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@ -2,7 +2,7 @@
[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](../http://makeapullrequest.com)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
@ -37,13 +37,13 @@ Travel with us around the world as we apply these classic techniques to data fro
> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
**Teachers**, we have [included some suggestions](../for-teachers.md) on how to use this curriculum.
**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum.
---
## Meet the Team
[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
> 🎥 Click the image above for a video about the project and the folks who created it!
@ -54,7 +54,7 @@ We have chosen two pedagogical tenets while building this curriculum: ensuring t
By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
> Find our [Code of Conduct](../CODE_OF_CONDUCT.md), [Contributing](../CONTRIBUTING.md), and [Translation](../TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
## Each lesson includes:
- optional sketchnote
@ -73,42 +73,42 @@ By ensuring that the content aligns with projects, the process is made more enga
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
| 01 | Introduction to machine learning | [Introduction](../1-Introduction/README.md) | Learn the basic concepts behind machine learning | [lesson](../1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](../1-Introduction/README.md) | Learn the history underlying this field | [lesson](../1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](../1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](../1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](../1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [lesson](../1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](../2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | [lesson](../2-Regression/1-Tools/README.md) | Jen |
| 06 | North American pumpkin prices 🎃 | [Regression](../2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](../2-Regression/2-Data/README.md) | Jen |
| 07 | North American pumpkin prices 🎃 | [Regression](../2-Regression/README.md) | Build linear and polynomial regression models | [lesson](../2-Regression/3-Linear/README.md) | Jen |
| 08 | North American pumpkin prices 🎃 | [Regression](../2-Regression/README.md) | Build a logistic regression model | [lesson](../2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](../3-Web-App/README.md) | Build a web app to use your trained model | [lesson](../3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](../4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | [lesson](../4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](../4-Classification/README.md) | Introduction to classifiers | [lesson](../4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](../4-Classification/README.md) | More classifiers | [lesson](../4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](../4-Classification/README.md) | Build a recommender web app using your model | [lesson](../4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](../5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | [lesson](../5-Clustering/1-Visualize/README.md) | Jen |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](../5-Clustering/README.md) | Explore the K-Means clustering method | [lesson](../5-Clustering/2-K-Means/README.md) | Jen |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](../6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](../6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](../6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [lesson](../6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](../6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [lesson](../6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](../6-NLP/README.md) | Sentiment analysis with hotel reviews, 1 | [lesson](../6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantic hotels of Europe ♥️ | [Natural language processing](../6-NLP/README.md) | Sentiment analysis with hotel reviews 2 | [lesson](../6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduction to time series forecasting | [Time series](../7-TimeSeries/README.md) | Introduction to time series forecasting | [lesson](../7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA | [Time series](../7-TimeSeries/README.md) | Time series forecasting with ARIMA | [lesson](../7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | Introduction to reinforcement learning | [Reinforcement learning](../8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [lesson](../8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](../8-Reinforcement/README.md) | Reinforcement learning Gym | [lesson](../8-Reinforcement/2-Gym/README.md) | Dmitry |
| Postscript | Real-World ML scenarios and applications | [ML in the Wild](../9-Real-World/README.md) | Interesting and revealing real-world applications of classical ML | [lesson](../9-Real-World/1-Applications/README.md) | Team |
| 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [lesson](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | [lesson](4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | [lesson](4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [lesson](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | [lesson](5-Clustering/1-Visualize/README.md) | Jen |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means clustering method | [lesson](5-Clustering/2-K-Means/README.md) | Jen |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [lesson](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [lesson](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews, 1 | [lesson](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 2 | [lesson](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduction to time series forecasting | [Time series](7-TimeSeries/README.md) | Introduction to time series forecasting | [lesson](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA | [Time series](7-TimeSeries/README.md) | Time series forecasting with ARIMA | [lesson](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | Introduction to reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [lesson](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [lesson](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Postscript | Real-World ML scenarios and applications | [ML in the Wild](9-Real-World/README.md) | Interesting and revealing real-world applications of classical ML | [lesson](9-Real-World/1-Applications/README.md) | Team |
## Offline access
You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
## PDFs
Find a pdf of the curriculum with links [here](../pdf/readme.pdf)
Find a pdf of the curriculum with links [here](pdf/readme.pdf)
## Help Wanted!
Would you like to contribute a translation? Please read our [translation guidelines](../TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
## Other Curricula

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[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](../http://makeapullrequest.com)
[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
@ -37,13 +37,13 @@
> 如果希望进一步学习，我们推荐跟随 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 的模块和学习路径。
**对于老师们**，我们对于如何使用这套教程[提供了一些建议](for-teachers.md)。
**对于老师们**，我们对于如何使用这套教程[提供了一些建议](../for-teachers.md)。
---
## 项目团队
[![宣传视频](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "宣传视频")
[![宣传视频](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "宣传视频")
> 🎥 点击上方的图片，来观看一个关于这个项目和它的创造者们的视频！
@ -54,7 +54,7 @@
通过确保课程内容与项目强相关，我们让学习过程对学生更具吸引力，概念的学习也被深化了。难度较低的课前测验可以吸引学生学习课程，课后的第二次测验进一步重复了课堂中的概念。该课程被设计地灵活有趣，可以一次性全部学习，或者分开来一部分一部分学习。这些项目由浅入深，从第一周的的小项目开始，在第十二周的周期结束时变得较为复杂。本课程还包括一个关于机器学习实际应用的后记，可用作额外学分或讨论的基础。
> 在这里，你可以找到我们的[行为守则](CODE_OF_CONDUCT.md)，[对项目作出贡献](CONTRIBUTING.md)以及[翻译](TRANSLATIONS.md)指南。我们欢迎各位提出有建设性的反馈！
> 在这里，你可以找到我们的[行为守则](../CODE_OF_CONDUCT.md)，[对项目作出贡献](../CONTRIBUTING.md)以及[翻译](../TRANSLATIONS.md)指南。我们欢迎各位提出有建设性的反馈！
## 每一节课都包含：
@ -74,41 +74,41 @@
| 课程编号 | 主体 | 课程组 | 学习目标 | 课程链接 | 作者 |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
| 01 | 机器学习简介 | [简介](1-Introduction/README.md) | 了解机器学习背后的基本概念 | [课程](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | 机器学习的历史 | [简介](1-Introduction/README.md) | 了解该领域的历史 | [课程](1-Introduction/2-history-of-ML/README.md) | Jen 和 Amy |
| 03 | 机器学习与公平 | [简介](1-Introduction/README.md) | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的重要哲学问题？ | [课程](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | 机器学习的技术工具 | [简介](1-Introduction/README.md) | 机器学习研究者使用哪些技术来构建机器学习模型？ | [课程](1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen |
| 05 | 回归简介 | [回归](2-Regression/README.md) | 开始使用 Python 和 Scikit-learn 构建回归模型 | [课程](2-Regression/1-Tools/README.md) | Jen |
| 06 | 北美南瓜价格 🎃 | [回归](2-Regression/README.md) | 可视化、进行数据清理，为机器学习做准备 | [课程](2-Regression/2-Data/README.md) | Jen |
| 07 | 北美南瓜价格 🎃 | [回归](2-Regression/README.md) | 建立线性和多项式回归模型 | [课程](2-Regression/3-Linear/README.md) | Jen |
| 08 | 北美南瓜价格 🎃 | [回归](2-Regression/README.md) | 构建逻辑回归模型 | [课程](2-Regression/4-Logistic/README.md) | Jen |
| 09 | 一个网页应用 🔌 | [网页应用](3-Web-App/README.md) | 构建一个 Web 应用程序以使用经过训练的模型 | [课程](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | 分类简介 | [分类](4-Classification/README.md) | 清理、准备和可视化数据； 分类简介 | [课程](4-Classification/1-Introduction/README.md) | Jen 和 Cassie |
| 11 | 美味的亚洲和印度美食 🍜 | [分类](4-Classification/README.md) | 分类器简介 | [课程](4-Classification/2-Classifiers-1/README.md) | Jen 和 Cassie |
| 12 | 美味的亚洲和印度美食 🍜 | [分类](4-Classification/README.md) | 关于分类器的更多内容 | [课程](4-Classification/3-Classifiers-2/README.md) | Jen 和 Cassie |
| 13 | 美味的亚洲和印度美食 🍜 | [分类](4-Classification/README.md) | 使用您的模型构建一个可以「推荐」的 Web 应用 | [课程](4-Classification/4-Applied/README.md) | Jen |
| 14 | 聚类简介 | [聚类](5-Clustering/README.md) | 清理、准备和可视化数据； 聚类简介 | [课程](5-Clustering/1-Visualize/README.md) | Jen |
| 15 | 探索尼日利亚人的音乐品味 🎧 | [聚类](5-Clustering/README.md) | 探索 K-Means 聚类方法 | [课程](5-Clustering/2-K-Means/README.md) | Jen |
| 16 | 自然语言处理 (NLP) 简介 ☕️ | [自然语言处理](6-NLP/README.md) | 通过构建一个简单的 bot (机器人) 来了解 NLP 的基础知识 | [课程](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | 常见的 NLP 任务 ☕️ | [自然语言处理](6-NLP/README.md) | 通过理解处理语言结构时所需的常见任务来加深对于自然语言处理 (NLP) 的理解 | [课程](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | 翻译和情感分析 ♥️ | [自然语言处理](6-NLP/README.md) | 对简·奥斯汀的文本进行翻译和情感分析 | [课程](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](6-NLP/README.md) | 对于酒店评价进行情感分析（上） | [课程](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](6-NLP/README.md) | 对于酒店评价进行情感分析（下） | [课程](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | 时间序列预测简介 | [时间序列](7-TimeSeries/README.md) | 时间序列预测简介 forecasting | [课程](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ 世界用电量 ⚡️ - 使用 ARIMA 进行时间序列预测 | [时间序列](7-TimeSeries/README.md) | 使用 ARIMA 进行时间序列预测 | [课程](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | 强化学习简介 | [强化学习](8-Reinforcement/README.md) | Q-Learning 强化学习简介 | [课程](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | 帮助 Peter 避开狼！🐺 | [强化学习](8-Reinforcement/README.md) | 强化学习练习 | [课程](8-Reinforcement/2-Gym/README.md) | Dmitry |
| 后记 | 现实世界中的机器学习场景和应用 | [自然场景下的机器学习](9-Real-World/README.md) | 探索有趣的经典机器学习方法，了解现实世界中机器学习的应用 | [课程](9-Real-World/1-Applications/README.md) | 团队 |
| 01 | 机器学习简介 | [简介](../1-Introduction/README.md) | 了解机器学习背后的基本概念 | [课程](../1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | 机器学习的历史 | [简介](../1-Introduction/README.md) | 了解该领域的历史 | [课程](../1-Introduction/2-history-of-ML/README.md) | Jen 和 Amy |
| 03 | 机器学习与公平 | [简介](../1-Introduction/README.md) | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的重要哲学问题？ | [课程](../1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | 机器学习的技术工具 | [简介](../1-Introduction/README.md) | 机器学习研究者使用哪些技术来构建机器学习模型？ | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen |
| 05 | 回归简介 | [回归](../2-Regression/README.md) | 开始使用 Python 和 Scikit-learn 构建回归模型 | [课程](../2-Regression/1-Tools/README.md) | Jen |
| 06 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 可视化、进行数据清理，为机器学习做准备 | [课程](../2-Regression/2-Data/README.md) | Jen |
| 07 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 建立线性和多项式回归模型 | [课程](../2-Regression/3-Linear/README.md) | Jen |
| 08 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 构建逻辑回归模型 | [课程](../2-Regression/4-Logistic/README.md) | Jen |
| 09 | 一个网页应用 🔌 | [网页应用](../3-Web-App/README.md) | 构建一个 Web 应用程序以使用经过训练的模型 | [课程](../3-Web-App/1-Web-App/README.md) | Jen |
| 10 | 分类简介 | [分类](../4-Classification/README.md) | 清理、准备和可视化数据； 分类简介 | [课程](../4-Classification/1-Introduction/README.md) | Jen 和 Cassie |
| 11 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 分类器简介 | [课程](../4-Classification/2-Classifiers-1/README.md) | Jen 和 Cassie |
| 12 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 关于分类器的更多内容 | [课程](../4-Classification/3-Classifiers-2/README.md) | Jen 和 Cassie |
| 13 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 使用您的模型构建一个可以「推荐」的 Web 应用 | [课程](../4-Classification/4-Applied/README.md) | Jen |
| 14 | 聚类简介 | [聚类](../5-Clustering/README.md) | 清理、准备和可视化数据； 聚类简介 | [课程](../5-Clustering/1-Visualize/README.md) | Jen |
| 15 | 探索尼日利亚人的音乐品味 🎧 | [聚类](../5-Clustering/README.md) | 探索 K-Means 聚类方法 | [课程](../5-Clustering/2-K-Means/README.md) | Jen |
| 16 | 自然语言处理 (NLP) 简介 ☕️ | [自然语言处理](../6-NLP/README.md) | 通过构建一个简单的 bot (机器人) 来了解 NLP 的基础知识 | [课程](../6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | 常见的 NLP 任务 ☕️ | [自然语言处理](../6-NLP/README.md) | 通过理解处理语言结构时所需的常见任务来加深对于自然语言处理 (NLP) 的理解 | [课程](../6-NLP/2-Tasks/README.md) | Stephen |
| 18 | 翻译和情感分析 ♥️ | [自然语言处理](../6-NLP/README.md) | 对简·奥斯汀的文本进行翻译和情感分析 | [课程](../6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](../6-NLP/README.md) | 对于酒店评价进行情感分析（上） | [课程](../6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](../6-NLP/README.md) | 对于酒店评价进行情感分析（下） | [课程](../6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | 时间序列预测简介 | [时间序列](../7-TimeSeries/README.md) | 时间序列预测简介 forecasting | [课程](../7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ 世界用电量 ⚡️ - 使用 ARIMA 进行时间序列预测 | [时间序列](../7-TimeSeries/README.md) | 使用 ARIMA 进行时间序列预测 | [课程](../7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | 强化学习简介 | [强化学习](../8-Reinforcement/README.md) | Q-Learning 强化学习简介 | [课程](../8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | 帮助 Peter 避开狼！🐺 | [强化学习](../8-Reinforcement/README.md) | 强化学习练习 | [课程](../8-Reinforcement/2-Gym/README.md) | Dmitry |
| 后记 | 现实世界中的机器学习场景和应用 | [自然场景下的机器学习](../9-Real-World/README.md) | 探索有趣的经典机器学习方法，了解现实世界中机器学习的应用 | [课程](../9-Real-World/1-Applications/README.md) | 团队 |
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## PDF 文档们
点击[这里](pdf/readme.pdf)查找课程的 PDF 文档们。
点击[这里](../pdf/readme.pdf)查找课程的 PDF 文档们。
## 需要你的帮助！
想贡献一份翻译吗？请阅读我们的[翻译指南](TRANSLATIONS.md)并在[此处](https://github.com/microsoft/ML-For-Beginners/issues/71)添加你的意见。
想贡献一份翻译吗？请阅读我们的[翻译指南](../TRANSLATIONS.md)并在[此处](https://github.com/microsoft/ML-For-Beginners/issues/71)添加你的意见。
## 其他课程