@ -24,7 +24,7 @@ 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**. 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 Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), 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 😊)
- **Explore Scikit-learn**. Familiarize yourself with [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons.
@ -139,9 +139,9 @@ Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com
---
# 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).
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-77952-leestott).
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.
Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) about the basics of ML.
In this non-graded assignment, you should brush up on Python and get your environment up and running and able to run notebooks.
Take this [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), and then get your systems setup by going through these introductory videos:
Take this [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), and then get your systems setup by going through these introductory videos:
- **আপনার মেশিন কে কনফিগার করুন এই ভিডিও দেখে**. শিখার জন্য এই লিংকটি ব্যবহার করুন [কিভাবে পাইথন ইন্সটল করতে হয়](https://youtu.be/CXZYvNRIAKM) এবং [সেটআপ এ ইডিটর](https://youtu.be/EU8eayHWoZg) .
- **পাইথন শিখুন**. [পাইথন](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) এর ব্যাসিক নলেজ জানা থাকা জরুরী। এই কোর্সের প্রোগ্রামিং ল্যাঙ্গুয়েজ ডেটা সাইন্সটিস্ট এর জন্য খুবই গুরুত্বপূর্ণ।
- **পাইথন শিখুন**. [পাইথন](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) এর ব্যাসিক নলেজ জানা থাকা জরুরী। এই কোর্সের প্রোগ্রামিং ল্যাঙ্গুয়েজ ডেটা সাইন্সটিস্ট এর জন্য খুবই গুরুত্বপূর্ণ।
- **Node.js এবং JavaScript শিখুন**.ওয়েব অ্যাপস তৈরির জন্য এই কোর্সে আমরা জাবাস্ক্রিপট ব্যাবহার করব। তাই, আপনার [নোড](https://nodejs.org) এবং [npm](https://www.npmjs.com/) ইন্সটল থাকতে হবে। অন্যদিকে, পাইথন এবং জাভাস্ক্রিপট ডেভেলাপমেন্টের জন্য [ভিজুয়াল স্টুডিও](https://code.visualstudio.com/) কোড এ দুটুই আছে।
- **একটি গিটহাব অ্যাকাউন্ট তৈরি করুন**. যেহেতু আপনি আমাদের কে [গিটহাব](https://github.com) এ পেয়েছেন, তারমানে আপনার ইতিমধ্যেই একাউন্ট আছে। তবে যদি না থাকে, একটি একাউন্ট তৈরি করুন এবং পরে ফর্ক করে আপনার বানিয়ে নিন। (স্টার দিতে ভুলে যাবেন না,😊 )
- **ঘুরিয়ে আসেন Scikit-learn**. নিজেকে পরিচিত করুন [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) এর সাথে, মেশিন লার্নিং লাইব্রেরি সেট যা আমরা এই কোর্সে উল্লেখ করে থাকব
@ -141,9 +141,9 @@ MIT এর জন গাটেং মেশিন লার্নিং এর
---
# পর্যালোচনা ও সেল্ফ স্টাডি
আপনি কিভাবে ক্লাউডে এমএল অ্যালগরিদম দিয়ে কাজ করতে পারেন সে সম্পর্কে আরও জানতে, এটি অনুসরণ করুন [লার্নিং পাথ](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)।
আপনি কিভাবে ক্লাউডে এমএল অ্যালগরিদম দিয়ে কাজ করতে পারেন সে সম্পর্কে আরও জানতে, এটি অনুসরণ করুন [লার্নিং পাথ](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)।
এম এল বেসিক জানুন [লার্নিং পাথ](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa)
এম এল বেসিক জানুন [লার্নিং পাথ](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott)
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 Python**. También se recomienda que tengas un entendimiento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), 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 libre de darnos una estrella 😊)
- **Explora Scikit-learn**. Familiarízate con [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un conjunto de bibliotecas de ML que referenciamos en estas lecciones.
@ -104,9 +104,9 @@ Dibuja, en papel o usando una aplicación como [Excalidraw](https://excalidraw.c
## 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).
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-77952-leestott).
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.
Toma esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) sobre las bases de ML.
@ -18,7 +18,7 @@ Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vou
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 Python**. Il est aussi recommandé d'avoir une connaissance basique de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), 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.
@ -102,7 +102,7 @@ Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw]
## 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).
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-77952-leestott).
@ -18,7 +18,7 @@ Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang
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 Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), 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.
@ -100,7 +100,7 @@ Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https:
## 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.
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-77952-leestott) ini.
@ -19,7 +19,7 @@ Benvenuti in questo corso su machine learning classico per principianti! Che si
Prima di iniziare con questo programma di studi, è necessario che il computer sia configurato e pronto per eseguire i notebook in locale.
- **Si configuri la propria macchina con l'aiuto di questi video**. Si scopra di più su come configurare la propria macchina in questa [serie di video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
- **Imparare Python**. Si consiglia inoltre di avere una conoscenza di base di [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un linguaggio di programmazione utile per i data scientist che si utilizzerà in questo corso.
- **Imparare Python**. Si consiglia inoltre di avere una conoscenza di base di [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un linguaggio di programmazione utile per i data scientist che si utilizzerà in questo corso.
- **Imparare Node.js e JavaScript**. Talvolta in questo corso si usa anche JavaScript durante la creazione di app web, quindi sarà necessario disporre di [node](https://nodejs.org) e [npm](https://www.npmjs.com/) installati, oltre a [Visual Studio Code](https://code.visualstudio.com/) disponibile sia per lo sviluppo Python che JavaScript.
- **Creare un account GitHub**. E' probabile che si [](https://github.com)disponga già di un account GitHub, ma in caso contrario occorre crearne uno e poi eseguire il fork di questo programma di studi per utilizzarlo autonomamente. (Sentitevi liberi di darci anche una stella 😊)
- **Esplorare Scikit-learn**. Familiarizzare con Scikit-learn,[]([https://scikit-learn.org/stable/user_guide.html) un insieme di librerie ML a cui si farà riferimento in queste lezioni.
@ -101,7 +101,7 @@ Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excali
## Revisione e Auto Apprendimento
Per saperne di più su come si può lavorare con gli algoritmi ML nel cloud, si segua questo [percorso di apprendimento](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
Per saperne di più su come si può lavorare con gli algoritmi ML nel cloud, si segua questo [percorso di apprendimento](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott).
- **Node.jsとJavaScriptを学習する。** このコースではウェブアプリを構築する際にJavaScriptも何度か使用しますので、[node](https://nodejs.org)と[npm](https://www.npmjs.com/)がインストールされていること、PythonとJavaScriptの両方の開発に必要な[Visual Studio Code](https://code.visualstudio.com/)が利用可能であることが必要です。
이 커리큘럼을 시작하기 전, 컴퓨터를 세팅하고 노트북을 로컬에서 실행할 수 있게 준비해야 합니다.
- **이 영상으로 컴퓨터 세팅하기**. [영상 플레이리스트](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.
- **Python 배우기**. 이 코스에서 사용할 데이터 사이언티스트에게 유용한 프로그래밍 언어인 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)에 대한 기본적인 이해를 해야 좋습니다.
- **Node.js 와 JavaScript 배우기**. 이 코스에서 웹앱을 빌드할 때 JavaScript를 사용하므로, [node](https://nodejs.org) 와 [npm](https://www.npmjs.com/)을 설치해야 합니다. Python 과 JavaScript의 개발환경 모두 쓸 수 있는 [Visual Studio Code](https://code.visualstudio.com/)도 있습니다.
- **GitHub 계정 만들기**. [GitHub](https://github.com) 계정이 혹시 없다면, 계정을 만든 뒤에 이 커리큘럼을 포크해서 개인에 맞게 쓸 수 있습니다. (star 하셔도 됩니다 😊)
- **Scikit-learn 찾아보기**. 이 강의에서 참조하고 있는 ML 라이브러리 셋인 [Scikit-learn](https://scikit-learn.org/stable/user_guide.html)을 숙지합니다.
@ -104,9 +104,9 @@
## 리뷰 & 자기주도 학습
클라우드에서 ML 알고리즘을 어떻게 사용하는 지 자세히 알아보려면, [학습 경로](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)를 따릅니다.
클라우드에서 ML 알고리즘을 어떻게 사용하는 지 자세히 알아보려면, [학습 경로](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)를 따릅니다.
ML의 기초에 대한 [학습 경로](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa)를 봅니다.
ML의 기초에 대한 [학습 경로](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott)를 봅니다.
@ -19,7 +19,7 @@ Nossas boas vindas a este curso de machine learning clássico para iniciantes! Q
Antes de iniciar este curso, você precisa ter seu computador configurado e pronto para executar notebooks localmente.
- **Configure sua máquina com estes vídeos**. Use os links a seguir para aprender [como instalar o Python](https://youtu.be/CXZYvNRIAKM) em seu sistema e [configurar um editor de texto](https://youtu.be/EU8eayHWoZg) para desenvolvimento.
- **Aprenda Python**. Também é recomendável ter um conhecimento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), uma linguagem de programação útil para cientistas de dados (data scientists) que usamos neste curso.
- **Aprenda Python**. Também é recomendável ter um conhecimento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), uma linguagem de programação útil para cientistas de dados (data scientists) que usamos neste curso.
- **Aprenda Node.js e JavaScript**. Também usamos JavaScript algumas vezes neste curso para criar aplicativos web, então você precisará ter [node](https://nodejs.org) e [npm](https://www.npmjs.com/) instalado, assim como o [Visual Studio Code](https://code.visualstudio.com/) disponível para desenvolvimento em Python e JavaScript.
- **Crie uma conta no GitHub**. Como você nos encontrou aqui no [GitHub](https://github.com),talvez você já tenha uma conta, mas se não, crie uma e faça um fork deste curso para usar por conta própria. (Sinta-se à vontade para nos dar uma estrela também 😊).
- **Explore o Scikit-learn**. Familiarize-se com o [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), um conjunto de bibliotecas de ML referenciadas nestas lições.
@ -104,9 +104,9 @@ Esboce, no papel ou usando um aplicativo online como [Excalidraw](https://excali
## Revisão e autoestudo
Para saber mais sobre como você pode trabalhar com algoritmos de ML na nuvem, siga este [Caminho de aprendizagem](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
Para saber mais sobre como você pode trabalhar com algoritmos de ML na nuvem, siga este [Caminho de aprendizagem](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott).
Faça o [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) sobre os fundamentos do ML.
Faça o [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) sobre os fundamentos do ML.
Перед тем, как приступить к изучению этой учебной программы, вам необходимо настроить компьютер и подготовить его для работы с ноутбуками локально.
- **Настройте свою машину с помощью этих видео**. Воспользуйтесь следующими ссылками, чтобы узнать [как установить Python](https://youtu.be/CXZYvNRIAKM) в вашей системе и [настроить текстовый редактор](https://youtu.be/EU8eayHWoZg) для разработки.
- **Изучите Python**. Также рекомендуется иметь базовые знания о [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), языке программирования, полезном для специалистов по данным, который мы используем в этом курсе.
- **Изучите Python**. Также рекомендуется иметь базовые знания о [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), языке программирования, полезном для специалистов по данным, который мы используем в этом курсе.
- **Изучите Node.js и JavaScript**. Мы также несколько раз используем JavaScript в этом курсе при создании веб-приложений, поэтому вам потребуется установить [node](https://nodejs.org) и [npm](https://www.npmjs.com/), а также [Visual Studio Code](https://code.visualstudio.com/), доступный для разработки как на Python, так и на JavaScript.
- **Создайте учетную запись GitHub**. Поскольку вы нашли нас на [GitHub](https://github.com), возможно, у вас уже есть учетная запись, но если нет, создайте ее, а затем создайте форк этой учебной программы, чтобы использовать ее самостоятельно. (Не стесняйтесь поставить звезду этому репозиторию 😊)
- **Ознакомьтесь со Scikit-learn**. Ознакомьтесь со [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), набором библиотек для машинного обучения, на которые мы ссылаемся в этих уроках.
@ -139,9 +139,9 @@
---
# Обзор и самообучение
Чтобы узнать больше о том, как вы можете работать с алгоритмами машинного обучения в облаке, следуйте курсу [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
Чтобы узнать больше о том, как вы можете работать с алгоритмами машинного обучения в облаке, следуйте курсу [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott).
Пройдите курс [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) по основам машинного обучения.
Пройдите курс [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) по основам машинного обучения.
@ -18,7 +18,7 @@ Yeni başlayanlar için klasik makine öğrenimi üzerine olan bu kursa hoş gel
Bu müfredata başlamadan önce, bilgisayarınızın yerel olarak (Jupyter) not defterlerini çalıştırmak için hazır olması gerekir.
- **Makinenizi bu videolar rehberliğinde yapılandırın**. Bu [video setinde](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) makinenizi nasıl kuracağınız hakkında daha fazla bilgi edinin.
- **Python öğrenin**. Ayrıca, veri bilimciler için faydalı bir programlama dili olan ve bu derslerde kullandığımız [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) programlama dili hakkında temel bilgilere sahip olmanız da önerilir.
- **Python öğrenin**. Ayrıca, veri bilimciler için faydalı bir programlama dili olan ve bu derslerde kullandığımız [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) programlama dili hakkında temel bilgilere sahip olmanız da önerilir.
- **Node.js ve JavaScript'i öğrenin**. Web uygulamaları oluştururken de bu kursta JavaScript'i birkaç kez kullanıyoruz, bu nedenle [node](https://nodejs.org), [npm](https://www.npmjs.com/) ve ayrıca hem Python hem de JavaScript geliştirme için kullanılabilen [Visual Studio Code](https://code.visualstudio.com/) yüklü olmalıdır.
- **GitHub hesabı oluşturun**. Bizi burada [GitHub](https://github.com) üzerinde bulduğunuza göre, zaten bir hesabınız olabilir, ancak mevcut değilse, bir tane hesap oluşturun ve ardından bu müfredatı kendi başınıza kullanmak için çatallayın (fork). (Bize de yıldız vermekten çekinmeyin 😊)
- **Scikit-learn'ü keşfedin**. Bu derslerde referans verdiğimiz, bir dizi ML kütüphanesinden oluşan [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) hakkında bilgi edinin.
@ -107,7 +107,7 @@ Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bi
## İnceleme ve Bireysel Çalışma
Bulutta makine öğrenimi algoritmalarıyla nasıl çalışabileceğiniz hakkında daha fazla bilgi edinmek için bu [Eğitim Patikasını](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa) izleyin.
Bulutta makine öğrenimi algoritmalarıyla nasıl çalışabileceğiniz hakkında daha fazla bilgi edinmek için bu [Eğitim Patikasını](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott) izleyin.
- **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。
@ -100,7 +100,7 @@
## 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-77952-leestott)。
- **探索 Scikit-learn**. 熟悉 [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我們在這些課程中引用的一組 ML 庫。
@ -96,7 +96,7 @@
## 復習與自學
要了解有關如何在雲中使用 ML 算法的更多信息，請遵循以下[學習路徑](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)。
要了解有關如何在雲中使用 ML 算法的更多信息，請遵循以下[學習路徑](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)。
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:
Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), y luego configure sus sistemas con estos videos introductorios:
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 :
Suivez ce [parcours d'apprentissage Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), puis configurez votre système en parcourant ces vidéos introductives :
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:
Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) ini, kemudian persiapkan sistem kamu dengan menonton video-video pengantar ini:
In questo compito senza valutazione, si dovrebbe rispolverare Python e rendere il proprio ambiente attivo e funzionante, in grado di eseguire notebook.
Si segua questo [percorso di apprendimento di Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) e quindi si configurino i propri sistemi seguendo questi video introduttivi:
Si segua questo [percorso di apprendimento di Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) e quindi si configurino i propri sistemi seguendo questi video introduttivi:
이 미채점 과제에서는 파이썬(Python)을 복습하고 Python 실행 환경 설정 및 노트북(Jupyter Notebook) 실행 방법까지 숙지해 보시길 바랍니다.
다음 [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)를 이수하시고, 아래 Python 입문 강좌를 통해 Python 설치 및 실행 환경을 설정해 보세요:
다음 [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)를 이수하시고, 아래 Python 입문 강좌를 통해 Python 설치 및 실행 환경을 설정해 보세요:
Nesta tarefa não corrigida, você deve se aprimorar em Python e colocar seu ambiente em funcionamento e capaz de executar notebooks.
Faça o [Caminho de aprendizagem do Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), e, em seguida, faça a configuração de seus sistemas analisando estes vídeos introdutórios:
Faça o [Caminho de aprendizagem do Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), e, em seguida, faça a configuração de seus sistemas analisando estes vídeos introdutórios:
Это задание не оценивается. Вы должны освежить в памяти Python и настроить свою среду, чтобы она могла запускать ноутбуки.
Воспользуйтесь этим курсом [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), а затем настройте свою систему, просмотрев эти вводные видео:
Воспользуйтесь этим курсом [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), а затем настройте свою систему, просмотрев эти вводные видео:
Bu not-verilmeyen ödevde, Python bilgilerinizi tazelemeli, geliştirme ortamınızı çalışır duruma getirmeli ve not defterlerini çalıştırabilmelisiniz.
Bu [Python Eğitim Patikasını](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) bitirin ve ardından bu tanıtım videolarını izleyerek sistem kurulumunuzu yapın :
Bu [Python Eğitim Patikasını](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) bitirin ve ardından bu tanıtım videolarını izleyerek sistem kurulumunuzu yapın :
As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic:
Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)
[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
@ -169,7 +169,7 @@ The tool helps you to assesses how a model's predictions affect different groups
- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning.
- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) of machine learning models in Azure Machine Learning.
- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning.
@ -208,7 +208,7 @@ Explore the Fairlearn toolkit:
Read about Azure Machine Learning's tools to ensure fairness:
Como un prerrequisito, por favor toma la ruta de aprendizaje "Responsible AI Principles" y mira el vídeo debajo sobre el tema:
Aprende más acerca de la AI responsable siguiendo este [curso](https://docs.microsoft.com/es-es/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
Aprende más acerca de la AI responsable siguiendo este [curso](https://docs.microsoft.com/es-es/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)
[](https://youtu.be/dnC8-uUZXSc "Enfoque de Microsoft para la AI responsable")
@ -168,7 +168,7 @@ La herramienta te ayuda a evaluar cómo unos modelos de predicción afectan a di
- Prueba algunos [notebooks de ejemplo](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
- Aprende a [cómo activar evaluación de justicia](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) de los modelos de aprendizaje automático en Azure Machine Learning.
- Aprende a [cómo activar evaluación de justicia](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) de los modelos de aprendizaje automático en Azure Machine Learning.
- Revisa estos [notebooks de ejemplo](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) para más escenarios de evaluaciones de justicia en Azure Machine Learning.
@ -204,7 +204,7 @@ Explorar la caja de herramientas de Fairlearn
Lee acerca de las herramientas de Azure Machine Learning para asegurar justicia
En tant que prérequis, veuillez lire le guide des connaissances sur les "Principes de l'IA responsable" et regarder la vidéo sur le sujet suivant :
En apprendre plus sur l'IA responsable en suivant ce [guide des connaissances](https://docs.microsoft.com/fr-fr/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
En apprendre plus sur l'IA responsable en suivant ce [guide des connaissances](https://docs.microsoft.com/fr-fr/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)
[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
@ -169,7 +169,7 @@ L'outil aide à évaluer comment les prédictions d'un modèle affectent différ
- Essayez quelques [notebooks d'exemples](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
- Apprenez [comment activer les évaluations d'équités](https://docs.microsoft.com/fr-fr/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) des modèles de machine learning sur Azure Machine Learning.
- Apprenez [comment activer les évaluations d'équités](https://docs.microsoft.com/fr-fr/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) des modèles de machine learning sur Azure Machine Learning.
- Jetez un coup d'oeil aux [notebooks d'exemples](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) pour plus de scénarios d'évaluation d'équités sur Azure Machine Learning.
@ -205,7 +205,7 @@ Explorer la boite à outils Fairlearn
Lire sur les outils Azure Machine Learning afin d'assurer l'équité
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
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-77952-leestott) ini
[](https://youtu.be/dnC8-uUZXSc "Pendekatan Microsoft untuk AI yang Bertanggung Jawab")
@ -170,7 +170,7 @@ Pelajaran pengantar ini tidak membahas secara mendalam mengenai detail mitigasi
- 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.
- Pelajari [bagaimana cara mengaktifkan penilaian keadilan](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) 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.
@ -206,7 +206,7 @@ Jelajahi *toolkit* Fairlearn
Baca mengenai *tools* Azure Machine Learning untuk memastikan keadilan
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)
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-77952-leestott)
[](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile")
@ -167,7 +167,7 @@ Lo strumento consente di valutare in che modo le previsioni di un modello influi
- 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 scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) 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.
@ -205,7 +205,7 @@ Si esplori il toolkit Fairlearn
Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità
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
[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)를 따라서 Responsible AI에 대하여 더 자세히 알아보세요
[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
@ -170,7 +170,7 @@ AI와 머신러닝의 공정성을 보장하는 건 계속 복잡한 사회기
- 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 머신러닝 모델의 [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) 알아보기.
- Azure Machine Learning에서 더 공정한 평가 시나리오에 대하여 [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) 확인해보기.
Como pré-requisito, siga o Caminho de aprendizagem "Princípios de AI responsável" e assista ao vídeo abaixo sobre o tópico:
Saiba mais sobre a AI responsável seguindo este [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
Saiba mais sobre a AI responsável seguindo este [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)
[](https://youtu.be/dnC8-uUZXSc "Abordagem da Microsoft para AI responsável")
@ -167,7 +167,7 @@ The tool helps you to assesses how a model's predictions affect different groups
- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning.
- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) of machine learning models in Azure Machine Learning.
- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning.
@ -204,7 +204,7 @@ Explore o kit de ferramentas Fairlearn
Leia sobre as ferramentas do Azure Machine Learning para garantir justiça
@ -89,7 +89,7 @@ In the context of machine learning, model fitting refers to the accuracy of the
## Parameter tuning
Once your initial training is complete, observe the quality of the model and consider improving it by tweaking its 'hyperparameters'. Read more about the process [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
Once your initial training is complete, observe the quality of the model and consider improving it by tweaking its 'hyperparameters'. Read more about the process [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott).
@ -89,7 +89,7 @@ En el contexto del machine learning, el ajuste del modelo se refiere a la precis
## 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).
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-77952-leestott).
@ -86,7 +86,7 @@ Dalam konteks machine learning, *model fitting* mengacu pada keakuratan dari fun
## 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).
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-77952-leestott).
@ -89,7 +89,7 @@ Nel contesto di machine learning, l'adattamento del modello si riferisce all'acc
## 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).
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-77952-leestott).
@ -89,7 +89,7 @@ feature는 데이터의 측정할 수 있는 속성입니다. 많은 데이터
## 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.
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-77952-leestott) 프로세스에 대하여 알아봅니다.
@ -89,7 +89,7 @@ No contexto do machine learning, o ajuste do modelo refere-se à precisão da fu
## Ajuste de parâmetro
Quando o treinamento inicial estiver concluído, observe a qualidade do modelo e considere melhorá-lo ajustando seus 'hiperparâmetros'. Leia mais sobre o processo [na documentação](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
Quando o treinamento inicial estiver concluído, observe a qualidade do modelo e considere melhorá-lo ajustando seus 'hiperparâmetros'. Leia mais sobre o processo [na documentação](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott).
@ -25,13 +25,13 @@ In this lesson, you will learn how to:
> 🎥 Click the image above for a video: using Python within VS Code.
1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well, to ease the setup for some users.
1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) available as well, to ease the setup for some users.
Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a [virtual environment](https://docs.python.org/3/library/venv.html).
2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) for Python development.
2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) for Python development.
> Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
> Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)
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.
@ -205,7 +205,7 @@ Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.
In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
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.
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-77952-leestott) to deepen your understanding.
> 🎥 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.
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-77952-leestott) 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.
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-77952-leestott) 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)
> 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-77952-leestott)
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.
@ -199,7 +199,7 @@ Grafique una variable diferente de este conjunto de datos. Sugerencia: edite est
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.
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-77952-leestott) para profundizar su comprensión.
@ -23,13 +23,13 @@ Dalam pelajaran ini, kamu akan belajar bagaimana untuk:
> 🎥 Klik foto di atas untuk sebuah video: menggunakan Python dalam VS Code
1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Python sudah dipasang di kebanyakan sistem komputer. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna.
1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Python sudah dipasang di kebanyakan sistem komputer. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna.
Beberapa penggunaan Python memerlukan satu versi perangkat lunak tersebut, sedangkan beberapa penggunaan lainnya mungkin memerlukan versi Python yang beda lagi. Oleh sebab itulah akan sangat berguna untuk bekerja dalam sebuah *[virtual environment](https://docs.python.org/3/library/venv.html)* (lingkungan virtual).
2. **Pasang Visual Studio Code**. Pastikan kamu sudah memasangkan Visual Studio Code di komputermu. Ikuti instruksi-instruksi ini untuk [memasangkan Visual Studio Code](https://code.visualstudio.com/) untuk instalasi dasar. Kamu akan menggunakan Python dalam Visual Studio Code dalam kursus ini, jadi kamu mungkin akan ingin mencari tahu cara [mengkonfigurasi Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) untuk menggunakan Python.
2. **Pasang Visual Studio Code**. Pastikan kamu sudah memasangkan Visual Studio Code di komputermu. Ikuti instruksi-instruksi ini untuk [memasangkan Visual Studio Code](https://code.visualstudio.com/) untuk instalasi dasar. Kamu akan menggunakan Python dalam Visual Studio Code dalam kursus ini, jadi kamu mungkin akan ingin mencari tahu cara [mengkonfigurasi Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) untuk menggunakan Python.
> Nyamankan diri dengan Python dengan mengerjakan [koleksi modul pembelajaran ini](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
> Nyamankan diri dengan Python dengan mengerjakan [koleksi modul pembelajaran ini](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)
3. **Pasang Scikit-learn**, dengan mengikuti [instruksi di sini](https://scikit-learn.org/stable/install.html). Karena harus dipastikan bahwa kamu sedang menggunakan Python 3, kami anjurkan kamu menggunakan sebuah *virtual environment*. Ingatlah juga bahwa jika kamu ingin memasangkan ini di sebuah M1 Mac, ada instruksi khusus dalam laman yang ditautkan di atas.
@ -201,7 +201,7 @@ Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini
Dalam tutorial ini, kamu bekerja dengan sebuah model regresi linear yang sederhana daripada regresi linear univariat atau berganda. Bacalah sedikit tentang perbedaan antara metode-metode ini atau tontonlah [video ini](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef).
Bacalah lebih banyak tentang konsep regresi dan pikirkanlah tentang jenis pertanyaan apa saja yang bisa dijawab teknik ini. Cobalah [tutorial ini](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) untuk memperdalam pemahamanmu.
Bacalah lebih banyak tentang konsep regresi dan pikirkanlah tentang jenis pertanyaan apa saja yang bisa dijawab teknik ini. Cobalah [tutorial ini](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) untuk memperdalam pemahamanmu.
@ -24,13 +24,13 @@ In questa lezione, si imparerà come:
> 🎥 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.
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-77952-leestott) 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.
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-77952-leestott) 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)
> 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-77952-leestott)
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.
@ -203,7 +203,7 @@ Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modific
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.
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-77952-leestott) per approfondire la propria conoscenza.
2. **Visual Studio Codeのインストール**: Visual Studio Codeがコンピュータにインストールされていることを確認してください。[こちらの手順](https://code.visualstudio.com/) でVisual Studio Codeをインストールしてください。このコースでは、Visual Studio CodeでPythonを使用しますので [Visual Studio Codeの設定](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) をブラッシュアップしておくといいです。
2. **Visual Studio Codeのインストール**: Visual Studio Codeがコンピュータにインストールされていることを確認してください。[こちらの手順](https://code.visualstudio.com/) でVisual Studio Codeをインストールしてください。このコースでは、Visual Studio CodeでPythonを使用しますので [Visual Studio Codeの設定](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) をブラッシュアップしておくといいです。
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.
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-77952-leestott)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.
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-77952-leestott)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.
> 이 [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)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.
@ -206,7 +206,7 @@ Scikit-learn 사용하면 올바르게 모델을 만들고 사용하기 위해
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.
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-77952-leestott)로 깊게 이해합니다.
@ -25,13 +25,13 @@ Nesta lição, você aprenderá como:
> 🎥 Clique na imagem acima para assistir o vídeo: usando Python no VS Code (vídeo em inglês).
1. **Instale Python**. Verifique se você já instalou [Python](https://www.python.org/downloads/) em seu computador. Você usará Python para muitas tarefas de _data science_ (ciência de dados) e _machine learning_. A maioria dos sistemas de computador já possui Python instalado. Existem [Pacotes de Código em Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponíveis para ajudar na instalação.
1. **Instale Python**. Verifique se você já instalou [Python](https://www.python.org/downloads/) em seu computador. Você usará Python para muitas tarefas de _data science_ (ciência de dados) e _machine learning_. A maioria dos sistemas de computador já possui Python instalado. Existem [Pacotes de Código em Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) disponíveis para ajudar na instalação.
Algumas aplicações em Python exigem versões diferentes da linguagem. Portanto, será útil trabalhar com [ambiente virtual](https://docs.python.org/3/library/venv.html).
2. **Instale o Visual Studio Code**. Verifique se já existe o Visual Studio Code instalado em seu computador. Siga essas instruções para [instalar o Visual Studio Code](https://code.visualstudio.com/) com uma instalação básica. Você usará Python no Visual Studio Code neste curso e precisará [configurar o Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) para isso.
2. **Instale o Visual Studio Code**. Verifique se já existe o Visual Studio Code instalado em seu computador. Siga essas instruções para [instalar o Visual Studio Code](https://code.visualstudio.com/) com uma instalação básica. Você usará Python no Visual Studio Code neste curso e precisará [configurar o Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) para isso.
> Fique mais confortável em usar Python trabalhando nessa coleção de [módulos de aprendizagem](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa).
> Fique mais confortável em usar Python trabalhando nessa coleção de [módulos de aprendizagem](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott).
3. **Instale a Scikit-learn**, seguindo [estas instruções](https://scikit-learn.org/stable/install.html). Visto que você precisa ter certeza que está usando o Python 3, é recomendável usar um ambiente virtual. Note que se você estiver usando essa biblioteca em um M1 Mac, há instruções específicas na página linkada acima.
@ -206,7 +206,7 @@ Plote uma variável diferente desse mesmo conjunto de dados. Dica: edite a linha
Neste tutorial, você trabalhou com regressão linear simples, ao invés de regressão univariada ou múltipla. Leia sobre as diferença desses métodos, ou assista [esse vídeo](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef).
Leia mais sobre o conceito de regressão e pense sobre os tipos de questões que podem ser respondidas usando essa técnica. Faça esse [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) para aprender mais.
Leia mais sobre o conceito de regressão e pense sobre os tipos de questões que podem ser respondidas usando essa técnica. Faça esse [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) para aprender mais.
> 🎥 Clique na imagem acima para um vídeo: utilizando Python dentro do Código VS.
1. **Instalar Python**. Certifique-se de que [Python](https://www.python.org/downloads/) está instalado no seu computador. Você usará Python para muitas tarefas de ciência de dados e machine learning. A maioria dos sistemas informáticos já inclui uma instalação Python. Há úteis [Python Pacotes de codificação](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponível também, para facilitar a configuração para alguns utilizadores.
1. **Instalar Python**. Certifique-se de que [Python](https://www.python.org/downloads/) está instalado no seu computador. Você usará Python para muitas tarefas de ciência de dados e machine learning. A maioria dos sistemas informáticos já inclui uma instalação Python. Há úteis [Python Pacotes de codificação](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) disponível também, para facilitar a configuração para alguns utilizadores.
Alguns usos de Python, no entanto, requerem uma versão do software, enquanto outros requerem uma versão diferente. Por esta razão, é útil trabalhar dentro de um [ambiente virtual](https://docs.python.org/3/library/venv.html).
2. **Instalar código de estúdio visual**. Certifique-se de que tem o Código do Estúdio Visual instalado no seu computador. Siga estas instruções para
[instalar Código do Estúdio Visual](https://code.visualstudio.com/) para a instalação básica. Você vai usar Python em Código estúdio visual neste curso, então você pode querer relembrá-lo [configurar código de estúdio visual](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) para o desenvolvimento de Python.
[instalar Código do Estúdio Visual](https://code.visualstudio.com/) para a instalação básica. Você vai usar Python em Código estúdio visual neste curso, então você pode querer relembrá-lo [configurar código de estúdio visual](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) para o desenvolvimento de Python.
> Fique confortável com python trabalhando através desta coleção de [Aprender módulos](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
> Fique confortável com python trabalhando através desta coleção de [Aprender módulos](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)
(https://scikit-learn.org/stable/install.html). Uma vez que precisa de garantir que utiliza o Python 3, recomenda-se que utilize um ambiente virtual. Note que se estiver a instalar esta biblioteca num Mac M1, existem instruções especiais na página acima ligada.
@ -208,7 +208,7 @@ Defina uma variável diferente deste conjunto de dados. Dica: edite esta linha:`
Neste tutorial, trabalhou com uma simples regressão linear, em vez de univariado ou regressão linear múltipla. Leia um pouco sobre as diferenças entre estes métodos, ou dê uma olhada[este vídeo](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
Leia mais sobre o conceito de regressão e pense sobre que tipo de perguntas podem ser respondidas por esta técnica. Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) para aprofundar a sua compreensão.
Leia mais sobre o conceito de regressão e pense sobre que tipo de perguntas podem ser respondidas por esta técnica. Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) para aprofundar a sua compreensão.
@ -25,13 +25,13 @@ Bu derste, şunları öğreneceğiz:
> 🎥 Video için yukarıdaki resme tıklayınız: Python'u VS Code içinde kullanma.
1. **Python Kurulumu**. [Python](https://www.python.org/downloads/) kurulumunun bilgisayarınızda yüklü olduğundan emin olun.Python'u birçok veri bilimi ve makine öğrenimi görevi için kullanacaksınız. Çoğu bilgisayar sistemi zaten bir Python kurulumu içerir. Şurada [Python Kodlama Paketleri](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) mevcut, bazı kullanıcılar için kurulumu daha kolay.
1. **Python Kurulumu**. [Python](https://www.python.org/downloads/) kurulumunun bilgisayarınızda yüklü olduğundan emin olun.Python'u birçok veri bilimi ve makine öğrenimi görevi için kullanacaksınız. Çoğu bilgisayar sistemi zaten bir Python kurulumu içerir. Şurada [Python Kodlama Paketleri](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) mevcut, bazı kullanıcılar için kurulumu daha kolay.
Ancak Python'un bazı kullanımları, yazılımın spesifik bir sürümünü gerektirir, diğerleri ise farklı bir sürüm gerektirir. Bu yüzden, [virtual environment](https://docs.python.org/3/library/venv.html) (sanal ortamlar) ile çalışmak daha kullanışlıdır.
2. **Visual Studio Code kurulumu**. Visual Studio Code'un bilgisayarınıza kurulduğundan emin olun. [Visual Studio Code kurulumu](https://code.visualstudio.com/) bu adımları takip ederek basitçe bir kurulum yapabilirsiniz. Bu kursta Python'ı Visual Studio Code'un içinde kullanacaksınız, bu yüzden nasıl yapılacağını görmek isteyebilirsiniz. Python ile geliştirme için [Visual Studio Code konfigürasyonu](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa).
2. **Visual Studio Code kurulumu**. Visual Studio Code'un bilgisayarınıza kurulduğundan emin olun. [Visual Studio Code kurulumu](https://code.visualstudio.com/) bu adımları takip ederek basitçe bir kurulum yapabilirsiniz. Bu kursta Python'ı Visual Studio Code'un içinde kullanacaksınız, bu yüzden nasıl yapılacağını görmek isteyebilirsiniz. Python ile geliştirme için [Visual Studio Code konfigürasyonu](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott).
> Bu koleksiyon üzerinde çalışarak Python ile rahatlayın. [Modülleri öğren](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
> Bu koleksiyon üzerinde çalışarak Python ile rahatlayın. [Modülleri öğren](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)
3. **Scikit-learn kurulumu**, [bu talimatları](https://scikit-learn.org/stable/install.html) takip ediniz. Python 3 kullandığınızdan emin olmanız gerektiğinden, sanal ortam kullanmanız önerilir. Not, bu kütüphaneyi bir M1 Mac'e kuruyorsanız, yukarıda bağlantısı verilen sayfada özel talimatlar var onları takip ediniz.
@ -203,7 +203,7 @@ Bu veri kümesinden farklı bir değişken çizin. İpucu: bu satırı düzenley
Bu eğitimde, tek değişkenli veya çoklu doğrusal regresyon yerine basit doğrusal regresyonla çalıştınızBu yöntemler arasındaki farklar hakkında biraz bilgi edinin veya şuna bir göz atın: [bu videoya](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
Regresyon kavramı hakkında daha fazla bilgi edinin ve bu teknikle ne tür soruların yanıtlanabileceğini düşünün. Anlayışınızı derinleştirmek için bu [eğitime](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) göz atabilirsiniz.
Regresyon kavramı hakkında daha fazla bilgi edinin ve bu teknikle ne tür soruların yanıtlanabileceğini düşünün. Anlayışınızı derinleştirmek için bu [eğitime](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) göz atabilirsiniz.
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-77952-leestott) 用于 Python 开发。
2. **安裝 Visual Studio Code**。確保你的計算機上安裝了 Visual Studio Code。按照這些說明 [安裝 Visual Studio Code](https://code.visualstudio.com/) 進行基本安裝。在本課程中，你將在 Visual Studio Code 中使用 Python，因此你可能想復習如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) 用於 Python 開發。
2. **安裝 Visual Studio Code**。確保你的計算機上安裝了 Visual Studio Code。按照這些說明 [安裝 Visual Studio Code](https://code.visualstudio.com/) 進行基本安裝。在本課程中，你將在 Visual Studio Code 中使用 Python，因此你可能想復習如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) 用於 Python 開發。
@ -147,7 +147,7 @@ Visualizations can also help determine the machine learning technique most appro
One data visualization library that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson).
> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott).
@ -147,7 +147,7 @@ Las visualizaciones también ayudan a determinar la técnica de aprendizaje auto
Una librería de visualización de datos que funciona bien en los notebooks de Jupyter es [Matplotlib](https://matplotlib.org/) (la cual también viste en la lección anterior).
> Obtén más experiencia con la visualización de datos en [estos tutoriales](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
> Obtén más experiencia con la visualización de datos en [estos tutoriales](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott).
@ -142,7 +142,7 @@ Visualisasi juga bisa membantu menentukan teknik *machine learning* yang palingn
Satu *library* visualisasi data yang bekerja dengan baik dalam sebuah *Jupyter notebook* adalah [Matplotlib](https://matplotlib.org/) (yang kamu juga lihat dalam pelajaran sebelumnya).
> Carilah pengalaman dalam memvisualisasi data dengan [tutorial-tutorial ini](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
> Carilah pengalaman dalam memvisualisasi data dengan [tutorial-tutorial ini](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott).
@ -141,7 +141,7 @@ Le visualizzazioni possono anche aiutare a determinare la tecnica di machine lea
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).
> 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-77952-leestott).
@ -148,7 +148,7 @@ As visualizações também podem ajudar a determinar a técnica de _machine lear
Uma biblioteca de visualização de dados que funciona bem nos blocos de _notebooks_ é a [Matplotlib](https://matplotlib.org/) (que você também viu na lição anterior).
> Ganhe mais experiência em visualização de dados fazendo [esses tutoriais](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
> Ganhe mais experiência em visualização de dados fazendo [esses tutoriais](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott).
✅ 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)
✅ 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-77952-leestott)
## 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`.
"✅ 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",
"✅ 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-77952-leestott)\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)
✅ 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-77952-leestott)
✅ Profundiza tu entendimiento de trabajo con este tipo de regresión en este [módulo de aprendizaje(https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
✅ Profundiza tu entendimiento de trabajo con este tipo de regresión en este [módulo de aprendizaje(https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott)
## Requisitos previos
Haber trabajado con los datos de calabazas, ahora estamos suficientemente familiarizados con estos para entender que hay una categoría binaria que podemos trabajar con `Color`.
@ -14,7 +14,7 @@ Dalam pelajaran ini, kamu akan belajar:
- Sebuah *library* baru untuk pemvisualisasian data
- Teknik-teknik untuk regresi logistik
✅ Perdalamkan pemahamanmu dalam bekerja dengan regresi jenis ini dalam [modul pembelajaran ini](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
✅ Perdalamkan pemahamanmu dalam bekerja dengan regresi jenis ini dalam [modul pembelajaran ini](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott)
- 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
✅ Con questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) 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).
✅ [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)애서 regression의 타입에 대하여 깊게 이해해봅니다.
✅ [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott)애서 regression의 타입에 대하여 깊게 이해해봅니다.
✅ Aprofunde seu conhecimento de como trabalhar com este tipo de regressão neste [módulo](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa).
✅ Aprofunde seu conhecimento de como trabalhar com este tipo de regressão neste [módulo](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott).
✅ aprofundar a sua compreensão de trabalhar com este tipo de regressão neste [módulo Aprender](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
✅ aprofundar a sua compreensão de trabalhar com este tipo de regressão neste [módulo Aprender](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott)
## Pré-requisito
Tendo trabalhado com os dados da abóbora, estamos agora familiarizados o suficiente para perceber que há uma categoria binária com a qual podemos trabalhar:` Cor`.
@ -17,7 +17,7 @@ In this series of lessons, you'll discover the differences between linear and lo
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)
> 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-77952-leestott)
@ -14,7 +14,7 @@ En esta serie de lecciones, descubrirá la diferencia entre la regresión lineal
En este grupo de lecciones, se preparará para comenzar las tareas 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 trabajar 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)
> Existen herramientas útiles _low-code_ que pueden ayudarlo a aprender a trabajar 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-77952-leestott)
@ -14,7 +14,7 @@ Dans cette série de leçons, vous découvrirez la différence entre la régress
Dans ce groupe de leçons, vous serez préparé afin de commencer les tâches de machine learning, y compris la configuration de Visual Studio Code pour gérer les blocs-notes, l'environnement commun pour les scientifiques des données. Vous découvrirez Scikit-learn, une bibliothèque pour le machine learning, et vous construirez vos premiers modèles, en vous concentrant sur les modèles de régression dans ce chapitre.
> Il existe des outils low-code utiles qui peuvent vous aider à apprendre à travailler avec des modèles de régression. Essayez [Azure ML pour cette tâche](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
> Il existe des outils low-code utiles qui peuvent vous aider à apprendre à travailler avec des modèles de régression. Essayez [Azure ML pour cette tâche](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)
पाठों के इस समूह में, आप मशीन लर्निंग सीखने के कार्यों को शुरू करने के लिए तैयार होंगे, जिसमें नोटबुक को प्रबंधित करने के लिए विजुअल स्टूडियो कोड को कॉन्फ़िगर करना, डेटा वैज्ञानिकों के लिए सामान्य वातावरण शामिल है। आप मशीन लर्निंग के लिए एक लाइब्रेरी स्किकिट-लर्न की खोज करेंगे, और आप इस अध्याय में रिग्रेशन मॉडल पर ध्यान केंद्रित करते हुए अपना पहला मॉडल बनाएंगे।
>ये उपयोगी निम्न-कोड उपकरण हैं जो आपको रिग्रेशन मॉडल के साथ काम करने के बारे में जानने में मदद कर सकते हैं.इस्तेमाल करे [इस कार्य के लिए अज़ूरे एमएल](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
>ये उपयोगी निम्न-कोड उपकरण हैं जो आपको रिग्रेशन मॉडल के साथ काम करने के बारे में जानने में मदद कर सकते हैं.इस्तेमाल करे [इस कार्य के लिए अज़ूरे एमएल](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)
@ -14,7 +14,7 @@ Dalam seri pelajaran ini, kamu akan menemukan perbedaan antara regresi linear da
Selain itu, kamu akan disiapkan untuk mulai mengerjakan tugas *machine learning*, termasuk mengkonfigurasi Visual Studio Code untuk mengelola *notebook*, lingkungan wajar untuk *data scientist*. Kamu akan menemukan Scikit-learn, sebuah *library* untuk *machine learning*, dan kamu akan membangun model pertamamu dengan memfokus pada model regresi dalam bab ini.
> Ada alat-alat *low-code* yang dapat membantumu belajar tentang bekerja dengan model regresi. Cobalah [Azure ML untuk tugas ini](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa).
> Ada alat-alat *low-code* yang dapat membantumu belajar tentang bekerja dengan model regresi. Cobalah [Azure ML untuk tugas ini](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott).
@ -15,7 +15,7 @@ In questa serie di lezioni si scoprirà la differenza tra regressione lineare e
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)
> 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-77952-leestott)
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。
> 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-77952-leestott)を使ってみてください。
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.
> 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-77952-leestott)를 시도해보세요.
@ -15,7 +15,7 @@ Nesta série de lições, você descobrirá a diferença entre regressão linear
Neste grupo de lições, te prepararemos para começar tarefas de _machine learning_, incluindo configuração do Visual Studio Code para gerenciar _notebooks_, o ambiente comum para _data scientists_ (cientistas de dados). Você descobrirá a Scikit-learn, uma biblioteca para _machine learning_, e construirá seus primeiros modelos, focando em modelos de regressão neste capítulo.
> Existem ferramentas _low-code_ que podem ajudar a aprender como trabalhar com modelos de regressão. Use a [Azure ML para esta tarefa](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa).
> Existem ferramentas _low-code_ que podem ajudar a aprender como trabalhar com modelos de regressão. Use a [Azure ML para esta tarefa](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott).
@ -19,7 +19,7 @@ Nesta série de lições, você vai descobrir a diferença entre regressão log
Neste grupo de lições, você será configurado para iniciar tarefas de machine learning, incluindo configurar o Código do Estúdio Visual para gerir cadernos, o ambiente comum para cientistas de dados. Você vai descobrir Scikit-learn, uma biblioteca para machine learning, e você vai construir seus primeiros modelos, focando-se em modelos de Regressão neste capítulo.
> Existem ferramentas de baixo código úteis que podem ajudá-lo a aprender sobre trabalhar com modelos de regressão. Tente
[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)
В этой группе уроков вы будете подготовлены, чтобы приступить к задачам машинного обучения, включая настройку Visual Studio Code для управления записными книжками, распространенным иструментом среди специалистов по данным. Вы откроете для себя scikit-learn, библиотеку для машинного обучения, и создадите свои первые модели, фокусируясь на регрессии в этой главе.
> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях регрессии. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa).
> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях регрессии. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott).
@ -17,7 +17,7 @@ Bu dersler dizisinde, lineer ve lojistik regresyon arasındaki farkları ve ne z
Bu ders grubunda, veri bilimcileri için ortak ortam olan not defterlerini yönetmek için Visual Studio Code'un yapılandırılması dahil olmak üzere makine öğrenimi görevlerine başlamak için hazırlanacaksınız.
> Regresyon modelleriyle çalışma hakkında bilgi edinmenize yardımcı olabilecek kullanışlı low-code (az kodlamalı) araçlar vardır. Bunu deneyin. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
> Regresyon modelleriyle çalışma hakkında bilgi edinmenize yardımcı olabilecek kullanışlı low-code (az kodlamalı) araçlar vardır. Bunu deneyin. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)
> 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)
> 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-77952-leestott)
> 這裏有一些代碼難度較低但很有用的工具可以幫助你學習使用回歸模型。 試一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
> 這裏有一些代碼難度較低但很有用的工具可以幫助你學習使用回歸模型。 試一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)
@ -27,7 +27,7 @@ There are many questions you need to ask:
- **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
- **Using TensorFlow.** 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 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-77952-leestott) 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-77952-leestott) 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`.
@ -27,7 +27,7 @@ Hay muchas preguntas que necesitas realizar:
- **¿Qué tecnología se usó para entrenar al modelo?** La tecnología elegida puede influir en las herramientas que necesitas utilizar.
- **Uso de TensorFlow.** Si estás entrenando un modelo usando TensorFlow, por ejemplo, ese ecosistema proporciona la capacidad de convertir un modelo de TensorFlow para su uso en una aplicación web usando [TensorFlow.js](https://www.tensorflow.org/js/).
- **Uso de PyTorch.** Si estás construyendo un modelo usando una librería como [PyTorch](https://pytorch.org/), tienes la opción de exportarlo en formato [ONNX](https://onnx.ai/) (Open Neural Network Exchange) para usarlo en aplicaciones web de javascript que puedan usar el entorno de ejecución [Onnx Runtime](https://www.onnxruntime.ai/). Esta opción será explorada en una futura lección para un modelo entrenado Scikit-learn.
- **Uso de Lobe.ai o Azure Custom Vision.** Si estás usando un sistema de aprendizaje automático SaaS (Software as a Service) como lo es [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) para entrenar un modelo, este tipo de software proporciona formas de exportar el modelo a diversas plataformas, incluyendo el construir una API a medida para que esta sea consultada en la nube por tu aplicación en línea.
- **Uso de Lobe.ai o Azure Custom Vision.** Si estás usando un sistema de aprendizaje automático SaaS (Software as a Service) como lo es [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) para entrenar un modelo, este tipo de software proporciona formas de exportar el modelo a diversas plataformas, incluyendo el construir una API a medida para que esta sea consultada en la nube por tu aplicación en línea.
También tienes la oportunidad de construir una plicación web completamente en Flask que será capaz de entrenar el propio modelo en un navegador web. Esto también puede ser realizado usando TensorFlow.js en un contexto JavaScript.
@ -37,7 +37,7 @@ Para nuestros propósitos, ya que hemos estado trabajando con notebooks basados
Para esta tarea, necesitas dos herramientas: Flask y Pickle, ambos corren en Python.
✅ ¿Qué es [Flask](https://palletsprojects.com/p/flask/)? Definido como un 'micro-framework' por sus creadores, Flask proporciona las características básicas de los frameworks web usando Python y un motor de plantillas para construir páginas web. Da un vistazo a [este módulo de aprendizaje](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) para practicar construir con Flask.
✅ ¿Qué es [Flask](https://palletsprojects.com/p/flask/)? Definido como un 'micro-framework' por sus creadores, Flask proporciona las características básicas de los frameworks web usando Python y un motor de plantillas para construir páginas web. Da un vistazo a [este módulo de aprendizaje](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) para practicar construir con Flask.
✅ ¿Qué es [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 es un módulo de Python que serializa y deserializa estructura de objetos Python. Cuando conviertes un modelo en 'pickle', serializas o aplanas su estructura para su uso en la web. Sé cuidadoso: Pickle no es intrínsecamente seguro, por lo que debes ser cuidadoso si solicitaste hacer 'un-pickle' en un archivo. Un archivo hecho pickle tiene el sufijo `.pkl`.
- **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.
- **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-77952-leestott) 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.
@ -37,7 +37,7 @@ Per questo scopo, poiché si è lavorato con i notebook basati su Python, verran
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 [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-77952-leestott) 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`.
- **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.
- **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-77952-leestott) 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.
@ -37,7 +37,7 @@
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.
✅ [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-77952-leestott)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.
@ -27,7 +27,7 @@ Existem muitas perguntas que você precisa fazer:
- **Qual tecnologia foi usada para treinar o modelo?** A tecnologia escolhida pode influenciar o ferramental que você precisa usar.
- **Usando o fluxo do Tensor.** Se você estiver treinando um modelo usando o TensorFlow, por exemplo, esse ecossistema oferece a capacidade de converter um modelo do TensorFlow para uso em um aplicativo da web usando [TensorFlow.js](https://www.tensorflow.org/js/).
- **Usando o PyTorch.** Se você estiver construindo um modelo usando uma biblioteca como [PyTorch](https://pytorch.org/), você tem a opção de exportá-lo em formato [ONNX](https://onnx.ai/) (Troca de rede neural aberta (Open Neural Network Exchange)) para uso em aplicativos web JavaScript que podem usar o [Onnx Runtime](https://www.onnxruntime.ai/). Esta opção será explorada em uma lição futura para um modelo treinado para aprender com Scikit.
- **Usando Lobe.ai ou Azure Custom Vision.** Se você estiver usando um sistema ML SaaS (Software as a Service), como [Lobe.ai](https://lobe.ai/) ou [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) para treinar um modelo, este tipo de software fornece maneiras de exportar o modelo para muitas plataformas, incluindo a construção de uma API sob medida para ser consultada na nuvem por seu aplicativo online.
- **Usando Lobe.ai ou Azure Custom Vision.** Se você estiver usando um sistema ML SaaS (Software as a Service), como [Lobe.ai](https://lobe.ai/) ou [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) para treinar um modelo, este tipo de software fornece maneiras de exportar o modelo para muitas plataformas, incluindo a construção de uma API sob medida para ser consultada na nuvem por seu aplicativo online.
Você também tem a oportunidade de construir um aplicativo web Flask inteiro que seria capaz de treinar o próprio modelo em um navegador da web. Isso também pode ser feito usando TensorFlow.js em um contexto JavaScript.
@ -37,7 +37,7 @@ Para nossos propósitos, já que estamos trabalhando com notebooks baseados em P
Para esta tarefa, você precisa de duas ferramentas: Flask e Pickle, ambos executados em Python.
✅ O que é [Flask](https://palletsprojects.com/p/flask/)? Definido como um 'micro-framework' por seus criadores, o Flask fornece os recursos básicos de estruturas web usando Python e um mecanismo de modelagem para construir páginas web. Dê uma olhada [neste módulo de aprendizagem](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) para praticar a construção com Flask.
✅ O que é [Flask](https://palletsprojects.com/p/flask/)? Definido como um 'micro-framework' por seus criadores, o Flask fornece os recursos básicos de estruturas web usando Python e um mecanismo de modelagem para construir páginas web. Dê uma olhada [neste módulo de aprendizagem](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) para praticar a construção com Flask.
✅ O que é [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 é um módulo Python que serializa e desserializa a estrutura de um objeto Python. Quando você 'pickle' um modelo, serializa ou aplaina sua estrutura para uso na web. Tenha cuidado: pickle não é intrinsecamente seguro, então tome cuidado se for solicitado para ser feito um 'un-pickle' em um arquivo. Um arquivo tem o sufixo `.pkl`.
@ -39,7 +39,7 @@ Para nossos propósitos, já que estamos trabalhando com notebooks baseados em P
Para esta tarefa, você precisa de duas ferramentas: Flask e Pickle, ambos em Python.
O que é [Frasco](https://palletsprojects.com/p/flask/)? Definido como um 'microframework' por seus criadores, o Flask fornece as características básicas de frameworks web usando Python e um motor de modelagem para construir páginas web. Dê uma olhada em [este módulo de aprendizado](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) para praticar a construção com o Flask.
O que é [Frasco](https://palletsprojects.com/p/flask/)? Definido como um 'microframework' por seus criadores, o Flask fornece as características básicas de frameworks web usando Python e um motor de modelagem para construir páginas web. Dê uma olhada em [este módulo de aprendizado](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) para praticar a construção com o Flask.
✅ O que é [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 é um módulo Python que serializa e desserializa uma estrutura de objeto Python. Ao "pichar" um modelo, você serializa ou achata sua estrutura para uso na web. Tenha cuidado: o pickle não é intrinsecamente seguro, portanto, tenha cuidado se for solicitado a `cancelar o pickle` de um arquivo. Um arquivo em conserto tem o sufixo `.pkl`.
@ -101,11 +101,11 @@ So, which classifier should you choose? Often, running through several and looki
> Plots generated on Scikit-learn's documentation
> 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)
> 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-77952-leestott)
### 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 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-77952-leestott). Here, we discover that, for our multiclass problem, we have some choices:
> A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options
Some files were not shown because too many files have changed in this diff
Show More
Lee Stott
2 years ago
committed by
GitHub