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Machine Learning for Beginners - A Curriculum

🌍 Embark on a global journey as we explore Machine Learning through the lens of world cultures 🌍

Microsoft's Cloud Advocates are excited to present a 12-week, 26-lesson curriculum focused on Machine Learning. This curriculum introduces you to classic machine learning techniques, primarily using the Scikit-learn library, while steering clear of deep learning (covered in our AI for Beginners' curriculum). Pair these lessons with our 'Data Science for Beginners' curriculum for a comprehensive learning experience!

Travel with us across the globe as we apply these techniques to datasets from various regions. Each lesson includes quizzes, step-by-step instructions, solutions, assignments, and more. Our project-based approach ensures you learn by doing, a proven method for retaining new skills.

✍️ A big thank you 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

🎨 Special thanks to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper

🙏 Heartfelt gratitude 🙏 to our Microsoft Student Ambassador authors, reviewers, and contributors, including Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal

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

Getting Started

Follow these steps:

1. Fork the Repository: Click the "Fork" button at the top-right corner of this page.
2. Clone the Repository: git clone https://github.com/microsoft/ML-For-Beginners.git

Find additional resources for this course in our Microsoft Learn collection

Students, to use this curriculum, fork the repository to your GitHub account and complete the exercises individually or in groups:

• Begin with a pre-lecture quiz.
• Read the lecture and complete the activities, pausing for reflection at each knowledge check.
• Build the projects by understanding the lessons rather than relying on solution code (available in the /solution folders for project-based lessons).
• Take the post-lecture quiz.
• Complete the challenge.
• Finish the assignment.
• After completing a lesson group, visit the Discussion Board and share your learning using the PAT rubric. A 'PAT' (Progress Assessment Tool) helps you assess your progress and encourages collaborative learning.

For further study, explore these Microsoft Learn modules and learning paths.

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

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Video walkthroughs

Some lessons are available as short videos. You can find them embedded in the lessons or on the ML for Beginners playlist on the Microsoft Developer YouTube channel by clicking the image below.

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Meet the Team

Gif by Mohit Jaisal

🎥 Click the image above to watch a video about the project and the team behind it!

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Pedagogy

This curriculum is built on two key principles: hands-on project-based learning and frequent quizzes. Additionally, it follows a common theme for cohesion.

By aligning content with projects, the learning process becomes more engaging, and concepts are easier to retain. Low-stakes quizzes before and after lessons help reinforce learning. The curriculum is flexible and fun, allowing students to take it in full or in part. Projects start simple and grow in complexity over the 12-week cycle. A postscript on real-world ML applications is included for extra credit or discussion.

Check out our Code of Conduct, Contributing, and Translation guidelines. We welcome your 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: 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 files with the .rmd extension. These are R Markdown files, which combine code, output, and commentary in a single document. R Markdown documents can be rendered into formats like PDF, HTML, or Word, making them ideal for data science.

A note about quizzes: All quizzes are located in the Quiz App folder, with 52 quizzes containing three questions each. They are linked within the lessons but can also be run locally. Follow the instructions in the quiz-app folder to host them locally or deploy to Azure.

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 key philosophical issues around fairness that students should consider when developing and applying ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	What techniques do ML researchers use to create 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 and Dmitry • 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 of 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 working 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
Postscript	Model Debugging in ML using RAI dashboard	ML in the Wild	Model Debugging in Machine Learning using Responsible AI dashboard components	Lesson	Ruth Yakubu

find all additional resources for this course in our Microsoft Learn collection

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.

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