@ -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'.
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
**🎨 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.
> **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.
| 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 |
| 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 |
| 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 |
| 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 |
| 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 | 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 |
| 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) | Build Linear and Polynomial Regression models | [lesson](2-Regression/3-Linear/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 a Logistic Regression model | [lesson](2-Regression/4-Logistic/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 | 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 |
| 08 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/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 |
| 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 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | Build a Discriminative Model | [lesson](4-Classification/2-Descriminative/README.md) | Cassie |
| 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) | Build a Generative Model | [lesson](4-Classification/3-Generative/README.md) | 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) | Build a Web App using your Model | [lesson](4-Classification/4-Applied/README.md) | Jen |
| 12 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | More Classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Cassie |
| 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 |
| 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 | 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 |
| 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 | 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 |
| 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 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](6-NLP/README.md) | Sentiment analysis, continued | [lesson]() | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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
## 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`.
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`.
Jen Looper
3 years ago