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@ -16,7 +16,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson cur
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'.
**✍️ Hearty thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshkinov, Ornella Altunyan, Amy Boyd
**✍️ Hearty thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshkinov, Chris Noring, Ornella Altunyan, and Amy Boyd
**🎨 Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
@ -69,32 +69,32 @@ By ensuring that the content aligns with projects, the process is made more enga
> **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 48 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 | Section | Concepts Taught | Learning Objectives | Linked Lesson | Author |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :---------: |
| 01 | [Introduction](1-Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | [Introduction](1-Introduction/README.md) | Fairness and Machine Learning | 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 | 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 |
| 05 | 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 |
| 06 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 07 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 08 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a Web app to use your trained model | [lesson](3-Web-App/README.md) | Jen |
| 09 | Introduction to Classification | [Classification](4-Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](4-Classification/1-Introduction/README.md) | Cassie |
| 10 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | Build a Discriminative Model | [lesson](4-Classification/2-Descriminative/README.md) | Cassie |
| 11 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | Build a Generative Model | [lesson](4-Classification/3-Generative/README.md) | Cassie |
| 12 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | Build a Web App using your Model | [lesson](4-Classification/4-Applied/README.md) | Jen |
| 13 | 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 |
| 14 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means Clustering Method | [lesson](5-Clustering/2-K-Means/README.md) | Jen |
| 15 | 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 |
| 16 | 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 |
| 17 | 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 |
| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](6-NLP/README.md) | Sentiment analysis, continued | [lesson]() | Stephen |
| 19 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](6-NLP/README.md) | Sentiment analysis, continued | [lesson]() | Stephen |
| 20 | Introduction to Time Series Forecasting | [Time Series](7-TimeSeries/README.md) | Introduction to Time Series Forecasting | [lesson](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 21 | ⚡️ 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 |
| 22 | 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 |
| 23 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning](8-Reinforcement/README.md) | Reinforcement Learning Gym | [lesson](8-Reinforcement/2-Gym/README.md) | Dmitry |
| 24 | 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 |
| Lesson Number | Section | Concepts Taught | Learning Objectives | Linked Lesson | Author |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
| 01 | [Introduction](1-Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | [Introduction](1-Introduction/README.md) | Fairness and Machine Learning | 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 | [Introduction](1-Introduction/README.md) | Techniques for Machine Learning | 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/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) | Cassie and Jen |
| 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) | 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, continued | [lesson]() | Stephen |
| 20 | Introduction to Time Series Forecasting | [Time Series](7-TimeSeries/README.md) | Introduction to Time Series Forecasting | [lesson](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 21 | ⚡️ 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 |
| 22 | 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 |
| 23 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning](8-Reinforcement/README.md) | Reinforcement Learning Gym | [lesson](8-Reinforcement/2-Gym/README.md) | Dmitry |
| 24 | 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`.

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