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Machine Learning for Beginners - A Curriculum

🌍 Travel the world as we explore Machine Learning through the lens of global cultures 🌍

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

Join us on a journey around the globe as we apply these classic techniques to datasets from various regions. Each lesson includes pre- and post-lesson quizzes, step-by-step instructions, solutions, assignments, and more. Our project-based approach ensures that 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 their contributions 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 entire repository to your GitHub account and complete the exercises individually or in a group:

• Start with a pre-lecture quiz.
• Read the lecture and complete the activities, pausing to reflect during each knowledge check.
• Try to build the projects by understanding the lessons rather than simply running the solution code. However, solution code is available in the /solution folders for each project-based lesson.
• Take the post-lecture quiz.
• Complete the challenge.
• Finish 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 helps you reflect on your learning. You can also engage with other PATs to foster collaborative learning.

For further study, we recommend exploring 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-form videos. You can find these 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 pedagogical principles: it is hands-on project-based and includes frequent quizzes. Additionally, the curriculum is tied together by a common theme for cohesion.

By aligning the content with projects, the learning process becomes more engaging, and students are more likely to retain the concepts. Low-stakes quizzes before a lesson help set the stage for learning, while post-lesson quizzes reinforce understanding. This curriculum is designed to be flexible and enjoyable, allowing students to take it in full or in part. The projects start small and gradually increase in complexity over the 12-week period. A postscript on real-world ML applications is included, which can be used as extra credit or as a discussion starter.

Check out our Code of Conduct, Contributing, and Translation guidelines. We welcome your constructive feedback!

Each lesson includes

• Optional sketchnote
• Optional supplemental video
• Video walkthrough (for some lessons)
• Pre-lecture warmup quiz
• Written lesson
• For project-based lessons, step-by-step guides 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. These files have an .rmd extension, which stands for R Markdown. R Markdown allows you to combine code, its output, and your thoughts in a single document. It can be rendered into formats like PDF, HTML, or Word.

A note about quizzes: All quizzes are located in the Quiz App folder, with 52 quizzes in total, each containing three questions. Quizzes are linked within the lessons, but the quiz app can also be run locally. Follow the instructions in the quiz-app folder to host it locally or deploy it 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 important philosophical issues around fairness that students should consider when building and applying ML models?	Lesson	Tomomi
04	Machine Learning Techniques	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 to prepare 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, prepare, 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, prepare, 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 insightful 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 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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Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.