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, 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, 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 Ambassador Eric Wanjau for our R lessons!

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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.

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Meet the Team

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

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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 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 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 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	Python R	Jen Eric Wanjau
06	North American pumpkin prices 🎃	Regression	Visualize and clean data in preparation for ML	Python R	Jen Eric Wanjau
07	North American pumpkin prices 🎃	Regression	Build linear and polynomial regression models	Python R	Jen Eric Wanjau
08	North American pumpkin prices 🎃	Regression	Build a logistic regression model	Python R	Jen Eric Wanjau
09	A Web App 🔌	Web App	Build a web app to use your trained model	Python	Jen
10	Introduction to classification	Classification	Clean, prep, and visualize your data; introduction to classification	Python R	Jen and Cassie Eric Wanjau
11	Delicious Asian and Indian cuisines 🍜	Classification	Introduction to classifiers	Python R	Jen and Cassie Eric Wanjau
12	Delicious Asian and Indian cuisines 🍜	Classification	More classifiers	Python R	Jen and Cassie Eric Wanjau
13	Delicious Asian and Indian cuisines 🍜	Classification	Build a recommender web app using your model	Python	Jen
14	Introduction to clustering	Clustering	Clean, prep, and visualize your data; Introduction to clustering	Python R	Jen Eric Wanjau
15	Exploring Nigerian Musical Tastes 🎧	Clustering	Explore the K-Means clustering method	Python R	Jen Eric Wanjau
16	Introduction to natural language processing ☕️	Natural language processing	Learn the basics about NLP by building a simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks required when dealing with language structures	Python	Stephen
18	Translation and sentiment analysis ♥️	Natural language processing	Translation and sentiment analysis with Jane Austen	Python	Stephen
19	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews 1	Python	Stephen
20	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews 2	Python	Stephen
21	Introduction to time series forecasting	Time series	Introduction to time series forecasting	Python	Francesca
22	⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA	Time series	Time series forecasting with ARIMA	Python	Francesca
23	⚡️ World Power Usage ⚡️ - time series forecasting with SVR	Time series	Time series forecasting with Support Vector Regressor	Python	Anirban
24	Introduction to reinforcement learning	Reinforcement learning	Introduction to reinforcement learning with Q-Learning	Python	Dmitry
25	Help Peter avoid the wolf! 🐺	Reinforcement learning	Reinforcement learning Gym	Python	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.

PDFs

Find a pdf of the curriculum with links here.

Help Wanted!

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Other Curricula

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• Data Science for Beginners