 모든 퀴즈는 [in this app](https://white-water-09ec41f0f.azurestaticapps.net/)에 묶여있으며, 각 3개 질문으로 총 50개 퀴즈가 있습니다. 강의에 연결되어 있지만 퀴즈 앱은 로컬에서 수행할 수 있습니다; `quiz-app` 폴더의 설명을 따릅니다.
+
+| 강의 번호 |                           토픽                            |                   강의 그룹                   | 학습 목표                                                                                                             |                     연결 강의                     |     저자     |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |              머신러닝 소개              |      [소개](../1-Introduction/translations/README.ko.md)       | 머신러닝의 기초 컨셉을 배웁니다                                                                                |   [강의](../1-Introduction/1-intro-to-ML/translations/README.ko.md)    |    Muhammad    |
+|      02       |              머신러닝의 역사               |      [소개](../1-Introduction/translations/README.ko.md)       | 이 필드의 역사를 배웁니다                                                                                         |  [강의](../1-Introduction/2-history-of-ML/translations/README.ko.md)   |  Jen and Amy   |
+|      03       |               공정과 머신러닝                |      [소개](../1-Introduction/translations/README.ko.md)       | 학생들이 ML 모델을 만들고 적용할 때 고려해야 할 공정과 관련한 중요 철학적인 이슈는 무엇인가요? |     [강의](../1-Introduction/3-fairness/translations/README.ko.md)     |     Tomomi     |
+|      04       |              머신러닝의 기술               |      [소개](../1-Introduction/translations/README.ko.md)       | ML 연구원들이 ML 모델을 만들 때 사용할 기술은 무엇인가요?                                                                       | [강의](../1-Introduction/4-techniques-of-ML/translations/README.ko.md) | Chris and Jen  |
+|      05       |                 regression 소개                |        [Regression](../2-Regression/translations/README.ko.md)         | regression 모델을 위한 Python과 Scikit-learn으로 시작합니다                                                                  |       [강의](../2-Regression/1-Tools/translations/README.ko.md)        |      Jen       |
+|      06       |              북미의 호박 가격 🎃               |        [Regression](../2-Regression/translations/README.ko.md)         | ML을 준비하기 위해서 데이터를 시각화하고 정리합니다                                                                                  |        [강의](../2-Regression/2-Data/translations/README.ko.md)        |      Jen       |
+|      07       |              북미의 호박 가격 🎃               |        [Regression](../2-Regression/translations/README.ko.md)         | linear와 polynomial regression 모델을 만듭니다                                                                                   |       [강의](2-Regression/3-Linear/translations/README.ko.md)       |      Jen       |
+|      08       |              북미의 호박 가격 🎃               |        [Regression](../2-Regression/translations/README.ko.md)         | logistic regression 모델을 만듭니다                                                                                               |      [강의](../2-Regression/4-Logistic/translations/README.ko.md)      |      Jen       |
+|      09       |                        웹 앱 🔌                         |           [웹 앱](../3-Web-App/translations/README.ko.md)            | 훈련된 모델로 웹 앱을 만듭니다                                                                                       |        [강의](../3-Web-App/1-Web-App/translations/README.ko.md)        |      Jen       |
+|      10       |               classification 소개               |    [Classification](../4-Classification/translations/README.ko.md)     | 데이터 정리, 준비, 그리고 시각화; classification을 소개합니다                                                            |  [강의](../4-Classification/1-Introduction/translations/README.ko.md)  | Jen and Cassie |
+|      11       |           맛있는 아시아와 인도 요리 🍜            |    [Classification](../4-Classification/translations/README.ko.md)     | classifier를 소개합니다                                                                                                     | [강의](../4-Classification/2-Classifiers-1/translations/README.ko.md)  | Jen and Cassie |
+|      12       |           맛있는 아시아와 인도 요리 🍜            |    [Classification](../4-Classification/translations/README.ko.md)     | 더 많은 classifier                                                                                                                | [강의](../4-Classification/3-Classifiers-2/translations/README.ko.md)  | Jen and Cassie |
+|      13       |           맛있는 아시아와 인도 요리 🍜            |    [Classification](../4-Classification/translations/README.ko.md)     | 모델로 추천 웹 앱을 만듭니다                                                                                    |    [강의](../4-Classification/4-Applied/translations/README.ko.md)     |      Jen       |
+|      14       |                 clustering 소개                 |        [Clustering](../5-Clustering/translations/README.ko.md)         | 데이터 정리, 준비, 그리고 시각화; clustering을 소개합니다                                                                |     [강의](../5-Clustering/1-Visualize/translations/README.ko.md)      |      Jen       |
+|      15       |            나이지리아인의 음악 취향 알아보기 🎧             |        [Clustering](../5-Clustering/translations/README.ko.md)         | K-Means clustering 메소드를 탐색합니다                                                                                           |      [강의](../5-Clustering/2-K-Means/translations/README.ko.md)       |      Jen       |
+|      16       |       natural language processing 소개 ☕️        |   [Natural language processing](../6-NLP/translations/README.ko.md)    | 간단한 봇을 만들면서 NLP에 대하여 기본을 배웁니다                                                                             |    [강의](../6-NLP/1-Introduction-to-NLP/translations/README.ko.md)    |    Stephen     |
+|      17       |                     일반적인 NLP 작업 ☕️                     |   [Natural language processing](../6-NLP/translations/README.ko.md)    | 언어 구조를 다룰 때 필요한 일반 작업을 이해하면서 NLP 지식을 깊게 팝니다                          |           [강의](../6-NLP/2-Tasks/translations/README.ko.md)           |    Stephen     |
+|      18       |            번역과 감정 분석 ♥️            |   [Natural language processing](../6-NLP/translations/README.ko.md)    | Jane Austen을 통한 번역과 감정 분석                                                                             |   [강의](../6-NLP/3-Translation-Sentiment/translations/README.ko.md)   |    Stephen     |
+|      19       |                유럽의 로맨틱 호텔 ♥️                 |   [Natural language processing](../6-NLP/translations/README.ko.md)    | 호텔 리뷰를 통한 감정 분석 1                                                                                         |      [강의](../6-NLP/4-Hotel-Reviews-1/translations/README.ko.md)      |    Stephen     |
+|      20       |                유럽의 로맨틱 호텔 ♥️                 |   [Natural language processing](../6-NLP/translations/README.ko.md)    | 호텔 리뷰를 통한 감정 분석 2                                                                                         |      [강의](../6-NLP/5-Hotel-Reviews-2/translations/README.ko.md)      |    Stephen     |
+|      21       |          time series forecasting 소개           |        [Time series](../7-TimeSeries/translations/README.ko.md)        | time series forecasting을 소개합니다                                                                                         |    [강의](../7-TimeSeries/1-Introduction/translations/README.ko.md)    |   Francesca    |
+|      22       | ⚡️ 세계 전력 사용량 ⚡️ - ARIMA의 time series forecasting |        [Time series](../7-TimeSeries/translations/README.ko.md)        | ARIMA의 Time series forecasting                                                                                              |       [강의](../7-TimeSeries/2-ARIMA/translations/README.ko.md)        |   Francesca    |
+|      23       |           reinforcement learning 소개           | [Reinforcement learning](../8-Reinforcement/translations/README.ko.md) | Q-Learning의 reinforcement learning을 소개합니다                                                                          |    [강의](../8-Reinforcement/1-QLearning/translations/README.ko.md)    |     Dmitry     |
+|      24       |                늑대를 피하는 Peter 도와주기! 🐺                | [Reinforcement learning](../8-Reinforcement/translations/README.ko.md) | Gym에서 Reinforcement learning                                                                                                       |       [강의](../8-Reinforcement/2-Gym/translations/README.ko.md)       |     Dmitry     |
+|  Postscript   |          실생활 ML 시나리오와 애플리케이션           |      [야생의 ML](../9-Real-World/translations/README.ko.md)       | classical ML의 흥미롭게 드러나는 현실 애플리케이션                                                               |    [강의](../9-Real-World/1-Applications/translations/README.ko.md)    |      Team      |
+
+## 오프라인 접근
+
+[Docsify](https://docsify.js.org/#/)에서 문서를 오프라인으로 실행할 수 있습니다. 저장소를 포크해서 로컬 머신에 [install Docsify](https://docsify.js.org/#/quickstart)히고, 이 저장소의 최상위 폴더에서, `docsify serve` 입력합니다. 웹 사이트는 로컬호스트로 3000 포트에서 서버가 켜집니다: `localhost:3000`.
+
+## PDF
+
+[here](../pdf/readme.pdf)에서 링크가 있는 커리큘럼의 PDF를 찾습니다.
+
+## 도와주세요!
+
+번역에 기여하고 싶으신가요? [translation guidelines](../TRANSLATIONS.md)를 읽고 [here](https://github.com/microsoft/ML-For-Beginners/issues/71)에 입력해주세요. 
+
+## 기타 커리큘럼
+
+우리 팀에서는 다른 커리큘럼도 제작합니다! 확인해보세요!
+
+- [Web Dev for Beginners](https://aka.ms/webdev-beginners)
+- [IoT for Beginners](https://aka.ms/iot-beginners)
diff --git a/translations/README.pt.md b/translations/README.pt.md
index 6985cbf43..fc6e4e8d0 100644
--- a/translations/README.pt.md
+++ b/translations/README.pt.md
@@ -71,7 +71,7 @@ Ao garantir que o conteúdo esteja alinhado com os projetos, o processo torna-se
 - tarefa
 - teste pós-aula
 
-> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://jolly-sea-0a877260f.azurestaticapps.net), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
+> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://white-water-09ec41f0f.azurestaticapps.net/), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
 
 | Número de aula |                                Tópico                                 |                 Agrupamento de Aulas                  | Objetivos de aprendizagem                                                                                                            |                   Aula vinculada                    |    Autor     |
 | :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |
diff --git a/translations/README.tr.md b/translations/README.tr.md
new file mode 100644
index 000000000..6ac6fa6e7
--- /dev/null
+++ b/translations/README.tr.md
@@ -0,0 +1,119 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# Yeni Başlayanlar için Makine Öğrenimi - Bir Eğitim Programı
+
+> :earth_africa: Dünya kültürleri sayesinde Makine Öğrenimini keşfederken dünyayı gezin :earth_africa:
+
+Microsoft'taki Azure Cloud Destekleyicileri tamamen **Makine Öğrenimi** hakkında olan 12 hafta ve 24 derslik eğitim programını sunmaktan memnuniyet duyar. Bu eğitim programında, kütüphane olarak temelde Scikit-learn kullanarak ve yakında çıkacak olan 'Yeni Başlayanlar için Yapay Zeka' dersinde anlatılan derin öğrenmeden uzak durarak, zaman zaman adlandırıldığı şekliyle, **klasik makine öğrenimi**ni öğreneceksiniz. Bu dersleri yakında çıkacak olan 'Yeni Başlayanlar için Veri Bilimi' eğitim programımızla da birleştirin!
+
+Biz bu klasik teknikleri dünyanın birçok alanından verilere uygularken bizimle dünyayı gezin. Her bir ders, ders başı ve ders sonu kısa sınavlarını, dersi tamamlamak için yazılı yönergeleri, bir çözümü, bir ödevi ve daha fazlasını içerir. Yeni becerilerin 'yerleşmesi' için kanıtlanmış bir yol olan proje temelli pedagojimiz, yaparken öğrenmenizi sağlar.
+
+**:writing_hand: Yazarlarımıza yürekten teşekkürler** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan, and Amy Boyd
+
+**:art: Çizerlerimize de teşekkürler** Tomomi Imura, Dasani Madipalli, and Jen Looper
+
+ **:pray: Microsoft Student Ambassador yazarlarımıza, eleştirmenlerimize ve içeriğe katkıda bulunanlara özel teşekkürler :pray:** özellikle Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+
+---
+# Başlarken
+
+**Öğrenciler**, bu eğitim programını kullanmak için, tüm yazılım havuzunu kendi GitHub hesabınıza çatallayın ve alıştırmaları kendiniz veya bir grup ile tamamlayın:
+
+- Bir ders öncesi kısa sınavı ile başlayın
+- Her bilgi kontrolünde durup derinlemesine düşünerek dersi okuyun ve etkinlikleri tamamlayın.
+- Çözüm kodunu çalıştırmaktansa dersleri kavrayarak projeleri yapmaya çalışın; yine de o çözüm kodu her proje yönelimli derste `/solution` klasörlerinde mevcut.
+- Ders sonrası kısa sınavını çözün
+- Meydan okumayı tamamlayın
+- Ödevi tamamlayın
+- Bir ders grubunu tamamladıktan sonra, [Tartışma Panosu](https://github.com/microsoft/ML-For-Beginners/discussions)'nu ziyaret edin ve uygun PAT yönergesini doldurarak "sesli öğrenin" (Yani, tamamen öğrenmeden önce öğrenme süreciniz üzerine derin düşünerek içgözlem ve geridönütlerle kendinizde farkındalık oluşturun.). 'PAT', bir Progress Assessment Tool'dur (Süreç Değerlendirme Aracı), öğrenmenizi daha ileriye taşımak için doldurduğunuz bir yönergedir. Diğer PAT'lere de karşılık verebilirsiniz, böylece beraber öğrenebiliriz.
+
+> İleri çalışma için, bu [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modüllerini ve öğrenme rotalarını takip etmenizi tavsiye ediyoruz.
+
+**Öğretmenler**, bu eğitim programının nasıl kullanılacağı hakkında [bazı öneriler ekledik](../for-teachers.md).
+
+---
+
+## Takımla Tanışın
+
+[![Tanıtım videosu](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+
+> :movie_camera: Proje ve projeyi yaratanlar hakkındaki video için yukarıdaki fotoğrafa tıklayın!
+
+---
+## Pedagoji
+
+Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **proje temelli** olduğundan ve **sık kısa sınavlar** içerdiğinden emin olmak. Ayrıca, bu eğitim programında tutarlılık sağlaması için genel bir **tema** var.
+
+İçeriğin projelerle uyumlu olduğuna emin olarak, süreç öğrenciler için daha ilgi çekici hale getirilmiştir ve kavramların akılda kalıcılığı artacaktır. Ayrıca, dersten önce ikincil değerli bir kısa sınav öğrencinin niyetini konuyu öğrenmek yaparken dersten sonra yapılan ikinci bir kısa sınav da akılda kalıcılığı sağlar. Bu eğitim programı esnek ve eğlenceli olacak şekilde hazırlanmıştır ve tümüyle veya kısmen işlenebilir. Projeler kolay başlar ve 12 haftalık zamanın sonuna doğru karmaşıklıkları gittikçe artar. Bu eğitim programı, Makine Öğreniminin gerçek hayattaki uygulamaları üzerine, ek puan veya tartışma için bir temel olarak kullanılabilecek bir ek yazı da içermektedir.
+
+> [Davranış Kuralları](../CODE_OF_CONDUCT.md)'mızı, [Katkıda Bulunma](../CONTRIBUTING.md) ve [Çeviri](../TRANSLATIONS.md) kılavuz ilkelerimizi inceleyin. Yapıcı geridönütlerinizi memnuniyetle karşılıyoruz!
+## Her bir ders şunları içermektedir:
+
+- isteğe bağlı eskiz notu
+- isteğe bağlı ek video
+- ders öncesi ısınma kısa sınavı
+- yazılı ders
+- proje temelli dersler için, projenin nasıl yapılacağına dair adım adım kılavuz
+- bilgi kontrolleri
+- bir meydan okuma
+- ek okuma
+- ödev
+- ders sonrası kısa sınavı
+
+> **Kısa sınavlar hakkında bir not**: Her biri üç sorudan oluşan ve toplamda 50 tane olan tüm kısa sınavlar [bu uygulamada](https://white-water-09ec41f0f.azurestaticapps.net/) bulunmaktadır. Derslerin içinden de bağlantı yoluyla ulaşılabilirler ancak kısa sınav uygulaması yerelde çalıştırılabilir; `quiz-app` klasöründeki yönergeleri takip edin.
+
+
+| Ders Numarası |                           Konu                             |                     Ders Gruplandırması                     |  Öğrenme Hedefleri                                                                                                              |                         Ders                          |     Yazar      |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |                   Makine Öğrenimi Giriş                    |         [Giriş](../1-Introduction/README.md)           | Makine öğreniminin temel kavramlarını öğrenmek                                                                                  |    [ders](../1-Introduction/1-intro-to-ML/README.md)     |    Muhammad    |
+|      02       |                   Makine Öğrenimi Tarihi                   |         [Giriş](../1-Introduction/README.md)           | Bu alanın altında yatan tarihi öğrenmek                                                                                         |   [ders](../1-Introduction/2-history-of-ML/README.md)    |  Jen and Amy   |
+|      03       |                 Eşitlik ve Makine Öğrenimi                 |         [Giriş](../1-Introduction/README.md)           | Öğrencilerin ML modelleri yaparken ve uygularken düşünmeleri gereken eşitlik hakkındaki önemli felsefi sorunlar nelerdir?       |      [ders](../1-Introduction/3-fairness/README.md)      |     Tomomi     |
+|      04       |               Makine Öğrenimi için Yöntemler               |         [Giriş](../1-Introduction/README.md)           | ML araştırmacıları ML modelleri üretmek için hangi yöntemleri kullanırlar?                                                      |  [ders](../1-Introduction/4-techniques-of-ML/README.md)  | Chris and Jen  |
+|      05       |                      Regresyona Giriş                      |         [Regresyon](../2-Regression/README.md)         | Regresyon modelleri için Python ve Scikit-learn'e başlamak                                                                      |        [ders](../2-Regression/1-Tools/README.md)         |      Jen       |
+|      06       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](../2-Regression/README.md)         | ML hazırlığı için verileri görselleştirmek ve temizlemek                                                                        |         [ders](../2-Regression/2-Data/README.md)         |      Jen       |
+|      07       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](../2-Regression/README.md)         | Doğrusal ve polinom regresyon modelleri yapmak                                                                                  |        [ders](../2-Regression/3-Linear/README.md)        |      Jen       |
+|      08       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](../2-Regression/README.md)         | Lojistik bir regresyon modeli yapmak                                                                                            |       [ders](../2-Regression/4-Logistic/README.md)       |      Jen       |
+|      09       |                    Bir Web Uygulaması :electric_plug:                    |        [Web Uygulaması](../3-Web-App/README.md)        | Eğittiğiniz modeli kullanmak için bir web uygulaması yapmak                                                                     |         [ders](../3-Web-App/1-Web-App/README.md)         |      Jen       |
+|      10       |                   Sınıflandırmaya Giriş                    |     [Sınıflandırma](../4-Classification/README.md)     | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; sınıflandırmaya giriş                                                  |   [ders](../4-Classification/1-Introduction/README.md)   | Jen and Cassie |
+|      11       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](../4-Classification/README.md)     | Sınıflandırıcılara giriş                                                                                                        |  [ders](../4-Classification/2-Classifiers-1/README.md)   | Jen and Cassie |
+|      12       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](../4-Classification/README.md)     | Daha fazla sınıflandırıcı                                                                                                       |  [ders](../4-Classification/3-Classifiers-2/README.md)   | Jen and Cassie |
+|      13       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](../4-Classification/README.md)     | Modelinizi kullanarak tavsiyede bulunan bir web uygulaması yapmak                                                               |     [ders](../4-Classification/4-Applied/README.md)      |      Jen       |
+|      14       |                      Kümelemeye Giriş                      |         [Kümeleme](../5-Clustering/README.md)          | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; kümelemeye giriş                                                       |      [ders](../5-Clustering/1-Visualize/README.md)       |      Jen       |
+|      15       |          Nijerya'nın Müzik Zevklerini Keşfetme :headphones:           |         [Kümeleme](../5-Clustering/README.md)          | K merkezli kümeleme yöntemini keşfetmek                                                                                         |       [ders](../5-Clustering/2-K-Means/README.md)        |      Jen       |
+|      16       |                 Doğal Dil İşlemeye Giriş  :coffee:                |        [Doğal Dil İşleme](../6-NLP/README.md)          | Basit bir bot yaratarak NLP temellerini öğrenmek                                                                                |     [ders](../6-NLP/1-Introduction-to-NLP/README.md)     |    Stephen     |
+|      17       |                   Yaygın NLP Görevleri :coffee:                   |        [Doğal Dil İşleme](../6-NLP/README.md)          | Dil yapılarıyla uğraşırken gereken yaygın görevleri anlayarak NLP bilginizi derinleştirmek                                      |            [ders](../6-NLP/2-Tasks/README.md)            |    Stephen     |
+|      18       |                  Çeviri ve Duygu Analizi :hearts:                 |        [Doğal Dil İşleme](../6-NLP/README.md)          | Jane Austen ile çeviri ve duygu analizi                                                                                         |    [ders](../6-NLP/3-Translation-Sentiment/README.md)    |    Stephen     |
+|      19       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](../6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi, 1                                                                                      |       [ders](../6-NLP/4-Hotel-Reviews-1/README.md)       |    Stephen     |
+|      20       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](../6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi 2                                                                                       |       [ders](../6-NLP/5-Hotel-Reviews-2/README.md)       |    Stephen     |
+|      21       |               Zaman Serisi Tahminine Giriş                 |        [Zaman Serisi](../7-TimeSeries/README.md)       | Zaman serisi tahminine giriş                                                                                                    |     [ders](../7-TimeSeries/1-Introduction/README.md)     |   Francesca    |
+|      22       | :zap: Dünya Güç Kullanımı :zap: - ARIMA ile Zaman Serisi Tahmini |        [Zaman Serisi](../7-TimeSeries/README.md)       | ARIMA ile zaman serisi tahmini                                                                                                  |        [ders](../7-TimeSeries/2-ARIMA/README.md)         |   Francesca    |
+|      23       |                Pekiştirmeli Öğrenmeye Giriş                |  [Pekiştirmeli Öğrenme](../8-Reinforcement/README.md)  | Q-Learning ile pekiştirmeli öğrenmeye giriş                                                                                     |     [ders](../8-Reinforcement/1-QLearning/README.md)     |     Dmitry     |
+|      24       |       Peter'ın Kurttan Uzak Durmasına Yardım Edin! :wolf:       |  [Pekiştirmeli Öğrenme](../8-Reinforcement/README.md)  | Pekiştirmeli öğrenme spor salonu                                                                                                |        [ders](../8-Reinforcement/2-Gym/README.md)        |     Dmitry     |
+|   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](../9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](../9-Real-World/1-Applications/README.md)     |     Takım      |
+## Çevrimdışı erişim
+
+Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu yazılım havuzunu çatallayın, yerel makinenizde [Docsify'ı kurun](https://docsify.js.org/#/quickstart) ve sonra bu yazılım havuzunun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
+
+## PDF'ler
+
+Eğitim programının bağlantılarla PDF'sine [buradan](../pdf/readme.pdf) ulaşabilirsiniz.
+
+## Yardım İsteniyor!
+
+Bir çeviri katkısında bulunmak ister misiniz? Lütfen [çeviri kılavuz ilkelerimizi](../TRANSLATIONS.md) okuyun ve [buraya](https://github.com/microsoft/ML-For-Beginners/issues/71) girdiyi ekleyin.
+
+## Diğer Eğitim Programları
+
+Takımımız başka eğitim programları üretiyor! İnceleyin:
+
+- [Yeni Başlayanlar için Web Geliştirme](https://aka.ms/webdev-beginners)
+- [Yeni Başlayanlar için Nesnelerin İnterneti](https://aka.ms/iot-beginners)
+
diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md
index f46a50d6c..e1735cc1e 100644
--- a/translations/README.zh-cn.md
+++ b/translations/README.zh-cn.md
@@ -29,11 +29,11 @@
 
 - 从课前测验开始
 - 阅读课程内容，完成所有的活动，在每次 knowledge check 时暂停并思考
-- 我们建议你基于理解来创建项目（而不是仅仅跑一遍示例代码）示例代码的位置在每一个项目的 `/solution` 文件夹中。
+- 我们建议你基于理解来创建项目（而不是仅仅跑一遍示例代码）。示例代码的位置在每一个项目的 `/solution` 文件夹中。
 - 进行课后测验
 - 完成课程挑战
 - 完成作业
-- 一节课完成后, 访问[讨论版](https://github.com/microsoft/ML-For-Beginners/discussions)，通过天蝎相应的 PAT Rubric (课程目标)来深化自己的学习成果。你也可以回应其它的 PAT，这样我们可以一起学习。
+- 一节课完成后, 访问[讨论版](https://github.com/microsoft/ML-For-Beginners/discussions)，通过填写相应的 PAT Rubric (课程目标) 来深化自己的学习成果。你也可以回应其它的 PAT，这样我们可以一起学习。
 
 > 如果希望进一步学习，我们推荐跟随 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 的模块和学习路径。
 
@@ -52,7 +52,7 @@
 
 此课程基于两个教学原则：学生应该上手进行**项目实践**，并完成**频繁的测验**。 此外，为了使整个课程更具有整体性，课程们有一个共同的**主题**。
 
-通过确保课程内容与项目强相关，我们让学习过程对学生更具吸引力，概念的学习也被深化了。难度较低的课前测验可以吸引学生学习课程，课后的第二次测验进一步重复了课堂中的概念。该课程被设计地灵活有趣，可以一次性全部学习，或者分开来一部分一部分学习。这些项目由浅入深，从第一周的的小项目开始，在第十二周的周期结束时变得较为复杂。本课程还包括一个关于机器学习实际应用的后记，可用作额外学分或讨论的基础。
+通过确保课程内容与项目强相关，我们让学习过程对学生更具吸引力，概念的学习也被深化了。难度较低的课前测验可以吸引学生学习课程，而课后的第二次测验也进一步重复了课堂中的概念。该课程被设计地灵活有趣，可以一次性全部学习，或者分开来一部分一部分学习。这些项目由浅入深，从第一周的小项目开始，在第十二周结束时变得较为复杂。本课程还包括一个关于机器学习实际应用的后记，可用作额外学分或进一步讨论的基础。
 
 > 在这里，你可以找到我们的[行为守则](../CODE_OF_CONDUCT.md)，[对项目作出贡献](../CONTRIBUTING.md)以及[翻译](../TRANSLATIONS.md)指南。我们欢迎各位提出有建设性的反馈！
 
@@ -69,36 +69,36 @@
 - 作业
 - 课后测验
 
-> **关于测验**：所有的测验都在[这个应用里](https://jolly-sea-0a877260f.azurestaticapps.net)，总共 50 个测验，每个测验三个问题。它们的链接在每节课中，而且这个测验应用可以在本地运行。请参考 `quiz-app` 文件夹中的指南。
-
-
-| 课程编号 |                           主体                            |                       课程组                       | 学习目标                                                                                                             |                     课程链接                     |     作者     |
-| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              机器学习简介              |      [简介](../1-Introduction/README.md)       | 了解机器学习背后的基本概念                                                                                |   [课程](../1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              机器学习的历史               |      [简介](../1-Introduction/README.md)       | 了解该领域的历史                                                                                         |  [课程](../1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
-|      03       |               机器学习与公平                |      [简介](../1-Introduction/README.md)       | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的重要哲学问题？ |     [课程](../1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              机器学习的技术工具               |      [简介](../1-Introduction/README.md)       | 机器学习研究者使用哪些技术来构建机器学习模型？                                                                       | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
-|      05       |                 回归简介                 |        [回归](../2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                                                                  |       [课程](../2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              北美南瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 可视化、进行数据清理，为机器学习做准备                                                                                  |        [课程](../2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              北美南瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 建立线性和多项式回归模型                                                                                   |       [课程](../2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              北美南瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 构建逻辑回归模型                                                                                               |      [课程](../2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        一个网页应用 🔌                         |           [网页应用](../3-Web-App/README.md)            | 构建一个 Web 应用程序以使用经过训练的模型                                                                                       |        [课程](../3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               分类简介               |    [分类](../4-Classification/README.md)     | 清理、准备和可视化数据； 分类简介                                                            |  [课程](../4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
-|      11       |          美味的亚洲和印度美食 🍜            |    [分类](../4-Classification/README.md)     | 分类器简介                                                                                                     | [课程](../4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
-|      12       |          美味的亚洲和印度美食 🍜            |    [分类](../4-Classification/README.md)     | 关于分类器的更多内容                                                                                                                | [课程](../4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
-|      13       |          美味的亚洲和印度美食 🍜            |    [分类](../4-Classification/README.md)     | 使用您的模型构建一个可以「推荐」的 Web 应用                                                                                    |    [课程](../4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 聚类简介                 |        [聚类](../5-Clustering/README.md)         | 清理、准备和可视化数据； 聚类简介                                                                |     [课程](../5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            探索尼日利亚人的音乐品味 🎧             |        [聚类](../5-Clustering/README.md)         | 探索 K-Means 聚类方法                                                                                           |      [课程](../5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       自然语言处理 (NLP) 简介  ☕️       |   [自然语言处理](../6-NLP/README.md)    | 通过构建一个简单的 bot (机器人) 来了解 NLP 的基础知识                                                                             |    [课程](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     常见的 NLP 任务 ☕️                     |   [自然语言处理](../6-NLP/README.md)    | 通过理解处理语言结构时所需的常见任务来加深对于自然语言处理 (NLP) 的理解                          |           [课程](../6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            翻译和情感分析 ♥️            |   [自然语言处理](../6-NLP/README.md)    | 对简·奥斯汀的文本进行翻译和情感分析                                                                             |   [课程](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                欧洲的浪漫酒店 ♥️                 |   [自然语言处理](../6-NLP/README.md)    | 对于酒店评价进行情感分析（上）                                                                                        |      [课程](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                欧洲的浪漫酒店 ♥️                 |   [自然语言处理](../6-NLP/README.md)    | 对于酒店评价进行情感分析（下）                                                                                         |      [课程](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          时间序列预测简介           |        [时间序列](../7-TimeSeries/README.md)        | 时间序列预测简介 forecasting                                                                                         |    [课程](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡️ 世界用电量 ⚡️ - 使用 ARIMA 进行时间序列预测 |        [时间序列](../7-TimeSeries/README.md)        | 使用 ARIMA 进行时间序列预测                                                                                              |       [课程](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           强化学习简介           | [强化学习](../8-Reinforcement/README.md) | Q-Learning 强化学习简介                                                                          |    [课程](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                帮助 Peter 避开狼！🐺                | [强化学习](../8-Reinforcement/README.md) | 强化学习练习                                                                                                      |       [课程](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  后记   |          现实世界中的机器学习场景和应用          |      [自然场景下的机器学习](../9-Real-World/README.md)       | 探索有趣的经典机器学习方法，了解现实世界中机器学习的应用                                                               |    [课程](../9-Real-World/1-Applications/README.md)    |      团队      |
+> **关于测验**：所有的测验都在[这个应用里](https://white-water-09ec41f0f.azurestaticapps.net/)，总共 50 个测验，每个测验三个问题。它们的链接在每节课中，而且这个测验应用可以在本地运行。请参考 `quiz-app` 文件夹中的指南。
+
+
+| 课程编号 |                     主体                     |                      课程组                       | 学习目标                                                                |                        课程链接                        |     作者      |
+| :------: | :------------------------------------------: | :-----------------------------------------------: | ----------------------------------------------------------------------- | :----------------------------------------------------: | :-----------: |
+|    01    |                 机器学习简介                 |        [简介](../1-Introduction/README.md)        | 了解机器学习背后的基本概念                                              |   [课程](../1-Introduction/1-intro-to-ML/README.md)    |   Muhammad    |
+|    02    |                机器学习的历史                |        [简介](../1-Introduction/README.md)        | 了解该领域的历史                                                        |  [课程](../1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
+|    03    |                机器学习与公平                |        [简介](../1-Introduction/README.md)        | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的重要哲学问题？    |     [课程](../1-Introduction/3-fairness/README.md)     |    Tomomi     |
+|    04    |              机器学习的技术工具              |        [简介](../1-Introduction/README.md)        | 机器学习研究者使用哪些技术来构建机器学习模型？                          | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
+|    05    |                   回归简介                   |         [回归](../2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                            |       [课程](../2-Regression/1-Tools/README.md)        |      Jen      |
+|    06    |                北美南瓜价格 🎃                |         [回归](../2-Regression/README.md)         | 可视化、进行数据清理，为机器学习做准备                                  |        [课程](../2-Regression/2-Data/README.md)        |      Jen      |
+|    07    |                北美南瓜价格 🎃                |         [回归](../2-Regression/README.md)         | 建立线性和多项式回归模型                                                |       [课程](../2-Regression/3-Linear/README.md)       |      Jen      |
+|    08    |                北美南瓜价格 🎃                |         [回归](../2-Regression/README.md)         | 构建逻辑回归模型                                                        |      [课程](../2-Regression/4-Logistic/README.md)      |      Jen      |
+|    09    |                一个网页应用 🔌                |        [网页应用](../3-Web-App/README.md)         | 构建一个 Web 应用程序以使用经过训练的模型                               |        [课程](../3-Web-App/1-Web-App/README.md)        |      Jen      |
+|    10    |                   分类简介                   |       [分类](../4-Classification/README.md)       | 清理、准备和可视化数据； 分类简介                                       |  [课程](../4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
+|    11    |            美味的亚洲和印度美食 🍜            |       [分类](../4-Classification/README.md)       | 分类器简介                                                              | [课程](../4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
+|    12    |            美味的亚洲和印度美食 🍜            |       [分类](../4-Classification/README.md)       | 关于分类器的更多内容                                                    | [课程](../4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
+|    13    |            美味的亚洲和印度美食 🍜            |       [分类](../4-Classification/README.md)       | 使用您的模型构建一个可以「推荐」的 Web 应用                             |    [课程](../4-Classification/4-Applied/README.md)     |      Jen      |
+|    14    |                   聚类简介                   |         [聚类](../5-Clustering/README.md)         | 清理、准备和可视化数据； 聚类简介                                       |     [课程](../5-Clustering/1-Visualize/README.md)      |      Jen      |
+|    15    |          探索尼日利亚人的音乐品味 🎧          |         [聚类](../5-Clustering/README.md)         | 探索 K-Means 聚类方法                                                   |      [课程](../5-Clustering/2-K-Means/README.md)       |      Jen      |
+|    16    |          自然语言处理 (NLP) 简介  ☕️          |        [自然语言处理](../6-NLP/README.md)         | 通过构建一个简单的 bot (机器人) 来了解 NLP 的基础知识                   |    [课程](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen    |
+|    17    |              常见的 NLP 任务 ☕️               |        [自然语言处理](../6-NLP/README.md)         | 通过理解处理语言结构时所需的常见任务来加深对于自然语言处理 (NLP) 的理解 |           [课程](../6-NLP/2-Tasks/README.md)           |    Stephen    |
+|    18    |               翻译和情感分析 ♥️               |        [自然语言处理](../6-NLP/README.md)         | 对简·奥斯汀的文本进行翻译和情感分析                                     |   [课程](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen    |
+|    19    |               欧洲的浪漫酒店 ♥️               |        [自然语言处理](../6-NLP/README.md)         | 对于酒店评价进行情感分析（上）                                          |      [课程](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen    |
+|    20    |               欧洲的浪漫酒店 ♥️               |        [自然语言处理](../6-NLP/README.md)         | 对于酒店评价进行情感分析（下）                                          |      [课程](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen    |
+|    21    |               时间序列预测简介               |       [时间序列](../7-TimeSeries/README.md)       | 时间序列预测简介 forecasting                                            |    [课程](../7-TimeSeries/1-Introduction/README.md)    |   Francesca   |
+|    22    | ⚡️ 世界用电量 ⚡️ - 使用 ARIMA 进行时间序列预测 |       [时间序列](../7-TimeSeries/README.md)       | 使用 ARIMA 进行时间序列预测                                             |       [课程](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca   |
+|    23    |                 强化学习简介                 |     [强化学习](../8-Reinforcement/README.md)      | Q-Learning 强化学习简介                                                 |    [课程](../8-Reinforcement/1-QLearning/README.md)    |    Dmitry     |
+|    24    |             帮助 Peter 避开狼！🐺             |     [强化学习](../8-Reinforcement/README.md)      | 强化学习练习                                                            |       [课程](../8-Reinforcement/2-Gym/README.md)       |    Dmitry     |
+|   后记   |        现实世界中的机器学习场景和应用        | [自然场景下的机器学习](../9-Real-World/README.md) | 探索有趣的经典机器学习方法，了解现实世界中机器学习的应用                |    [课程](../9-Real-World/1-Applications/README.md)    |     团队      |
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