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@ -73,35 +73,35 @@ By ensuring that the content aligns with projects, the process is made more enga
- assignment
- post-lecture quiz
> **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
| 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [lesson](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | [lesson](4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | [lesson](4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [lesson](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | [lesson](5-Clustering/1-Visualize/README.md) | Jen |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means clustering method | [lesson](5-Clustering/2-K-Means/README.md) | Jen |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [lesson](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [lesson](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 1 | [lesson](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 2 | [lesson](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduction to time series forecasting | [Time series](7-TimeSeries/README.md) | Introduction to time series forecasting | [lesson](7-TimeSeries/1-Introduction/README.md) | Francesca |
> **A note about quizzes**: All quizzes are contained [in this app](https://white-water-09ec41f0f.azurestaticapps.net/), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
| 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | [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 |
| 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

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- 課題
- 講義後の小テスト
> **小テストに関する注意**: すべての小テストは [このアプリ](https://jolly-sea-0a877260f.azurestaticapps.net) に含まれており、各3問からなる50個の小テストがあります。これらはレッスン内からリンクされていますが、アプリをローカルで実行することもできます。`quiz-app` フォルダ内の指示に従ってください。
> **小テストに関する注意**: すべての小テストは [このアプリ](https://white-water-09ec41f0f.azurestaticapps.net/) に含まれており、各3問からなる50個の小テストがあります。これらはレッスン内からリンクされていますが、アプリをローカルで実行することもできます。`quiz-app` フォルダ内の指示に従ってください。
| レッスン番号 | トピック | レッスングループ | 学習の目的 | 関連するレッスン | 著者 |
| :----------: | :------------------------------------------: | :----------------------------------------------------: | ------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------: | :------------: |

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- tarefa
- teste pós-aula
> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://jolly-sea-0a877260f.azurestaticapps.net), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://white-water-09ec41f0f.azurestaticapps.net/), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
| Número de aula | Tópico | Agrupamento de Aulas | Objetivos de aprendizagem | Aula vinculada | Autor |
| :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |

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- ödev
- ders sonrası kısa sınavı
> **Kısa sınavlar hakkında bir not**: Her biri üç sorudan oluşan ve toplamda 50 tane olan tüm kısa sınavlar [bu uygulamada](https://jolly-sea-0a877260f.azurestaticapps.net) bulunmaktadır. Derslerin içinden de bağlantı yoluyla ulaşılabilirler ancak kısa sınav uygulaması yerelde çalıştırılabilir; `quiz-app` klasöründeki yönergeleri takip edin.
> **Kısa sınavlar hakkında bir not**: Her biri üç sorudan oluşan ve toplamda 50 tane olan tüm kısa sınavlar [bu uygulamada](https://white-water-09ec41f0f.azurestaticapps.net/) bulunmaktadır. Derslerin içinden de bağlantı yoluyla ulaşılabilirler ancak kısa sınav uygulaması yerelde çalıştırılabilir; `quiz-app` klasöründeki yönergeleri takip edin.
| Ders Numarası | Konu | Ders Gruplandırması | Öğrenme Hedefleri | Ders | Yazar |

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- 作业
- 课后测验
> **关于测验**：所有的测验都在[这个应用里](https://jolly-sea-0a877260f.azurestaticapps.net)，总共 50 个测验，每个测验三个问题。它们的链接在每节课中，而且这个测验应用可以在本地运行。请参考 `quiz-app` 文件夹中的指南。
| 课程编号 | 主体 | 课程组 | 学习目标 | 课程链接 | 作者 |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
| 01 | 机器学习简介 | [简介](../1-Introduction/README.md) | 了解机器学习背后的基本概念 | [课程](../1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | 机器学习的历史 | [简介](../1-Introduction/README.md) | 了解该领域的历史 | [课程](../1-Introduction/2-history-of-ML/README.md) | Jen 和 Amy |
| 03 | 机器学习与公平 | [简介](../1-Introduction/README.md) | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的重要哲学问题？ | [课程](../1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | 机器学习的技术工具 | [简介](../1-Introduction/README.md) | 机器学习研究者使用哪些技术来构建机器学习模型？ | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen |
| 05 | 回归简介 | [回归](../2-Regression/README.md) | 开始使用 Python 和 Scikit-learn 构建回归模型 | [课程](../2-Regression/1-Tools/README.md) | Jen |
| 06 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 可视化、进行数据清理，为机器学习做准备 | [课程](../2-Regression/2-Data/README.md) | Jen |
| 07 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 建立线性和多项式回归模型 | [课程](../2-Regression/3-Linear/README.md) | Jen |
| 08 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 构建逻辑回归模型 | [课程](../2-Regression/4-Logistic/README.md) | Jen |
| 09 | 一个网页应用 🔌 | [网页应用](../3-Web-App/README.md) | 构建一个 Web 应用程序以使用经过训练的模型 | [课程](../3-Web-App/1-Web-App/README.md) | Jen |
| 10 | 分类简介 | [分类](../4-Classification/README.md) | 清理、准备和可视化数据； 分类简介 | [课程](../4-Classification/1-Introduction/README.md) | Jen 和 Cassie |
| 11 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 分类器简介 | [课程](../4-Classification/2-Classifiers-1/README.md) | Jen 和 Cassie |
| 12 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 关于分类器的更多内容 | [课程](../4-Classification/3-Classifiers-2/README.md) | Jen 和 Cassie |
| 13 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 使用您的模型构建一个可以「推荐」的 Web 应用 | [课程](../4-Classification/4-Applied/README.md) | Jen |
| 14 | 聚类简介 | [聚类](../5-Clustering/README.md) | 清理、准备和可视化数据； 聚类简介 | [课程](../5-Clustering/1-Visualize/README.md) | Jen |
| 15 | 探索尼日利亚人的音乐品味 🎧 | [聚类](../5-Clustering/README.md) | 探索 K-Means 聚类方法 | [课程](../5-Clustering/2-K-Means/README.md) | Jen |
| 16 | 自然语言处理 (NLP) 简介 ☕️ | [自然语言处理](../6-NLP/README.md) | 通过构建一个简单的 bot (机器人) 来了解 NLP 的基础知识 | [课程](../6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | 常见的 NLP 任务 ☕️ | [自然语言处理](../6-NLP/README.md) | 通过理解处理语言结构时所需的常见任务来加深对于自然语言处理 (NLP) 的理解 | [课程](../6-NLP/2-Tasks/README.md) | Stephen |
| 18 | 翻译和情感分析 ♥️ | [自然语言处理](../6-NLP/README.md) | 对简·奥斯汀的文本进行翻译和情感分析 | [课程](../6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](../6-NLP/README.md) | 对于酒店评价进行情感分析（上） | [课程](../6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](../6-NLP/README.md) | 对于酒店评价进行情感分析（下） | [课程](../6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | 时间序列预测简介 | [时间序列](../7-TimeSeries/README.md) | 时间序列预测简介 forecasting | [课程](../7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ 世界用电量 ⚡️ - 使用 ARIMA 进行时间序列预测 | [时间序列](../7-TimeSeries/README.md) | 使用 ARIMA 进行时间序列预测 | [课程](../7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | 强化学习简介 | [强化学习](../8-Reinforcement/README.md) | Q-Learning 强化学习简介 | [课程](../8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | 帮助 Peter 避开狼！🐺 | [强化学习](../8-Reinforcement/README.md) | 强化学习练习 | [课程](../8-Reinforcement/2-Gym/README.md) | Dmitry |
| 后记 | 现实世界中的机器学习场景和应用 | [自然场景下的机器学习](../9-Real-World/README.md) | 探索有趣的经典机器学习方法，了解现实世界中机器学习的应用 | [课程](../9-Real-World/1-Applications/README.md) | 团队 |
> **关于测验**：所有的测验都在[这个应用里](https://white-water-09ec41f0f.azurestaticapps.net/)，总共 50 个测验，每个测验三个问题。它们的链接在每节课中，而且这个测验应用可以在本地运行。请参考 `quiz-app` 文件夹中的指南。
| 课程编号 | 主体 | 课程组 | 学习目标 | 课程链接 | 作者 |
| :------: | :------------------------------------------: | :-----------------------------------------------: | ----------------------------------------------------------------------- | :----------------------------------------------------: | :-----------: |
| 01 | 机器学习简介 | [简介](../1-Introduction/README.md) | 了解机器学习背后的基本概念 | [课程](../1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | 机器学习的历史 | [简介](../1-Introduction/README.md) | 了解该领域的历史 | [课程](../1-Introduction/2-history-of-ML/README.md) | Jen 和 Amy |
| 03 | 机器学习与公平 | [简介](../1-Introduction/README.md) | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的重要哲学问题？ | [课程](../1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | 机器学习的技术工具 | [简介](../1-Introduction/README.md) | 机器学习研究者使用哪些技术来构建机器学习模型？ | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen |
| 05 | 回归简介 | [回归](../2-Regression/README.md) | 开始使用 Python 和 Scikit-learn 构建回归模型 | [课程](../2-Regression/1-Tools/README.md) | Jen |
| 06 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 可视化、进行数据清理，为机器学习做准备 | [课程](../2-Regression/2-Data/README.md) | Jen |
| 07 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 建立线性和多项式回归模型 | [课程](../2-Regression/3-Linear/README.md) | Jen |
| 08 | 北美南瓜价格 🎃 | [回归](../2-Regression/README.md) | 构建逻辑回归模型 | [课程](../2-Regression/4-Logistic/README.md) | Jen |
| 09 | 一个网页应用 🔌 | [网页应用](../3-Web-App/README.md) | 构建一个 Web 应用程序以使用经过训练的模型 | [课程](../3-Web-App/1-Web-App/README.md) | Jen |
| 10 | 分类简介 | [分类](../4-Classification/README.md) | 清理、准备和可视化数据； 分类简介 | [课程](../4-Classification/1-Introduction/README.md) | Jen 和 Cassie |
| 11 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 分类器简介 | [课程](../4-Classification/2-Classifiers-1/README.md) | Jen 和 Cassie |
| 12 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 关于分类器的更多内容 | [课程](../4-Classification/3-Classifiers-2/README.md) | Jen 和 Cassie |
| 13 | 美味的亚洲和印度美食 🍜 | [分类](../4-Classification/README.md) | 使用您的模型构建一个可以「推荐」的 Web 应用 | [课程](../4-Classification/4-Applied/README.md) | Jen |
| 14 | 聚类简介 | [聚类](../5-Clustering/README.md) | 清理、准备和可视化数据； 聚类简介 | [课程](../5-Clustering/1-Visualize/README.md) | Jen |
| 15 | 探索尼日利亚人的音乐品味 🎧 | [聚类](../5-Clustering/README.md) | 探索 K-Means 聚类方法 | [课程](../5-Clustering/2-K-Means/README.md) | Jen |
| 16 | 自然语言处理 (NLP) 简介 ☕️ | [自然语言处理](../6-NLP/README.md) | 通过构建一个简单的 bot (机器人) 来了解 NLP 的基础知识 | [课程](../6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | 常见的 NLP 任务 ☕️ | [自然语言处理](../6-NLP/README.md) | 通过理解处理语言结构时所需的常见任务来加深对于自然语言处理 (NLP) 的理解 | [课程](../6-NLP/2-Tasks/README.md) | Stephen |
| 18 | 翻译和情感分析 ♥️ | [自然语言处理](../6-NLP/README.md) | 对简·奥斯汀的文本进行翻译和情感分析 | [课程](../6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](../6-NLP/README.md) | 对于酒店评价进行情感分析（上） | [课程](../6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | 欧洲的浪漫酒店 ♥️ | [自然语言处理](../6-NLP/README.md) | 对于酒店评价进行情感分析（下） | [课程](../6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | 时间序列预测简介 | [时间序列](../7-TimeSeries/README.md) | 时间序列预测简介 forecasting | [课程](../7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ 世界用电量 ⚡️ - 使用 ARIMA 进行时间序列预测 | [时间序列](../7-TimeSeries/README.md) | 使用 ARIMA 进行时间序列预测 | [课程](../7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | 强化学习简介 | [强化学习](../8-Reinforcement/README.md) | Q-Learning 强化学习简介 | [课程](../8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | 帮助 Peter 避开狼！🐺 | [强化学习](../8-Reinforcement/README.md) | 强化学习练习 | [课程](../8-Reinforcement/2-Gym/README.md) | Dmitry |
| 后记 | 现实世界中的机器学习场景和应用 | [自然场景下的机器学习](../9-Real-World/README.md) | 探索有趣的经典机器学习方法，了解现实世界中机器学习的应用 | [课程](../9-Real-World/1-Applications/README.md) | 团队 |
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