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<!-- WEHUB_ZH_README -->
> [!NOTE]
> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
> [English](./README.en.md) · [原始项目](https://github.com/microsoft/ML-For-Beginners) · [上游 README](https://github.com/microsoft/ML-For-Beginners/blob/HEAD/README.md)
> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
@@ -8,16 +14,16 @@
[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
### 🌐 Multi-Language Support
### 🌐 多语言支持
#### Supported via GitHub Action (Automated & Always Up-to-Date)
#### 通过 GitHub Action 支持（自动化且始终保持最新）
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](./translations/zh-HK/README.md) | [Chinese (Traditional, Macau)](./translations/zh-MO/README.md) | [Chinese (Traditional, Taiwan)](./translations/zh-TW/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Khmer](./translations/km/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/pt-BR/README.md) | [Portuguese (Portugal)](./translations/pt-PT/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md)
> **Prefer to Clone Locally?**
> **希望在本地克隆？**
>
> This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:
> 本仓库包含 50+ 种语言翻译，会显著增加下载体积。若要在克隆时排除翻译文件，请使用 sparse checkout（稀疏检出）：
>
> **Bash / macOS / Linux:**
> ```bash
@@ -33,211 +39,210 @@
> git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"
> ```
>
> This gives you everything you need to complete the course with a much faster download.
> 这样你就能获得完成课程所需的全部内容，同时下载速度会快得多。
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
#### Join Our Community
#### 加入我们的社区
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
We have a Discord learn with AI series ongoing, learn more and join us at [Learn with AI Series](https://aka.ms/learnwithai/discord) from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science.
我们正在 Discord 上举办 Learn with AI 系列学习活动，了解更多并加入我们：[Learn with AI Series](https://aka.ms/learnwithai/discord)（2025 年 9 月 18 日至 30 日）。你将获得在数据科学中使用 GitHub Copilot 的技巧与窍门。
![Learn with AI series](/images/3.png)
# Machine Learning for Beginners - A Curriculum
# 机器学习入门 — 一门课程
> 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍
> 🌍 伴随世界各地文化，一起环游地球，探索机器学习 🌍
Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Pair these lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners), as well!
Microsoft 云倡导者（Cloud Advocates）团队很高兴提供这门为期 12 周、共 26 课时的**机器学习**课程。在本课程中，你将学习有时被称为**经典机器学习（classic machine learning）**的内容，主要使用 Scikit-learn 作为库，并刻意避开深度学习；深度学习内容请参阅我们的 [AI for Beginners' curriculum](https://aka.ms/ai4beginners).。你也可以将这些课时与我们的 ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners), 搭配学习！
Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
与我们一同环游世界，将这些经典技术应用于来自全球各地的数据。每节课都包含课前与课后测验、完成课时所需的书面说明、参考答案、作业等内容。我们以项目为基础的教学法让你边做边学，这是帮助新技能真正“扎根”的经证实有效的方式。
**✍️ Hearty thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
**✍️ 衷心感谢我们的作者** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
**🎨 Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
**🎨 同时也感谢我们的插画师** Tomomi Imura, Dasani Madipalli, and Jen Looper
**🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
**🙏 特别感谢 🙏 我们的 Microsoft Student Ambassador 作者、审稿人与内容贡献者**， notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
**🤩 Extra gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!**
**🤩 额外感谢 Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta 为我们提供的 R 语言课程！**
# Getting Started
# 入门指南
Follow these steps:
1. **Fork the Repository**: Click on the "Fork" button at the top-right corner of this page.
2. **Clone the Repository**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
请按以下步骤操作：
1. **Fork 本仓库**：点击本页面右上角的 "Fork" 按钮。
2. **克隆仓库**： `git clone https://github.com/microsoft/ML-For-Beginners.git`
> [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
> [在 Microsoft Learn 合集中查找本课程的全部附加资源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
> 🔧 **Need help?** Check our [Troubleshooting Guide](TROUBLESHOOTING.md) for solutions to common issues with installation, setup, and running lessons.
> 🔧 **需要帮助？** 请查看我们的[故障排除指南](TROUBLESHOOTING.md)，获取有关安装、配置和运行课时的常见问题解决方案。
**[Students](https://aka.ms/student-page)**, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
**[学生](https://aka.ms/student-page)**,若要使用本课程，请将整个仓库 fork 到你自己的 GitHub 账户，并自行或与小组一起完成练习：
- Start with a pre-lecture quiz.
- Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `/solution` folders in each project-oriented lesson.
- Take the post-lecture quiz.
- Complete the challenge.
- Complete the assignment.
- After completing a lesson group, visit the [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.
- 从课前测验开始。
- 阅读讲义并完成活动，在每个知识点检查时暂停并反思。
- 尽量通过理解课程内容来完成项目，而不是直接运行参考答案代码；不过，在每节以项目为导向的课时中，参考答案代码可在 `/solution` 文件夹中找到。
- 完成课后测验。
- 完成挑战任务。
- 完成作业。
- 完成一组课时后，请访问[讨论区](https://github.com/microsoft/ML-For-Beginners/discussions)，通过填写相应的 PAT 评分量表来“大声学习”。'PAT' 是 Progress Assessment Tool（学习进度评估工具）的缩写，是你用来促进学习的评分量表。你也可以对其他人的 PAT 做出回应，这样我们就能一起学习。
> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules and learning paths.
> 如需进一步学习，我们建议跟随这些 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 模块和学习路径。
**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum.
**教师**朋友们，我们[提供了一些使用建议](for-teachers.md)，介绍如何使用本课程。
---
## Video walkthroughs
## 视频导览
Some of the lessons are available as short form video. You can find all these in-line in the lessons, or on the [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) by clicking the image below.
部分课程提供短视频形式。你可以在课程内联找到这些内容，也可以点击下方图片，在 [Microsoft Developer YouTube 频道上的 ML for Beginners 播放列表](https://aka.ms/ml-beginners-videos) 中查看。
[![ML for beginners banner](./images/ml-for-beginners-video-banner.png)](https://aka.ms/ml-beginners-videos)
---
## Meet the Team
## 认识团队
[![Promo video](./images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
**Gif by** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
**Gif 作者：** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
> 🎥 Click the image above for a video about the project and the folks who created it!
> 🎥 点击上方图片，观看关于本项目及其创建者的视频！
---
## Pedagogy
## 教学法
We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. In addition, this curriculum has a common **theme** to give it cohesion.
在构建本课程时，我们遵循两大教学原则：确保课程是动手实践的**项目驱动**，并包含**频繁的测验**。此外，本课程还有一个统一的**主题**，以增强整体连贯性。
By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
通过确保内容与项目相结合，学习过程对学生更具吸引力，概念记忆也会得到加强。此外，课前进行低风险的测验能让学生明确学习某一主题的意图，而课后的第二次测验则有助于进一步巩固记忆。本课程设计灵活有趣，可以完整学习，也可以部分学习。项目从小规模开始，在 12 周周期结束时逐渐变得更为复杂。本课程还包含一篇关于机器学习（ML）现实世界应用的附录，可作为额外学分或讨论基础。
> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translations](translations), and [Troubleshooting](TROUBLESHOOTING.md) guidelines. We welcome your constructive feedback!
> 请查阅我们的[行为准则](CODE_OF_CONDUCT.md)、[贡献指南](CONTRIBUTING.md)、[翻译](translations)和[故障排除](TROUBLESHOOTING.md)说明。我们欢迎你的建设性反馈！
## Each lesson includes
## 每节课包含
- optional sketchnote
- optional supplemental video
- video walkthrough (some lessons only)
- [pre-lecture warmup quiz](https://ff-quizzes.netlify.app/en/ml/)
- written lesson
- for project-based lessons, step-by-step guides on how to build the project
- knowledge checks
- a challenge
- supplemental reading
- assignment
- [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
- 可选的手绘笔记（sketchnote）
- 可选的补充视频
- 视频导览（仅部分课程）
- [课前热身测验](https://ff-quizzes.netlify.app/en/ml/)
- 书面课程
- 对于项目驱动课程，提供分步指南，介绍如何构建项目
- 知识检测
- 挑战
- 补充阅读
- 作业
- [课后测验](https://ff-quizzes.netlify.app/en/ml/)
> **A note about languages**: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the `/solution` folder and look for R lessons. They include an .rmd extension that represents an **R Markdown** file which can be simply defined as an embedding of `code chunks` (of R or other languages) and a `YAML header` (that guides how to format outputs such as PDF) in a `Markdown document`. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.
> **关于语言的说明**：这些课程主要使用 Python 编写，但许多课程也提供 R 版本。要完成 R 课程，请前往 `/solution` 文件夹并查找 R 课程。它们包含 .rmd 扩展名，表示 **R Markdown** 文件，可简单定义为在 `Markdown document` 中嵌入 `code chunks`（R 或其他语言）和 `YAML header`（用于指导如何格式化 PDF 等输出）。因此，它是数据科学的优秀创作框架，因为它允许你通过 Markdown 将代码、输出和思考结合起来。此外，R Markdown 文档可渲染为 PDF、HTML 或 Word 等输出格式。
> **A note about quizzes**: All quizzes are contained in [Quiz App folder](./quiz-app/), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder to locally host or deploy to Azure.
> **关于测验的说明**：所有测验都包含在 [Quiz App 文件夹](./quiz-app/) 中，共 52 套测验，每套 3 道题。它们从课程内链接，但测验应用可在本地运行；请按照 `quiz-app` 文件夹中的说明在本地托管或部署到 Azure。
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
| 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [Lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [Lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [Lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | [Python](2-Regression/1-Tools/README.md) • [R](2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [Python](2-Regression/2-Data/README.md) • [R](2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | [Python](2-Regression/3-Linear/README.md) • [R](2-Regression/3-Linear/solution/R/lesson_3.html) | Jen and Dmitry • Eric Wanjau |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | [Python](2-Regression/4-Logistic/README.md) • [R](2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | [Python](4-Classification/1-Introduction/README.md) • [R](4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen and Cassie • Eric Wanjau |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | [Python](4-Classification/2-Classifiers-1/README.md) • [R](4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | [Python](4-Classification/3-Classifiers-2/README.md) • [R](4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means clustering method | [Python](5-Clustering/2-K-Means/README.md) • [R](5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduction to time series forecasting | [Time series](7-TimeSeries/README.md) | Introduction to time series forecasting | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA | [Time series](7-TimeSeries/README.md) | Time series forecasting with ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ World Power Usage ⚡️ - time series forecasting with SVR | [Time series](7-TimeSeries/README.md) | Time series forecasting with Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Introduction to reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Postscript | Real-World ML scenarios and applications | [ML in the Wild](9-Real-World/README.md) | Interesting and revealing real-world applications of classical ML | [Lesson](9-Real-World/1-Applications/README.md) | Team |
| Postscript | Model Debugging in ML using RAI dashboard | [ML in the Wild](9-Real-World/README.md) | Model Debugging in Machine Learning using Responsible AI dashboard components | [Lesson](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu |
| 01 | 机器学习简介 | [Introduction](1-Introduction/README.md) | 学习机器学习背后的基本概念 | [Lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | 机器学习的历史 | [Introduction](1-Introduction/README.md) | 了解该领域的历史渊源 | [Lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | 公平性与机器学习 | [Introduction](1-Introduction/README.md) | 在构建和应用 ML 模型时，学生应考虑哪些关于公平性的重要哲学问题？ | [Lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | 机器学习技术 | [Introduction](1-Introduction/README.md) | ML 研究人员使用哪些技术来构建 ML 模型？ | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | 回归简介 | [Regression](2-Regression/README.md) | 使用 Python 和 Scikit-learn 开始构建回归模型 | [Python](2-Regression/1-Tools/README.md) • [R](2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau |
| 06 | 北美南瓜价格 🎃 | [Regression](2-Regression/README.md) | 为机器学习准备数据：可视化与清洗 | [Python](2-Regression/2-Data/README.md) • [R](2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau |
| 07 | 北美南瓜价格 🎃 | [Regression](2-Regression/README.md) | 构建线性和多项式回归模型 | [Python](2-Regression/3-Linear/README.md) • [R](2-Regression/3-Linear/solution/R/lesson_3.html) | Jen and Dmitry • Eric Wanjau |
| 08 | 北美南瓜价格 🎃 | [Regression](2-Regression/README.md) | 构建逻辑回归模型 | [Python](2-Regression/4-Logistic/README.md) • [R](2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau |
| 09 | Web 应用 🔌 | [Web App](3-Web-App/README.md) | 构建 Web 应用以使用你训练好的模型 | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | 分类简介 | [Classification](4-Classification/README.md) | 清洗、准备和可视化数据；分类入门 | [Python](4-Classification/1-Introduction/README.md) • [R](4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen and Cassie • Eric Wanjau |
| 11 | 美味的亚洲和印度美食 🍜 | [Classification](4-Classification/README.md) | 分类器入门 | [Python](4-Classification/2-Classifiers-1/README.md) • [R](4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
| 12 | 美味的亚洲和印度美食 🍜 | [Classification](4-Classification/README.md) | 更多分类器 | [Python](4-Classification/3-Classifiers-2/README.md) • [R](4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
| 13 | 美味的亚洲和印度美食 🍜 | [Classification](4-Classification/README.md) | 使用你的模型构建推荐 Web 应用 | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | 聚类简介 | [Clustering](5-Clustering/README.md) | 清洗、准备和可视化数据；聚类入门 | [Python](5-Clustering/1-Visualize/README.md) • [R](5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau |
| 15 | 探索尼日利亚音乐品味 🎧 | [Clustering](5-Clustering/README.md) | 探索 K-Means 聚类方法 | [Python](5-Clustering/2-K-Means/README.md) • [R](5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau |
| 16 | 自然语言处理（NLP）简介 ☕️ | [Natural language processing](6-NLP/README.md) | 通过构建简单机器人学习 NLP 基础知识 | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | 常见 NLP 任务 ☕️ | [Natural language processing](6-NLP/README.md) | 通过理解处理语言结构时所需的常见任务，加深 NLP 知识 | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | 翻译与情感分析 ♥️ | [Natural language processing](6-NLP/README.md) | 使用简·奥斯汀作品进行翻译和情感分析 | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | 欧洲浪漫酒店 ♥️ | [Natural language processing](6-NLP/README.md) | 基于酒店评论的情感分析 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | 欧洲浪漫酒店 ♥️ | [Natural language processing](6-NLP/README.md) | 基于酒店评论的情感分析 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | 时间序列预测简介 | [Time series](7-TimeSeries/README.md) | 时间序列预测入门 | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ 世界电力使用 ⚡️ - 使用 ARIMA 进行时间序列预测 | [Time series](7-TimeSeries/README.md) | 使用 ARIMA 进行时间序列预测 | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ 世界电力使用 ⚡️ - 使用 SVR 进行时间序列预测 | [Time series](7-TimeSeries/README.md) | 使用支持向量回归器（Support Vector Regressor）进行时间序列预测 | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | 强化学习简介 | [Reinforcement learning](8-Reinforcement/README.md) | 使用 Q-Learning 入门强化学习 | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | 帮助 Peter 躲避狼！ 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | 强化学习 Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Postscript | 现实世界 ML 场景与应用 | [ML in the Wild](9-Real-World/README.md) | 经典机器学习的有趣且富有启发性的现实世界应用 | [Lesson](9-Real-World/1-Applications/README.md) | Team |
| Postscript | 使用 RAI 仪表板进行 ML 模型调试 | [ML in the Wild](9-Real-World/README.md) | 使用负责任 AI（Responsible AI）仪表板组件进行机器学习模型调试 | [Lesson](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu |
> [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
> [在本课程的 Microsoft Learn 合集中查找所有额外资源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
## Offline access
## 离线访问
You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
你可以使用 [Docsify](https://docsify.js.org/#/). 离线运行此文档。Fork 本仓库，在本地 [安装 Docsify](https://docsify.js.org/#/quickstart)，然后在本仓库的根目录中输入 `docsify serve`。网站将在 localhost 的 3000 端口提供服务：`localhost:3000`。
## PDFs
## PDF
Find a pdf of the curriculum with links [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
可在[此处](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). 找到带链接的课程 PDF。
## 🎒 其他课程
## 🎒 Other Courses
Our team produces other courses! Check out:
我们的团队还制作了其他课程！欢迎查看：
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
[![LangChain for Beginners](https://img.shields.io/badge/LangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://github.com/microsoft/langchain-for-beginners?WT.mc_id=m365-94501-dwahlin)
[![LangChain4j 初学者](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
[![LangChain.js 初学者](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
[![LangChain 初学者](https://img.shields.io/badge/LangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://github.com/microsoft/langchain-for-beginners?WT.mc_id=m365-94501-dwahlin)
---
### Azure / Edge / MCP / Agents
[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
[![AZD 初学者](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Edge AI 初学者](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![MCP 初学者](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
[![AI Agents 初学者](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
---
### Generative AI Series
[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
### 生成式 AI 系列
[![生成式 AI 初学者](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![生成式 AI（.NET）](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
[![生成式 AI（Java）](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
[![生成式 AI（JavaScript）](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
---
### Core Learning
[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
### 核心学习
[![ML 初学者](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
[![数据科学初学者](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
[![AI 初学者](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
[![网络安全初学者](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
[![Web 开发初学者](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
[![IoT 初学者](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
[![XR 开发初学者](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
---
### Copilot Series
[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
### Copilot 系列
[![Copilot AI 结对编程](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
[![Copilot C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
[![Copilot 冒险](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
<!-- CO-OP TRANSLATOR OTHER COURSES END -->
## Getting Help
## 获取帮助
If you get stuck or have questions while learning Machine Learning or building AI applications, don't worry — help is available.
如果你在学习机器学习（Machine Learning）或构建 AI 应用时遇到困难或有疑问，别担心——你可以获得帮助。
You can join discussions with other learners and developers, ask questions, and share your ideas with the community.
你可以与其他学习者和开发者一起讨论、提问，并与社区分享你的想法。
- Join the community to ask questions and learn with others
- Discuss Machine Learning concepts and project ideas
- Get guidance from experienced developers
- 加入社区，提问并与他人一起学习
- 讨论机器学习概念和项目想法
- 向有经验的开发者寻求指导
A supportive community is a great way to grow your skills and solve problems faster.
一个互助的社区是提升技能、更快解决问题的绝佳途径。
[Microsoft Foundry Discord Community](https://discord.gg/nTYy5BXMWG)
[Microsoft Foundry Discord 社区](https://discord.gg/nTYy5BXMWG)
If you encounter bugs, errors, or have suggestions for improvements, you can also open an **Issue** in this repository to report the problem.
如果你遇到 bug、错误或有改进建议，也可以在本仓库中开启 **Issue** 来报告问题。
For product feedback or to search existing community posts, visit the Developer Forum:
如需产品反馈或搜索现有社区帖子，请访问开发者论坛：
[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
[![Microsoft Foundry 开发者论坛](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
## Additional Learning Tips
## 更多学习技巧
- Review notebooks after each lesson for better understanding.
- Practice implementing algorithms on your own.
- Explore real-world datasets using learned concepts.
- 每节课后复习 notebook，以加深理解。
- 自行练习实现算法。
- 运用所学概念探索真实世界数据集。