Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about traditional Machine Learning. In this lesson group, you will learn about what is sometimes called 'classic' ML, using primarily Scikit-Learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum.
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'.
**🙏Special thanks🙏 to our Microsoft Student Ambassador reviewers and content contributors**, notably Rishit Dagli, Rohan Raj, Muhammad Sakib Khan Inan, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum. If you would like to create your own lessons, we have also included a [lesson template](lesson-template/README.md)
- 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.
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.
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.
> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
> **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.
| 02 | [Introduction](Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Amy |
| 03 | [Introduction](Introduction/README.md) | The Ethics of Machine Learning | What are the important ethical issues that students should consider when building and applying ML models? | [lesson](Introduction/3-ethics/README.md) | Tomomi |
| 04 | Introduction to Regression | [Regression](Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](Regression/1-Tools/README.md) | Jen |
| 05 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](Regression/2-Data/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](Regression/3-Linear/README.md) | Jen |
| 07 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build a Logistic Regression model | [lesson](Regression/4-Logistic/README.md) | Jen |
| 08 | A Web App 🔌 | [Web App](Web-App/README.md) | Build a Web app to use your trained model | [lesson](Web-App/README.md) | Jen |
| 09 | Introduction to Classification | [Classification](Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](Classification/1-Data/README.md) | Cassie |
| 10 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Discriminative Model | [lesson](Classification/2-Descriminative/README.md) | Cassie |
| 11 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Generative Model | [lesson](Classification/3-Generative/README.md) | Cassie |
| 12 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Web App using your Model | [lesson](Classification/4-Applied/README.md) | Cassie |
| 13 | Introduction to Clustering | [Clustering](Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](Clustering/1-Visualize/README.md) | Jen |
| 15 | Introduction to Natural Language Processing ☕️ | [Natural Language Processing](NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 16 | Common NLP Tasks ☕️ | [Natural Language Processing](NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [lesson](NLP/2-Tasks/README.md) | Stephen |
| 17 | Translation and Sentiment Analysis ❤️ | [Natural Language Processing](NLP/README.md) | Translation and Sentiment analysis with Jane Austen | [lesson](NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | Sentiment analysis, continued | [lesson]() | Stephen |
| 20 | Introduction to Time Series Forecasting | [Time Series](TimeSeries/README.md) | Introduction to Time Series Forecasting | [lesson](TimeSeries/1-Introduction/README.md) | Francesca |
| 21 | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ | [Time Series](TimeSeries/README.md) | Time Series Forecasting with ARIMA | [lesson](TimeSeries/2-ARIMA/README.md) | Francesca |
| 24 | Real-World ML Scenarios and Applications | ML in the Wild | Interesting and Revealing real-world applications of classical ML | [lesson](Real-World/1-Applications/README.md) | Ornella |
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