README.md

🌐 Multi-Language Support

Supported via GitHub Action (Automated & 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

Prefer to Clone Locally?

Dis repository get 50+ language translations wey dey increase how big e be to download. To clone witout 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 go give you everything wey you need to complete di course fast well-well.

Join Our Community

We get Discord learn wit AI series wey dey go, learn more and join us fo Learn with AI Series from 18 - 30 September, 2025. You go get beta tips and tricks for how to use GitHub Copilot for Data Science.

Machine Learning for Beginners - A Curriculum

🌍 Travel round di world as we dey explore Machine Learning with world cultures 🌍

Cloud Advocates for Microsoft happy to offer 12-week, 26-lesson curriculum wey dey all about Machine Learning. For dis curriculum, you go learn wetin dem dey sometimes call classic machine learning, mainly using Scikit-learn as library, no go deep learning wey dey inside our AI for Beginners' curriculum. You fit pair dis lessons wit our 'Data Science for Beginners' curriculum too!

Travel wit us round di world as we apply these classic techniques to data from many different places for di world. Every lesson get pre- and post-lesson quizzes, written instructions to complete di lesson, solution, assignment, and more. Our project-based method make you learn while you dey build, na beta way to make new skills 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 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 thanks to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!

Getting Started

Follow these steps:

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

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

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

Students, to use dis curriculum, fork di whole repo go your own GitHub account and complete di exercises on your own or wit group:

• Start wit pre-lecture quiz.
• Read di lecture and do di activities, stop and think for each knowledge check.
• Try build di projects by understanding di lessons instead of just running di solution code; however di code dey inside /solution folder for each project-based lesson.
• Do post-lecture quiz.
• Complete di challenge.
• Complete di assignment.
• After you finish one lesson group, visit di Discussion Board and "learn out loud" by filling di correct PAT rubric. PAT na Progress Assessment Tool wey be rubric wey you fit fill to deepen your learning. You fit also react to other people PATs so we fit learn together.

For more study, we recommend following these Microsoft Learn modules and learning paths.

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

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Video walkthroughs

Some lessons dey available as short video form. You fit find all dem for inside di lessons, or for ML for Beginners playlist on the Microsoft Developer YouTube channel by clicking di picture 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 pedagogy principles as we dey build dis curriculum: make e be hands-on project-based and make e get many quizzes. Plus, dis curriculum get one common theme to make am get connection.

By making sure say di content dey relate to projects, di process dey more interesting for students and e go help them remember things well. Also, low-stakes quiz before class dey set the mindset of di student for how to learn di topic, while another quiz after class dey make dem store di knowledge more. Dis curriculum na flexible and fun one, you fit take all or part. Di projects start small and go get harder by di time 12 weeks finish. Dis curriculum get one postscript on real-world uses of ML, wey fit be extra credit or starting point for discussion.

Find our Code of Conduct, Contributing, Translations, and Troubleshooting guidelines. We dey welcome your constructive feedback!

Each lesson get

• 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
• challenge
• supplemental reading
• assignment
• post-lecture quiz

One note about languages: These lessons mainly written for Python, but many dey also for R. To finish R lesson, go di /solution folder and find di R lessons. Dem get .rmd extension wey mean R Markdown file wey fit be described as mixing code chunks (of R or other languages) and YAML header (wey dey guide how to format outputs like PDF) inside Markdown document. Like dis, e serve as good authoring setup for data science because e let you put together your code, output, and your thoughts by writing them down in Markdown. Also, R Markdown documents fit be rendered into output formats like PDF, HTML, or Word. Note about quizzes: All quizzes dem de for inside Quiz App folder, get total 52 quizzes wey each get three questions. Dem link am for inside lesson dem but quiz app fit run local; just follow the instruction wey dey for quiz-app folder to run am local or make e go Azure.

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Introduction to machine learning	Introduction	Learn the basic concepts wey dey behind machine learning	Lesson	Muhammad
02	The History of machine learning	Introduction	Learn the history wey dey under this field	Lesson	Jen and Amy
03	Fairness and machine learning	Introduction	Wetin be the important philosophical wahala about fairness wey pikin dem for learn when dem dey build and use ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	Wetin kind techniques ML researchers dey use to build ML models?	Lesson	Chris and Jen
05	Introduction to regression	Regression	Start to learn Python and Scikit-learn for regression models	Python • R	Jen • Eric Wanjau
06	North American pumpkin prices 🎃	Regression	Visualize and clean data make e ready 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 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, 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 recommender web app with 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 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 simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Increase your NLP knowledge by understanding common tasks wey you need when you dey handle 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 real real 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 for inside our Microsoft Learn collection

Offline access

You fit run this documentation offline by using Docsify. Fork this repo, install Docsify for your local machine, then for the root folder of this repo, type docsify serve. The website go de serve for port 3000 for your localhost: localhost:3000.

PDFs

Find pdf of the 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 you go build AI apps. Join other learners and beta developers for discussions about MCP. Na supportive community wey questions dey welcome and knowledge dey share freely.

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

Additional Learning Tips

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

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Disclaimer:
Dis document dem don use AI translation service Co-op Translator translate am. Even though we dey try make everything correct, abeg sabi say automatic translation fit get some errors or wahala. Di original document wey dem write for di proper language na di correct one to trust. If na serious matter, e better make human expert translate am. We no go responsible if person misunderstand or interpret am wrong because of this translation.