cocoa-chen / ML-For-Beginners

12 weeks, 24 lessons, classic Machine Learning for all

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Machine Learning for Beginners - A Curriculum

🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our forthcoming 'Data Science for Beginners' curriculum, as well!

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 Soshnikov, Chris Noring, Ornella Altunyan, and Amy Boyd

🎨 Thanks as well to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper

πŸ™ Special thanks πŸ™ to our Microsoft Student Ambassador authors, reviewers and content contributors, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal

Getting Started

Students, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:

  • Start with a pre-lecture quiz
  • Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
  • 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.
  • Take the post-lecture quiz
  • Complete the challenge
  • Complete the assignment
  • After completing a lesson group, visit the Discussion board and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.

For further study, we recommend following these Microsoft Learn modules and learning paths.

Teachers, we have included some suggestions on how to use this curriculum.

Meet the Team

Promo video

πŸŽ₯ Click the image above for a video about the project and the folks who created it!


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. 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, Contributing, and Translation guidelines. We welcome your constructive feedback!

Each lesson includes:

  • optional sketchnote
  • optional supplemental video
  • pre-lecture warmup quiz
  • written lesson
  • for project-based lessons, step-by-step guides on how to build the project
  • knowledge checks
  • a challenge
  • supplemental reading
  • assignment
  • post-lecture quiz

A note about quizzes: All quizzes are contained in this app, 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 Learn the basic concepts behind machine learning lesson Muhammad
02 The History of machine learning Introduction Learn the history underlying this field lesson Jen and Amy
03 Fairness and machine learning Introduction What are the important philosophical issues around fairness that students should consider when building and applying ML models? lesson Tomomi
04 Techniques for machine learning Introduction What techniques do ML researchers use to build ML models? lesson Chris and Jen
05 Introduction to regression Regression Get started with Python and Scikit-learn for regression models lesson Jen
06 North American pumpkin prices πŸŽƒ Regression Visualize and clean data in preparation for ML lesson Jen
07 North American pumpkin prices πŸŽƒ Regression Build linear and polynomial regression models lesson Jen
08 North American pumpkin prices πŸŽƒ Regression Build a logistic regression model lesson Jen
09 A Web App πŸ”Œ Web App Build a web app to use your trained model lesson Jen
10 Introduction to classification Classification Clean, prep, and visualize your data; introduction to classification lesson Jen and Cassie
11 Delicious Asian and Indian cuisines 🍜 Classification Introduction to classifiers lesson Jen and Cassie
12 Delicious Asian and Indian cuisines 🍜 Classification More classifiers lesson Jen and Cassie
13 Delicious Asian and Indian cuisines 🍜 Classification Build a recommender web app using your model lesson Jen
14 Introduction to clustering Clustering Clean, prep, and visualize your data; Introduction to clustering lesson Jen
15 Exploring Nigerian Musical Tastes 🎧 Clustering Explore the K-Means clustering method lesson Jen
16 Introduction to natural language processing β˜•οΈ Natural language processing Learn the basics about NLP by building a simple bot lesson Stephen
17 Common NLP Tasks β˜•οΈ Natural language processing Deepen your NLP knowledge by understanding common tasks required when dealing with language structures lesson Stephen
18 Translation and sentiment analysis β™₯️ Natural language processing Translation and sentiment analysis with Jane Austen lesson Stephen
19 Romantic hotels of Europe β™₯️ Natural language processing Sentiment analysis with hotel reviews, 1 lesson Stephen
20 Romantic hotels of Europe β™₯️ Natural language processing Sentiment analysis with hotel reviews 2 lesson Stephen
21 Introduction to time series forecasting Time series Introduction to time series forecasting lesson Francesca
22 ⚑️ World Power Usage ⚑️ - time series forecasting with ARIMA Time series Time series forecasting with ARIMA lesson Francesca
23 Introduction to reinforcement learning Reinforcement learning Introduction to reinforcement learning with Q-Learning lesson Dmitry
24 Help Peter avoid the wolf! 🐺 Reinforcement learning Reinforcement learning Gym lesson Dmitry
Postscript Real-World ML scenarios and applications ML in the Wild Interesting and revealing real-world applications of classical ML lesson Team

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify 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.


Find a pdf of the curriculum with links here

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Other Curricula

Our team produces other curricula! Check out:


12 weeks, 24 lessons, classic Machine Learning for all

License:MIT License


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