README.md

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Machine Learning for Beginners - A Curriculum

🌍 Travel around di world as we dey learn Machine Learning wit di help of world cultures 🌍

Cloud Advocates for Microsoft don prepare one 12-week, 26-lesson curriculum wey dey teach about Machine Learning. For dis curriculum, you go learn wetin dem dey call classic machine learning, wey dey use Scikit-learn as di main library and e no dey include deep learning, wey dem cover for AI for Beginners' curriculum. You fit pair dis lessons wit our 'Data Science for Beginners' curriculum, too!

Follow us waka around di world as we dey use dis classic techniques for data from different parts of di world. Each lesson get pre-lesson and post-lesson quiz, written instructions for di lesson, solution, assignment, and more. Our project-based style go help you learn as you dey build, wey be one sure 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 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!

How to Start

Follow dis steps:

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

find all extra resources for dis course for our Microsoft Learn collection

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

Students, to use dis curriculum, fork di whole repo go your own GitHub account and complete di exercises by yourself 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; but di code dey available for /solution folders for each project-based lesson.
• Take di post-lecture quiz.
• Do di challenge.
• Do di assignment.
• After you finish one lesson group, visit di Discussion Board and "learn out loud" by filling di PAT rubric. PAT na Progress Assessment Tool wey be rubric wey you go fill to help your learning. You fit also react to other PATs so we go fit learn together.

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

Teachers, we don add some ideas on how you fit use dis curriculum.

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Video walkthroughs

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

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Meet di Team

Gif by Mohit Jaisal

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

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Pedagogy

We don choose two teaching styles for dis curriculum: make e dey hands-on project-based and make e get plenty quizzes. Plus, dis curriculum get one common theme wey dey make am dey connected.

By making sure say di content dey match wit projects, e go make di process dey more interesting for students and e go help dem remember di concepts well. Plus, quiz wey no dey too hard before class go help di student focus on di topic, while di second quiz after class go help dem remember am better. Dis curriculum dey flexible and fun and you fit take am complete or small small. Di projects dey start small and e dey grow more complex by di end of di 12-week cycle. Dis curriculum also get one extra part about real-world use of ML, wey fit be extra credit or topic for discussion.

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

Each lesson get

• optional sketchnote
• optional extra video
• video walkthrough (some lessons only)
• pre-lecture warmup quiz
• written lesson
• for project-based lessons, step-by-step guide on how to build di project
• knowledge checks
• one challenge
• extra reading
• assignment
• post-lecture quiz

About languages: Di lessons dey mainly for Python, but some dey available for R. To do R lesson, go /solution folder and look for R lessons. Dem get .rmd extension wey mean R Markdown file wey dey combine code chunks (of R or other languages) and YAML header (wey dey guide how to format outputs like PDF) inside Markdown document. E dey good for data science because e dey allow you mix your code, di output, and your thoughts for Markdown. Plus, R Markdown documents fit turn to output formats like PDF, HTML, or Word.

About quizzes: All quizzes dey inside Quiz App folder, total 52 quizzes wey get three questions each. Dem dey linked inside di lessons but di quiz app fit run locally; follow di instruction for quiz-app folder to host am locally or deploy am go Azure.

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Intro to machine learning	Introduction	Learn di basic idea wey dey behind machine learning	Lesson	Muhammad
02	Di History of machine learning	Introduction	Learn di history wey dey for dis field	Lesson	Jen and Amy
03	Fairness and machine learning	Introduction	Wetin be di important philosophical matter wey students suppose think about when dem dey build and use ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	Wetin be di techniques wey ML researchers dey use to build ML models?	Lesson	Chris and Jen
05	Intro to regression	Regression	Start 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 logistic regression model	Python • R	Jen • Eric Wanjau
09	A Web App 🔌	Web App	Build web app to use di trained model	Python	Jen
10	Intro to classification	Classification	Clean, prep, and visualize your data; intro to classification	Python • R	Jen and Cassie • Eric Wanjau
11	Delicious Asian and Indian cuisines 🍜	Classification	Intro 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	Intro to clustering	Clustering	Clean, prep, and visualize your data; Intro to clustering	Python • R	Jen • Eric Wanjau
15	Exploring Nigerian Musical Tastes 🎧	Clustering	Explore di K-Means clustering method	Python • R	Jen • Eric Wanjau
16	Intro to natural language processing ☕️	Natural language processing	Learn di basics of NLP by building simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks wey dey involve 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	Intro to time series forecasting	Time series	Intro 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	Intro to reinforcement learning	Reinforcement learning	Intro to reinforcement learning with Q-Learning	Python	Dmitry
25	Help Peter avoid di 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 dis course for our Microsoft Learn collection

Offline access

You fit run dis documentation offline by using Docsify. Fork dis repo, install Docsify for your local machine, and then for di root folder of dis repo, type docsify serve. Di website go dey served 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 dem out:

Azure / Edge / MCP / Agents

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Generative AI Series

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Core Learning

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Copilot Series

How to Get Help

If e be say you dey stuck or you get any question about how to build AI apps. You fit join other learners and developers wey sabi well well to talk about MCP. Na one kind community wey dey support people, dem go answer your question and share knowledge freely.

If you get feedback about product or you dey see error when you dey build, go visit:

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Disclaimer:
Dis docu don dey translate wit AI translation service Co-op Translator. Even though we dey try make am correct, abeg sabi say automatic translation fit get mistake or no dey accurate well. Di original docu for im native language na di main correct source. For important information, e good make una use professional human translation. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because of dis translation.