ML-For-Beginners/translations/pcm

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Prefer to Clone Locally?

Dis repository get 50+ language translations wey dey increase di download size well well. To 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 wey you need to complete di course quick quick.

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Machine Learning for Beginners - A Curriculum

🌍 Travel around di world as we dey explore Machine Learning thru world cultures 🌍

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

Make you travel with us around di world as we dey apply dis classic techniques to data from many parts of di world. Every lesson get pre- and post-lesson quizzes, written instructions to complete di lesson, solution, assignment, and more. Our project-based way to teach fit help you learn well as you dey build, na the best way for new skills to hold tight.

✍️ 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 all dis steps:

1. Fork the Repository: Click the "Fork" button for di top-right corner of dis page.

2. Clone the Repository: git clone https://github.com/microsoft/ML-For-Beginners.git

Find all extra resources for dis course inside our Microsoft Learn collection

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

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

• Start with pre-lecture quiz.
• Read the lecture and do di activities, stop and think for each knowledge check.
• Try create the projects by understanding the lessons instead of just running the solution code; but dat code dey for /solution folders for each project-based lesson.
• Do the post-lecture quiz.
• Complete the challenge.
• Finish the assignment.
• After you finish one lesson group, visit the Discussion Board and "learn out loud" by filling di correct PAT rubric. 'PAT' na Progress Assessment Tool wey you fit fill to take your learning go further. You fit also react to others PATs so we fit learn together.

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

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

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Video walkthroughs

Some lessons get short video form. You fit find all these inline for the lessons, or on the ML for Beginners playlist for the Microsoft Developer YouTube channel by clicking the image below.

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Meet the Team

Gif by Mohit Jaisal

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

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Pedagogy

We choose two methods to teach while we dey build dis curriculum: make am hands-on project-based and make e get plenty quizzes. Plus, dis curriculum get common theme to make e dey make sense together.

As we make the content align with projects, e help students enjoy and remember better. Also, small quiz before class go prepare the mind of student to learn topic, while second quiz after class go help make e stick. Dis curriculum design to be flexible and fun, you fit take all or part. Di projects start small and dey get complex well by end of 12-week course. This curriculum get postscript on real-world Machine Learning applications, fit use am as extra credit or for discussion.

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

Each lesson get

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

One note about languages: Dem write most of dis lessons for Python, but plenti dey available for R too. If you want complete one R lesson, waka go the /solution folder and find R lessons. Dem get .rmd extension wey mean R Markdown file we fit talk sey na how you go put code chunks (wey fit be R or other languages) and one YAML header (wey dey guide how outputs go be like PDF) for inside one Markdown document. So e mean sey e good framework for data science because e allow you join your code, wetin e produce, and your brain thoughts by make you fit write dem down for Markdown. Plus, R Markdown documents fit turn into output formats like PDF, HTML, or Word.

One note about quizzes: All quizzes dey for Quiz App folder, plenti 52 quizzes wit three question each. Dem connect inside the lessons but quiz app fit run for your computer; follow the instruction wey dey the quiz-app folder to run am local or put am for 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 the important philosophical tins about fairness we students suppose consider 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 work with Python and Scikit-learn for regression models	Python • R	Jen • Eric Wanjau
06	North American pumpkin prices 🎃	Regression	See and clean data well well 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 wey you fit use your trained model	Python	Jen
10	Introduction to classification	Classification	Clean, prep, and show your data graph; 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 show your data graph; 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 one simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Make your NLP knowledge strong by understanding common tasks wey dey when you 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 clear real-world uses 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 by using Docsify. Fork dis repo, install Docsify for your local machine, then for the root folder of this repo, type docsify serve. The website go dey for port 3000 for your localhost: localhost:3000.

PDFs

Find pdf of the curriculum wit links here.

🎒 Other Courses

Our team dey produce other courses! Look:

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 questions as you dey learn Machine Learning or dey build AI applications, no worry — help dey.

You fit join discussions with oda learners and developers, ask question, and share your ideas with di community.

• Join di community to ask questions and learn with oda people
• Discuss Machine Learning concepts and project ideas
• Get guidance from experienced developers

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 improvements, you fit open Issue for this repository to report di problem.

For product feedback or to find existing community posts, visit di Developer Forum:

Additional Learning Tips

• Review notebooks after each lesson to understand better.
• Practice to implement algorithms by yourself.
• Explore real-world datasets using wetin you don learn.

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Disclaimer: Dis document don translate wit AI translation service Co-op Translator. Even as we dey try make am correct, abeg sabi say automated translations fit get errors or wahala. Di original document for e own language na di real authority. For important information, na professional human translation better. We no dey responsible for any wahala or wrong understanding wey fit come from dis translation use.