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Machine Learning for Beginners - A Curriculum

🌍 Travel around di world as we dey explore Machine Learning through 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 about wetin dem dey call classic machine learning, wey mainly dey use Scikit-learn as library and no go deep learning, wey dey inside our AI for Beginners' curriculum. You fit join these lessons with our 'Data Science for Beginners' curriculum too!

Make you travel with us around di world as we dey apply these classic techniques to data from many parts of di world. Each lesson get pre- and post-lesson quizzes, written instructions to complete di lesson, solution, assignment, and more. Our project-based way of teaching dey allow you learn as you dey build, na better 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 too 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:

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

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

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

Students, to use dis curriculum, fork di whole repo to your own GitHub account and complete di exercises by yourself or with group:

Start with pre-lecture quiz.
Read di lecture and complete di activities, stop and think for each knowledge check.
Try to create di projects by understanding di lessons instead of just running di solution code; but di code dey available for /solution folders for each project-based lesson.
Take di 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 you go fill to help your learning. You fit also react to other PATs so we fit learn together.

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

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

Video walkthroughs

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

Meet the Team

Gif by Mohit Jaisal

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

Pedagogy

We choose two teaching principles while we dey build dis curriculum: to make am hands-on project-based and to include frequent quizzes. Plus, dis curriculum get one common theme to make am consistent.

By making sure say di content match projects, di process go dey more interesting for students and e go help dem remember concepts well. Plus, one low-stakes quiz before class dey set student mind to learn topic, and one more quiz after class go help dem remember more. Dis curriculum design to be flexible and fun and you fit take am full or part. Di projects start small and go big as di 12-week cycle dey end. Dis curriculum also get one postscript on real-world ML applications, wey fit be extra credit or discussion base.

Find our Code of Conduct, Contributing, Translation, and Troubleshooting guidelines. We dey 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 di project
knowledge checks
a challenge
supplemental reading
assignment
post-lecture quiz

A note about languages: These lessons mainly dey written for Python, but many dey also available for R. To complete R lesson, go di /solution folder and find R lessons. Dem get .rmd extension wey mean R Markdown file wey fit be simply defined as embedding code chunks (of R or other languages) and YAML header (wey guide how to format outputs like PDF) inside Markdown document. So, e serve as good authoring framework for data science because e allow you to combine your code, output, and your thoughts by writing dem down for Markdown. Plus, R Markdown documents fit render to output formats like PDF, HTML, or Word.

A note about quizzes: All quizzes dey inside Quiz App folder, total 52 quizzes with three questions each. Dem link from inside lessons but quiz app fit run locally; follow instruction inside quiz-app folder to host locally or deploy to Azure.

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Introduction to machine learning	Introduction	Learn di basic concepts wey dey behind machine learning	Lesson	Muhammad
02	The History of machine learning	Introduction	Learn di history wey dey under dis field	Lesson	Jen and Amy
03	Fairness and machine learning	Introduction	Wetin be di important philosophical issues about fairness wey students suppose consider wen dem dey build and apply ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	Wetin techniques ML researchers dey use to build ML models?	Lesson	Chris and Jen
05	Introduction to regression	Regression	Start to use 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 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 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 K-Means clustering method	Python • R	Jen • Eric Wanjau
16	Introduction to natural language processing ☕️	Natural language processing	Learn basics about NLP by building simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks wey dem need wen dem dey deal 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, then for di root folder of dis repo, type docsify serve. Di website go dey serve for port 3000 for your localhost: localhost:3000.

PDFs

Find pdf of di curriculum with links here.

🎒 Other Courses

Our team dey produce other courses! Check am out:

LangChain

Azure / Edge / MCP / Agents

Generative AI Series

Core Learning

Copilot Series

Getting Help

If you get stuck or get any question about how to build AI apps. Join other learners and experienced developers for discussions about MCP. Na supportive community wey questions dey welcome and knowledge dey shared freely.

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

Disclaimer: Dis document don translate wit AI translation service Co-op Translator. Even though we dey try make am correct, abeg sabi say automated translation fit get some mistakes or no too correct. Di original document wey e dey for im own language na di correct one. If na serious matter, e better make human professional translate am. We no go responsible for any wahala or wrong understanding wey fit happen because of dis translation.

29 KiB Raw Blame History Unescape Escape