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🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

Prefer to Clone Locally?

Dis repository get over 50 languages wey dem don translate and e go make di download size big well well. If you want clone am without di translations, use sparse checkout:

Bash / macOS / Linux:
git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
cd ML-For-Beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
CMD (Windows):
git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
cd ML-For-Beginners
git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"
Dis one go give you everything wey you need to finish di course quick quick.

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Machine Learning for Beginners - A Curriculum

🌍 Travel around di world as we explore Machine Learning through different world cultures 🌍

Cloud Advocates for Microsoft happy to offer 12 weeks, 26 lesson curriculum all about Machine Learning. For dis curriculum, you go learn about wetin dem dey call classic machine learning, using mainly Scikit-learn as library and avoid deep learning, wey we cover for our AI for Beginners' curriculum. You fit also join am with our 'Data Science for Beginners' curriculum.

Travel with us round di world as we apply dis classic techniques to data from many parts of di world. Each lesson get quiz before and after, instructions to complete am, solution, assignment, and more. Our project-based method go help you learn as you build, wey be beta way for new skills to stick.

✍️ Big 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 also to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper

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

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

Getting Started

Follow dis steps:

Fork di Repository: Click di "Fork" button for di top-right corner of dis page.
Clone di Repository: git clone https://github.com/microsoft/ML-For-Beginners.git

💡 Quick Start Tip: You want start for browser without setting Python for your machine? Use GitHub Codespaces to create cloud development environment for your fork. Open di green Code menu, select Codespaces, and create codespace; then install each lesson dependencies as e dey needed.

find all additional resources for dis course inside our Microsoft Learn collection

🔧 Need help? Check our Troubleshooting Guide for solutions to common wahala for installation, setup, and running lessons.

Students, to use dis curriculum, fork di full repo to your own GitHub account and complete di exercises alone or for group:

Start with pre-lecture quiz.
Read di lecture and do di activities, stop and think for each knowledge check.
Try create di projects by understanding di lessons, no too dey rely on di solution code; but di code dey for /solution folders for each project lesson.
Do di post-lecture quiz.
Complete di challenge.
Complete di assignment.
After finishing one lesson group, visit di Discussion Board and "learn out loud" by filling di correct PAT rubric. PAT na Progress Assessment Tool wey you fill to boost your learning. You fit also react to other PATs so we fit learn together.

For more study, we recommend you follow dis Microsoft Learn modules and learning paths.

Teachers, we don include some suggestions on how to use dis curriculum.

Video walkthroughs

Some of di lessons dey available as short video. You fit find all dem inside di lessons, or for di ML for Beginners playlist for Microsoft Developer YouTube channel by clicking di picture below.

Meet the Team

Gif by Mohit Jaisal

🎥 Click di picture above make you watch video about di project and di people wey create am!

Pedagogy

We choose two teaching principles wen we build dis curriculum: make e be hands-on project-based and make e get frequent quizzes. E good make dis curriculum get one common theme so e go get better organisation.

If di content dey connect with projects, e go make learning sweet for students and e go make concepts stick well. Also, one small quiz before class go set student mind for learning, and the quiz after class go help dem remember well. Dis curriculum na flexible and fun for use, e fit be complete or part of am. Projects start small and get hard as di 12 weeks finish. Also, dis curriculum get small postscript for real-life ML applications, wey fit be extra credit or discussion material.

Find our Code of Conduct, Contributing, Translations, and Troubleshooting 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 mainly write for Python, but many get also for R. To finish R lesson, go /solution folder find R lessons. Dem get .rmd extension wey mean R Markdown file wey be mix of code chunks (of R or other languages) and YAML header (wey show how make outputs like PDF). E good for data science because e blend code, output, and your writing for Markdown. Plus, R Markdown files fit turn to PDF, HTML, or Word.

A note about quizzes: All quizzes dey inside Quiz App folder, with 52 quizzes each with three questions. Dem link am from lessons but quiz app fit run locally; follow instruction inside quiz-app folder to host 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 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 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 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
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 fit run dis documentation offline by using Docsify. Fork dis repo, install Docsify for your local machine, den for the root folder of dis repo, type docsify serve. The website go dey available for port 3000 for your localhost: localhost:3000.

PDFs

Find one pdf of the curriculum with links here.

🎒 Other Courses

Our team dey make other courses! Check am out:

LangChain

Azure / Edge / MCP / Agents

Generative AI Series

Core Learning

Copilot Series

Getting Help

If you jam problem or get questions while you dey learn Machine Learning or dey build AI applications, no worry — help dey.

You fit join discussions with other learners and developers, ask questions, and share your ideas with the community.

Join the community to ask questions and learn with others
Discuss Machine Learning concepts and project ideas
Get guidance from experienced developers

One supportive community na beta way to grow your skills and solve problems quick.

Microsoft Foundry Discord Community

If you find bugs, errors, or get suggestions for better improvement, you fit open one Issue for dis repository to report the problem.

For product feedback or to search existing community posts, go visit the Developer Forum:

Additional Learning Tips

Review notebooks after each lesson for better understanding.
Practice implementing algorithms on your own.
Explore real-world datasets using learned concepts.

Disclaimer: Dis document don translate wit AI translation service Co-op Translator. Even tho we dey try make am correct, abeg make you know say automated translation fit get errors or mistakes. Di original document for dia own language na im be di correct source. For important info, make person wey sabi human translation do am. We no go responsible for any misunderstanding or wrong understanding wey fit happen because of dis translation.

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