Machine Learning for Beginners – A Curriculum

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

Machine Learning for Beginners: Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-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 '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, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd

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

🙏 Special thanks 🙏 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

🤩 Extra gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!

Machine Learning for Beginners: Pedagogy

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

Each lesson includes:

  • optional sketchnote
  • optional supplemental video
  • video walkthrough (some lessons only)
  • 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 languages: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the /solution folder and look for R lessons. They include an .rmd extension that represents an R Markdown file which can be simply defined as an embedding of code chunks (of R or other languages) and a YAML header (that guides how to format outputs such as PDF) in a Markdown document. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.

A note about quizzes: All quizzes are contained in this app, for 52 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 NumberTopicLesson GroupingLearning ObjectivesLinked LessonAuthor
01Introduction to machine learningIntroductionLearn the basic concepts behind machine learningLessonMuhammad
02The History of machine learningIntroductionLearn the history underlying this fieldLessonJen and Amy
03Fairness and machine learningIntroductionWhat are the important philosophical issues around fairness that students should consider when building and applying ML models?LessonTomomi
04Techniques for machine learningIntroductionWhat techniques do ML researchers use to build ML models?LessonChris and Jen
05Introduction to regressionRegressionGet started with Python and Scikit-learn for regression modelsPythonRJenEric Wanjau
06North American pumpkin prices 🎃RegressionVisualize and clean data in preparation for MLPythonRJenEric Wanjau
07North American pumpkin prices 🎃RegressionBuild linear and polynomial regression modelsPythonRJen and DmitryEric Wanjau
08North American pumpkin prices 🎃RegressionBuild a logistic regression modelPythonRJenEric Wanjau
09A Web App 🔌Web AppBuild a web app to use your trained modelPythonJen
10Introduction to classificationClassificationClean, prep, and visualize your data; introduction to classificationPythonRJen and CassieEric Wanjau
11Delicious Asian and Indian cuisines 🍜ClassificationIntroduction to classifiersPythonRJen and CassieEric Wanjau
12Delicious Asian and Indian cuisines 🍜ClassificationMore classifiersPythonRJen and CassieEric Wanjau
13Delicious Asian and Indian cuisines 🍜ClassificationBuild a recommender web app using your modelPythonJen
14Introduction to clusteringClusteringClean, prep, and visualize your data; Introduction to clusteringPythonRJenEric Wanjau
15Exploring Nigerian Musical Tastes 🎧ClusteringExplore the K-Means clustering methodPythonRJenEric Wanjau
16Introduction to natural language processing ☕️Natural language processingLearn the basics about NLP by building a simple botPythonStephen
17Common NLP Tasks ☕️Natural language processingDeepen your NLP knowledge by understanding common tasks required when dealing with language structuresPythonStephen
18Translation and sentiment analysis ♥️Natural language processingTranslation and sentiment analysis with Jane AustenPythonStephen
19Romantic hotels of Europe ♥️Natural language processingSentiment analysis with hotel reviews 1PythonStephen
20Romantic hotels of Europe ♥️Natural language processingSentiment analysis with hotel reviews 2PythonStephen
21Introduction to time series forecastingTime seriesIntroduction to time series forecastingPythonFrancesca
22⚡️ World Power Usage ⚡️ - time series forecasting with ARIMATime seriesTime series forecasting with ARIMAPythonFrancesca
23⚡️ World Power Usage ⚡️ - time series forecasting with SVRTime seriesTime series forecasting with Support Vector RegressorPythonAnirban
24Introduction to reinforcement learningReinforcement learningIntroduction to reinforcement learning with Q-LearningPythonDmitry
25Help Peter avoid the wolf! 🐺Reinforcement learningReinforcement learning GymPythonDmitry
PostscriptReal-World ML scenarios and applicationsML in the WildInteresting and revealing real-world applications of classical MLLessonTeam
PostscriptModel Debugging in ML using RAI dashboardML in the WildModel Debugging in Machine Learning using Responsible AI dashboard componentsLessonRuth Yakubu

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.