chore(i18n): sync translations with latest source changes (chunk 1/1, 391 changes)

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parent 87f77f3e67
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@ -13,7 +13,7 @@
#### Supported via GitHub Action (Automated & Always Up-to-Date)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh/README.md) | [Chinese (Traditional, Hong Kong)](./translations/hk/README.md) | [Chinese (Traditional, Macau)](./translations/mo/README.md) | [Chinese (Traditional, Taiwan)](./translations/tw/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/br/README.md) | [Portuguese (Portugal)](./translations/pt/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md)
[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](./translations/zh-HK/README.md) | [Chinese (Traditional, Macau)](./translations/zh-MO/README.md) | [Chinese (Traditional, Taiwan)](./translations/zh-TW/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/pt-BR/README.md) | [Portuguese (Portugal)](./translations/pt-PT/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md)
> **Prefer to Clone Locally?**

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@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
Nesta seção do currículo, você será apresentado aos conceitos básicos que fundamentam o campo do aprendizado de máquina, o que ele é, e aprenderá sobre sua história e as técnicas que os pesquisadores utilizam para trabalhar com ele. Vamos explorar juntos este novo mundo do aprendizado de máquina!
![globo](../../../translated_images/br/globe.59f26379ceb40428.webp)
![globo](../../../translated_images/pt-BR/globe.59f26379ceb40428.webp)
> Foto por <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> no <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
### Aulas

@ -48,7 +48,7 @@
" width=\"630\"/>\n",
" <figcaption>Arte por @allison_horst</figcaption>\n",
"\n",
"<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/encouRage.e75d5fe0367fb913.webp)<br>Arte por @allison_horst-->\n"
"<!--![Arte por \\@allison_horst](../../../../../../translated_images/pt-BR/encouRage.e75d5fe0367fb913.webp)<br>Arte por @allison_horst-->\n"
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@ -227,7 +227,7 @@
" <figcaption>Arte por @allison_horst</figcaption>\n",
"\n",
"\n",
"<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>Arte por \\@allison_horst-->\n"
"<!--![Arte por \\@allison_horst](../../../../../../translated_images/pt-BR/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>Arte por \\@allison_horst-->\n"
],
"metadata": {
"id": "o4jLY5-VZO2C"
@ -531,7 +531,7 @@
" <figcaption>Infográfico por Dasani Madipalli</figcaption>\n",
"\n",
"\n",
"<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
"<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt-BR/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
"\n",
"Existe um *sábio* ditado que diz o seguinte:\n",
"\n",

@ -130,7 +130,7 @@
">\n",
"> Em outras palavras, e referindo-se à pergunta original dos dados das abóboras: \"prever o preço de uma abóbora por alqueire por mês\", `X` se referiria ao preço e `Y` ao mês de venda.\n",
">\n",
"> ![](../../../../../../translated_images/br/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n",
"> ![](../../../../../../translated_images/pt-BR/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n",
" Infográfico por Jen Looper\n",
"> \n",
"> Calcule o valor de Y. Se você está pagando cerca de \\$4, deve ser abril!\n",
@ -162,7 +162,7 @@
" <figcaption>Arte por @allison_horst</figcaption>\n",
"\n",
"\n",
"<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/janitor.e4a77dd3d3e6a32e.webp){width=\"700\"}-->\n"
"<!--![Arte por \\@allison_horst](../../../../../../translated_images/pt-BR/janitor.e4a77dd3d3e6a32e.webp){width=\"700\"}-->\n"
],
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"id": "WdUKXk7Bs8-V"
@ -567,7 +567,7 @@
" <figcaption>Infográfico por Dasani Madipalli</figcaption>\n",
"\n",
"\n",
"<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
"<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt-BR/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
],
"metadata": {
"id": "YqXjLuWavNxW"
@ -808,7 +808,7 @@
" <figcaption>Infográfico por Dasani Madipalli</figcaption>\n",
"\n",
"\n",
"<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
"<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt-BR/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
],
"metadata": {
"id": "HOCqJXLTwtWI"

@ -6,7 +6,7 @@
"source": [
"## Construir um modelo de regressão logística - Aula 4\n",
"\n",
"![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/br/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
"![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/pt-BR/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
"\n",
"#### **[Questionário pré-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
"\n",
@ -78,7 +78,7 @@
"\n",
"A regressão logística não oferece os mesmos recursos que a regressão linear. A primeira fornece uma previsão sobre uma `categoria binária` (\"laranja ou não laranja\"), enquanto a segunda é capaz de prever `valores contínuos`, por exemplo, dado a origem de uma abóbora e o momento da colheita, *quanto o preço dela vai aumentar*.\n",
"\n",
"![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/pumpkin-classifier.562771f104ad5436.webp)\n",
"![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt-BR/pumpkin-classifier.562771f104ad5436.webp)\n",
"\n",
"### Outras classificações\n",
"\n",
@ -88,7 +88,7 @@
"\n",
"- **Ordinal**, que envolve categorias ordenadas, útil se quisermos organizar nossos resultados de forma lógica, como nossas abóboras ordenadas por um número finito de tamanhos (mini,pequeno,médio,grande,xl,xxl).\n",
"\n",
"![Regressão multinomial vs ordinal](../../../../../../translated_images/br/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
"![Regressão multinomial vs ordinal](../../../../../../translated_images/pt-BR/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
"\n",
"#### **As variáveis NÃO precisam ser correlacionadas**\n",
"\n",

@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
Na América do Norte, as abóboras são frequentemente esculpidas em rostos assustadores para o Halloween. Vamos descobrir mais sobre esses vegetais fascinantes!
![jack-o-lanterns](../../../translated_images/br/jack-o-lanterns.181c661a9212457d.webp)
![jack-o-lanterns](../../../translated_images/pt-BR/jack-o-lanterns.181c661a9212457d.webp)
> Foto de <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> no <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
## O que você vai aprender

@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
Nesta seção do currículo, você será introduzido a um tópico aplicado de aprendizado de máquina: como salvar seu modelo Scikit-learn como um arquivo que pode ser usado para fazer previsões dentro de um aplicativo web. Depois que o modelo estiver salvo, você aprenderá como utilizá-lo em um aplicativo web construído com Flask. Primeiro, você criará um modelo usando alguns dados relacionados a avistamentos de OVNIs! Em seguida, você construirá um aplicativo web que permitirá inserir um número de segundos junto com valores de latitude e longitude para prever qual país relatou ter visto um OVNI.
![Estacionamento de OVNIs](../../../translated_images/br/ufo.9e787f5161da9d4d.webp)
![Estacionamento de OVNIs](../../../translated_images/pt-BR/ufo.9e787f5161da9d4d.webp)
Foto por <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> no <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>

@ -44,7 +44,7 @@
" <figcaption>Comemore as culinárias pan-asiáticas nestas lições! Imagem de Jen Looper</figcaption>\n",
"\n",
"\n",
"<!--![Comemore as culinárias pan-asiáticas nestas lições! Imagem de Jen Looper](../../../../../../translated_images/br/pinch.b33c0ba76f284aad94a3c4e3ed83e13ed1e17fbcf4db8ca8583c3a0c135e2e99.png)-->\n",
"<!--![Comemore as culinárias pan-asiáticas nestas lições! Imagem de Jen Looper](../../../../../../translated_images/pt-BR/pinch.b33c0ba76f284aad94a3c4e3ed83e13ed1e17fbcf4db8ca8583c3a0c135e2e99.png)-->\n",
"\n",
"Classificação é uma forma de [aprendizado supervisionado](https://wikipedia.org/wiki/Supervised_learning) que tem muito em comum com técnicas de regressão. Na classificação, você treina um modelo para prever a qual `categoria` um item pertence. Se o aprendizado de máquina é sobre prever valores ou nomes para coisas usando conjuntos de dados, então a classificação geralmente se divide em dois grupos: *classificação binária* e *classificação multiclasses*.\n",
"\n",

@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
Na Ásia e na Índia, as tradições culinárias são extremamente diversas e muito deliciosas! Vamos analisar dados sobre culinárias regionais para tentar entender seus ingredientes.
![Vendedor de comida tailandesa](../../../translated_images/br/thai-food.c47a7a7f9f05c218.webp)
![Vendedor de comida tailandesa](../../../translated_images/pt-BR/thai-food.c47a7a7f9f05c218.webp)
> Foto por <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> no <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
## O que você vai aprender

@ -15,7 +15,7 @@ Clustering é uma tarefa de aprendizado de máquina que busca encontrar objetos
O público diversificado da Nigéria tem gostos musicais igualmente variados. Usando dados extraídos do Spotify (inspirado por [este artigo](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)), vamos analisar algumas músicas populares na Nigéria. Este conjunto de dados inclui informações sobre a pontuação de 'dançabilidade', 'acousticness', volume, 'speechiness', popularidade e energia de várias músicas. Será interessante descobrir padrões nesses dados!
![Um toca-discos](../../../translated_images/br/turntable.f2b86b13c53302dc.webp)
![Um toca-discos](../../../translated_images/pt-BR/turntable.f2b86b13c53302dc.webp)
> Foto por <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> no <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>

@ -17,7 +17,7 @@ Nesta seção do currículo, você será introduzido a um dos usos mais difundid
Nestas lições, aprenderemos os fundamentos do PLN construindo pequenos bots conversacionais para entender como o aprendizado de máquina ajuda a tornar essas conversas cada vez mais 'inteligentes'. Você viajará no tempo, conversando com Elizabeth Bennett e Mr. Darcy do clássico romance de Jane Austen, **Orgulho e Preconceito**, publicado em 1813. Depois, você aprofundará seu conhecimento aprendendo sobre análise de sentimentos por meio de avaliações de hotéis na Europa.
![Livro Orgulho e Preconceito e chá](../../../translated_images/br/p&p.279f1c49ecd88941.webp)
![Livro Orgulho e Preconceito e chá](../../../translated_images/pt-BR/p&p.279f1c49ecd88941.webp)
> Foto por <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> no <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
## Lições

@ -17,7 +17,7 @@ Nestes dois módulos, você será introduzido à previsão de séries temporais,
Nosso foco regional é o uso de eletricidade no mundo, um conjunto de dados interessante para aprender a prever o consumo futuro de energia com base em padrões de carga anteriores. Você verá como esse tipo de previsão pode ser extremamente útil em um ambiente empresarial.
![rede elétrica](../../../translated_images/br/electric-grid.0c21d5214db09ffa.webp)
![rede elétrica](../../../translated_images/pt-BR/electric-grid.0c21d5214db09ffa.webp)
Foto de [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) de torres elétricas em uma estrada em Rajasthan no [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)

@ -13,7 +13,7 @@ O aprendizado por reforço, RL, é considerado um dos paradigmas básicos de apr
Imagine que você tem um ambiente simulado, como o mercado de ações. O que acontece se você impuser uma determinada regulamentação? Isso terá um efeito positivo ou negativo? Se algo negativo acontecer, você precisa aceitar esse _reforço negativo_, aprender com ele e mudar de direção. Se o resultado for positivo, você precisa construir sobre esse _reforço positivo_.
![Pedro e o lobo](../../../translated_images/br/peter.779730f9ba3a8a8d.webp)
![Pedro e o lobo](../../../translated_images/pt-BR/peter.779730f9ba3a8a8d.webp)
> Pedro e seus amigos precisam escapar do lobo faminto! Imagem por [Jen Looper](https://twitter.com/jenlooper)

@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
Nesta seção do currículo, você será apresentado a algumas aplicações reais do aprendizado de máquina clássico. Pesquisamos na internet para encontrar artigos e publicações sobre aplicações que utilizam essas estratégias, evitando ao máximo redes neurais, aprendizado profundo e IA. Descubra como o aprendizado de máquina é usado em sistemas empresariais, aplicações ecológicas, finanças, artes e cultura, entre outros.
![chess](../../../translated_images/br/chess.e704a268781bdad8.webp)
![chess](../../../translated_images/pt-BR/chess.e704a268781bdad8.webp)
> Foto por <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> no <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>

@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA:
Estamos realizando uma série de aprendizado com IA no Discord, saiba mais e junte-se a nós em [Learn with AI Series](https://aka.ms/learnwithai/discord) de 18 a 30 de setembro de 2025. Você receberá dicas e truques para usar o GitHub Copilot para Ciência de Dados.
![Learn with AI series](../../../../translated_images/br/3.9b58fd8d6c373c20.webp)
![Learn with AI series](../../../../translated_images/pt-BR/3.9b58fd8d6c373c20.webp)
# Machine Learning para Iniciantes - Um Currículo
@ -90,7 +90,7 @@ Siga estes passos:
Algumas das lições estão disponíveis em vídeos curtos. Você pode encontrar todos eles incorporados nas lições, ou na [playlist ML for Beginners no canal Microsoft Developer do YouTube](https://aka.ms/ml-beginners-videos) clicando na imagem abaixo.
[![ML for beginners banner](../../../../translated_images/br/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
[![ML for beginners banner](../../../../translated_images/pt-BR/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
---

@ -0,0 +1,596 @@
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# Introduction to Machine Learning
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)

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# Get Up and Running
## Instructions

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# History of Machine Learning
![Summary of History of Machine Learning in a sketchnote](../../../../sketchnotes/ml-history.png)

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# Create a timeline
## Instructions

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# Building Machine Learning solutions with responsible AI
![Summary of responsible AI in Machine Learning in a sketchnote](../../../../sketchnotes/ml-fairness.png)

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# Explore the Responsible AI Toolbox
## Instructions

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# Techniques of Machine Learning
The process of creating, using, and maintaining machine learning models and the data they rely on is quite different from many other development workflows. In this lesson, we will break down the process and outline the key techniques you need to understand. You will:

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# Interview a data scientist
## Instructions

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# Introduction to machine learning
In this section of the curriculum, you will be introduced to the fundamental concepts behind the field of machine learning, what it entails, and learn about its history and the techniques researchers use to work with it. Let's dive into this exciting world of ML together!

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# Get started with Python and Scikit-learn for regression models
![Summary of regressions in a sketchnote](../../../../sketchnotes/ml-regression.png)

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# Regression with Scikit-learn
## Instructions

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---

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# Build a regression model using Scikit-learn: prepare and visualize data
![Data visualization infographic](../../../../2-Regression/2-Data/images/data-visualization.png)

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# Exploring Visualizations
There are several different libraries available for data visualization. Use the Pumpkin data from this lesson to create some visualizations with matplotlib and seaborn in a sample notebook. Which libraries are easier to use?

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---

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# Build a regression model using Scikit-learn: regression four ways
![Linear vs polynomial regression infographic](../../../../2-Regression/3-Linear/images/linear-polynomial.png)
@ -115,11 +106,11 @@ Now that you understand the math behind linear regression, let's create a regres
From the previous lesson, you've likely observed that the average price for different months looks like this:
<img alt="Average price by month" src="../2-Data/images/barchart.png" width="50%"/>
<img alt="Average price by month" src="../../../../translated_images/en/barchart.a833ea9194346d76.webp" width="50%"/>
This suggests there might be some correlation, and we can attempt to train a linear regression model to predict the relationship between `Month` and `Price`, or between `DayOfYear` and `Price`. Here's the scatterplot showing the latter relationship:
<img alt="Scatter plot of Price vs. Day of Year" src="images/scatter-dayofyear.png" width="50%" />
<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/en/scatter-dayofyear.bc171c189c9fd553.webp" width="50%" />
Let's check for correlation using the `corr` function:
@ -138,7 +129,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()):
ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)
```
<img alt="Scatter plot of Price vs. Day of Year" src="images/scatter-dayofyear-color.png" width="50%" />
<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/en/scatter-dayofyear-color.65790faefbb9d54f.webp" width="50%" />
Our investigation suggests that variety has a greater impact on price than the actual selling date. This can be visualized with a bar graph:
@ -146,7 +137,7 @@ Our investigation suggests that variety has a greater impact on price than the a
new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')
```
<img alt="Bar graph of price vs variety" src="images/price-by-variety.png" width="50%" />
<img alt="Bar graph of price vs variety" src="../../../../translated_images/en/price-by-variety.744a2f9925d9bcb4.webp" width="50%" />
Let's focus on one pumpkin variety, the 'pie type,' and examine the effect of the date on price:
@ -154,7 +145,7 @@ Let's focus on one pumpkin variety, the 'pie type,' and examine the effect of th
pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']
pie_pumpkins.plot.scatter('DayOfYear','Price')
```
<img alt="Scatter plot of Price vs. Day of Year" src="images/pie-pumpkins-scatter.png" width="50%" />
<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/en/pie-pumpkins-scatter.d14f9804a53f927e.webp" width="50%" />
If we calculate the correlation between `Price` and `DayOfYear` using the `corr` function, we get approximately `-0.27`, indicating that training a predictive model is worthwhile.
@ -228,7 +219,7 @@ plt.scatter(X_test,y_test)
plt.plot(X_test,pred)
```
<img alt="Linear regression" src="images/linear-results.png" width="50%" />
<img alt="Linear regression" src="../../../../translated_images/en/linear-results.f7c3552c85b0ed1c.webp" width="50%" />
## Polynomial Regression
@ -257,7 +248,7 @@ Using `PolynomialFeatures(2)` means we will include all second-degree polynomial
Pipelines can be used in the same way as the original `LinearRegression` object, meaning we can `fit` the pipeline and then use `predict` to get prediction results. Below is the graph showing test data and the approximation curve:
<img alt="Polynomial regression" src="images/poly-results.png" width="50%" />
<img alt="Polynomial regression" src="../../../../translated_images/en/poly-results.ee587348f0f1f60b.webp" width="50%" />
Using Polynomial Regression, we can achieve slightly lower MSE and higher determination, but not significantly. We need to consider other features!
@ -275,7 +266,7 @@ Ideally, we want to predict prices for different pumpkin varieties using the sam
Heres how the average price depends on variety:
<img alt="Average price by variety" src="images/price-by-variety.png" width="50%" />
<img alt="Average price by variety" src="../../../../translated_images/en/price-by-variety.744a2f9925d9bcb4.webp" width="50%" />
To include variety in our model, we first need to convert it to numeric form, or **encode** it. There are several ways to do this:

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# Create a Regression Model
## Instructions

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---

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# Logistic regression to predict categories
![Logistic vs. linear regression infographic](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)

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# Retrying some Regression
## Instructions

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---

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# Regression models for machine learning
## Regional topic: Regression models for pumpkin prices in North America 🎃

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# Build a Web App to use a ML Model
In this lesson, you will train a machine learning model using a fascinating dataset: _UFO sightings over the past century_, sourced from NUFORC's database.

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# Try a different model
## Instructions

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# Build a web app to use your ML model
In this part of the curriculum, you'll explore a practical application of machine learning: how to save your Scikit-learn model as a file that can be used to make predictions in a web application. Once the model is saved, you'll learn how to integrate it into a web app built with Flask. You'll start by creating a model using data about UFO sightings! Then, you'll develop a web app that allows users to input a number of seconds along with latitude and longitude values to predict which country reported the UFO sighting.

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# Introduction to classification
In these four lessons, you will dive into one of the core areas of traditional machine learning: _classification_. We'll explore various classification algorithms using a dataset about the diverse and delicious cuisines of Asia and India. Get ready to whet your appetite!

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# Explore classification methods
## Instructions

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---

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# Cuisine classifiers 1
In this lesson, you will use the dataset you saved from the previous lesson, which contains balanced and clean data about cuisines.

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# Study the solvers
## Instructions

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---

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# Cuisine classifiers 2
In this second classification lesson, you will explore additional methods for classifying numeric data. You will also learn about the implications of choosing one classifier over another.

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# Parameter Play
## Instructions

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---

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# Build a Cuisine Recommender Web App
In this lesson, you will create a classification model using techniques learned in previous lessons and the delicious cuisine dataset used throughout this series. Additionally, you will develop a small web app to utilize a saved model, leveraging Onnx's web runtime.

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# Build a recommender
## Instructions

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# Getting started with classification
## Regional topic: Delicious Asian and Indian Cuisines 🍜

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# Introduction to clustering
Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that assumes a dataset is unlabelled or that its inputs are not paired with predefined outputs. It uses various algorithms to analyze unlabeled data and group it based on patterns identified within the data.

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# Research other visualizations for clustering
## Instructions

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---

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# K-Means Clustering
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)

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# Try different clustering methods
## Instructions

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---

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# Clustering models for machine learning
Clustering is a machine learning task that aims to identify objects that are similar to each other and group them into clusters. What sets clustering apart from other machine learning approaches is that it happens automatically. In fact, its fair to say its the opposite of supervised learning.

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# Introduction to natural language processing
This lesson provides a brief history and key concepts of *natural language processing*, a subfield of *computational linguistics*.

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# Search for a bot
## Instructions

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# Common natural language processing tasks and techniques
For most *natural language processing* tasks, the text to be processed must be broken down, analyzed, and the results stored or cross-referenced with rules and datasets. These tasks allow the programmer to derive the _meaning_, _intent_, or simply the _frequency_ of terms and words in a text.

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# Make a Bot talk back
## Instructions

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# Translation and sentiment analysis with ML
In the previous lessons, you learned how to create a basic bot using `TextBlob`, a library that incorporates machine learning behind the scenes to perform basic NLP tasks like extracting noun phrases. Another significant challenge in computational linguistics is accurately _translating_ a sentence from one spoken or written language to another.

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# Poetic license
## Instructions

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# Sentiment analysis with hotel reviews - processing the data
In this section, you'll apply techniques from previous lessons to perform exploratory data analysis on a large dataset. Once you understand the relevance of the various columns, you'll learn:

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# NLTK
## Instructions

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# Sentiment analysis with hotel reviews
Now that you've explored the dataset in detail, it's time to filter the columns and apply NLP techniques to gain new insights about the hotels.

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# Try a different dataset
## Instructions

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# Getting started with natural language processing
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written—commonly referred to as natural language. It is a branch of artificial intelligence (AI). NLP has been around for over 50 years and has its origins in the field of linguistics. The entire field focuses on enabling machines to comprehend and process human language. This capability can then be applied to tasks such as spell checking or machine translation. NLP has numerous practical applications across various domains, including medical research, search engines, and business intelligence.

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Download the hotel review data to this folder.
---

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# Introduction to time series forecasting
![Summary of time series in a sketchnote](../../../../sketchnotes/ml-timeseries.png)

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# Visualize some more Time Series
## Instructions

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# Time series forecasting with ARIMA
In the previous lesson, you explored time series forecasting and worked with a dataset showing variations in electrical load over time.

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# A new ARIMA model
## Instructions

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# Time Series Forecasting with Support Vector Regressor
In the previous lesson, you learned how to use the ARIMA model for time series predictions. Now, you'll explore the Support Vector Regressor model, which is used to predict continuous data.

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# A new SVR model
## Instructions [^1]

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# Introduction to time series forecasting
What is time series forecasting? It's the process of predicting future events by analyzing past trends.

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## Visualizing the Learned Policy
After running the learning algorithm, we can visualize the Q-Table to see the learned policy. The arrows (or circles) in each cell will indicate the preferred direction of movement based on the Q-Table values. This visualization helps us understand how the agent has learned to navigate the environment.

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# A More Realistic World
In our scenario, Peter could move around almost endlessly without feeling tired or hungry. In a more realistic world, he would need to sit down and rest occasionally, as well as eat to sustain himself. Let's make our world more realistic by implementing the following rules:

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## Prerequisites
In this lesson, we will use a library called **OpenAI Gym** to simulate different **environments**. You can run the code for this lesson locally (e.g., using Visual Studio Code), in which case the simulation will open in a new window. If you're running the code online, you may need to make some adjustments, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7).

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# Train Mountain Car
[OpenAI Gym](http://gym.openai.com) is designed so that all environments share the same API—i.e., the same methods `reset`, `step`, and `render`, as well as the same abstractions for **action space** and **observation space**. This makes it possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes.

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