README.md

🌐 Multi-Language Support

Dem dey supported via GitHub Action (Automatic & Always Up-to-Date)

Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Kannada | Korean | Lithuanian | Malay | Malayalam | Marathi | Nepali | Nigerian Pidgin | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Telugu | Thai | Turkish | Ukrainian | Urdu | Vietnamese

You prefer make you Clone am for your local machine?

Dis repo get more than 50 language translation wey go make di download big well-well. If you wan clone 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 you need to finish di course quick-quick.

Join Our Community

We get one Discord "learn with AI" series wey dey go on, learn more and join us for Learn with AI Series from 18 - 30 September, 2025. You go fit collect tips and now-how for how to use GitHub Copilot for Data Science.

Machine Learning for Beginners - A Curriculum

🌍 Make we waka all around di world as we dey explore Machine Learning through different world cultures 🌍

Cloud Advocates for Microsoft dey happy to offer one 12-week, 26-lesson curriculum wey dey all about Machine Learning. For this curriculum, you go learn about wetin dem dey call sometimes classic machine learning, we go mainly use Scikit-learn as library and avoid deep learning, wey dem cover for our AI for Beginners' curriculum. Combine this lessons with our 'Data Science for Beginners' curriculum, too!

Make you waka with us round di world as we take these classic techniques apply to data from plenty areas for di world. Every lesson get pre- and post-lesson quizzes, written instructions to finish the lesson, solution, assignment, and plenty more. Our project-based way to teach dey help you learn as you build, na better way to make new skills dey stay for your head.

✍️ Big tori and 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 again 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 big thanks to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!

How to Start

Follow these steps:

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

You go find all other materials for dis course for our Microsoft Learn collection

🔧 You need help? Check our Troubleshooting Guide for solutions to common wahala with installation, setup, and to run lessons.

Students, to use this curriculum, fork the whole repo to your own GitHub account and do the exercises your own or with group:

• Start with pre-lecture quiz.
• Read di lecture and complete di activities, stop sometimes and reflect at every knowledge check.
• Try make you create the projects by understanding di lessons rather than just running di solution code; but dat code dey available for the /solution folders for each project-based lesson.
• Take di post-lecture quiz.
• Finish di challenge.
• Finish di assignment.
• After you don finish one lesson group, go visit di Discussion Board and "learn out loud" by filling out di correct PAT rubric. PAT na Progress Assessment Tool wey be rubric wey you go fill to help your learning. You fit react to other PATs so we fit learn together.

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

Teachers, we don put some suggestions on top how to use this curriculum.

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Video walkthroughs

Some lessons dey available as short form video. You fit find all of them inline for di lessons, or for di ML for Beginners playlist on di Microsoft Developer YouTube channel by clicking the image below.

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Meet the Team

Gif by Mohit Jaisal

🎥 Click di picture above for video about di project and di people wey create am!

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Pedagogy

We choose two main tinz wey we go dey follow while making this curriculum: make e hands-on project-based and make e get frequent quizzes. Plus, dis curriculum get one common theme to make e get connection.

By making sure sey di content match with projects, di process go be more interesting for students and e go help dem remember better. Plus, one low-stakes quiz before class dey set di student mind to learn topic, and the second quiz after class dey make dem remember more. This curriculum na flexible and fun, you fit do am complete or part-part. The projects start small and dem dey get more complex as di 12 weeks dey reach end. This curriculum also get one postscript about real-world ways to use ML, wey fit be extra credit or foundation for discussion.

Find our Code of Conduct, Contributing, Translation, and Troubleshooting guidelines. We dey happy to hear your constructive feedback!

Every lesson get

• optional sketchnote
• optional additional video
• video walkthrough (only some lessons get am)
• pre-lecture warmup quiz
• written lesson
• for project-based lessons, step-by-step guides on how to build the project
• knowledge checks
• challenge
• supplemental reading
• assignment
• post-lecture quiz

One note about languages: These lessons mainly dem write for Python, but plenti sef dey available for R. To finish one R lesson, go the /solution folder and find R lessons. Dem get .rmd extension wey mean R Markdown file wey fit be simply talk say na di joining of code chunks (we fit get for R or other languages) and one YAML header (wey dey guide how outputs like PDF go be formatted) for one Markdown document. So e be like example authoring framework for data science because e go allow you put your code, di output, and your thoughts together by allowing you write am for Markdown. Plus, R Markdown documents fit get rendered to output formats like PDF, HTML, or Word.

One note about quizzes: All di quizzes dey for Quiz App folder, get 52 quizzes wey get three questions each. Dem link dem from inside di lessons but you fit run di quiz app local; just follow di instruction wey dey di quiz-app folder to run or deploy am 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	Wetin be di important philosophical issues about fairness wey students suppose consider when dem dey build and use ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	Which techniques ML researchers dey use to build ML models?	Lesson	Chris and Jen
05	Introduction to regression	Regression	Start to waka for 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 one logistic regression model	Python • R	Jen • Eric Wanjau
09	A Web App 🔌	Web App	Build 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 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	Learn about K-Means clustering method	Python • R	Jen • Eric Wanjau
16	Introduction to natural language processing ☕️	Natural language processing	Learn di small-small basics about NLP by building one simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Sharpen your NLP knowledge by understanding common tasks to do when you dey reason 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 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 this documentation offline using Docsify. Fork this repo, install Docsify for your local machine, then for di root folder of this repo, type docsify serve. Di website go dey serve for port 3000 for your localhost: localhost:3000.

PDFs

Find pdf version of di curriculum with links here.

🎒 Other Courses

Our team dey produce other courses! Check am out:

LangChain

Azure / Edge / MCP / Agents

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Generative AI Series

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Core Learning

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Copilot Series

Getting Help

If you get stuck or get any question about how to build AI apps. Join other learners and experience developers for talk about MCP. Na supportive community wey dey open for questions and knowledge dey share freely.

If you get product feedback or errors while you dey build, visit:

Additional Learning Tips

• Check your notebooks after every lesson to understand better.
• Try dey practice to implement algorithms by yourself.
• Explore real-world datasets using the things wey you learn.

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Disclaimer: Dis document na AI translation service Co-op Translator translate am. Even though we try make am correct, abeg sabi say automated translation fit get wahala or mistakes. The original document for the original language be di correct source. If na important thing, e better make professional human translation do am. We no go responsible if person waka misunderstand or use dis translation wrong.