ML-For-Beginners/translations/pcm

🌐 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 | Khmer | 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 wan Clone am Tinside?

Dis repo get 50+ language translations wey dey make the download size big. 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 you need to complete di course fast.

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Machine Learning for Beginners - Curriculum

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

Cloud Advocates for Microsoft happy to offer 12-week, 26-lesson curriculum all about Machine Learning. For dis curriculum, you go learn wetin dem dey call classic machine learning, wey go use Scikit-learn as main library and no go dey do deep learning, wey we cover for our AI for Beginners' curriculum. Join these lessons with our 'Data Science for Beginners' curriculum, together too!

Travel with us round the world as we take apply these classic ways to data from plenty places for world. Every lesson get pre- and post-lesson quizzes, written instructions to finish the lesson, solution, assignment, and more. Our project-based way of teaching go make you learn while you dey build, na how new skills dem dey stick well.

✍️ 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 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 di "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

🔧 You need help? Look our Troubleshooting Guide for solution to common wahala like installation, setup, and how to run lessons.

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

• Start with pre-lecture quiz.
• Read the lecture and do the activities, stop sometimes to think for every knowledge check.
• Try create the projects by understanding the lessons instead of just running the solution code; but the code dey for /solution folders inside every project-based lesson.
• Take the post-lecture quiz.
• Finish the challenge.
• Do the assignment.
• After you finish one lesson group, visit the Discussion Board and "learn out loud" by filling di right PAT rubric. 'PAT' na Progress Assessment Tool wey be like rubric wey you dey fill to improve your learning. You fit also react other PATs so we go 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 form video versions. You fit find all these for inside lessons, or for the ML for Beginners playlist for Microsoft Developer YouTube channel by clicking di picture below.

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Meet the Team

Gif by Mohit Jaisal

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

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Pedagogy

We choose two pedagogy principles when we dey build dis curriculum: di first na to make am hands-on project-based and di second na to include many quizzes. Plus, dis curriculum get one common theme to give am better cohesion.

By making sure say content dey match with projects, e go make students dey more interested and dem go remember concepts well well. Also, low-stakes quiz before class dey set the mindset of student to learn better, while another quiz after class go make dem remember better. Dis curriculum designed to be flexible and fun and you fit take am full or part. Di projects begin small and go big and complex by end of 12-week cycle. Dis curriculum still get small last part about real-world Machine Learning usage, wey fit be extra credit or topic for discussion.

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

Each lesson include

• 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 the project
• knowledge checks
• a challenge
• extra reading
• assignment
• post-lecture quiz

Wan note about languages: Dem sabi write dis lessons mostly for Python, but plenty dey for R too. If you want finish wan R lesson, waka go the /solution folder make you find R lessons. Dem get .rmd extension wey mean R Markdown file wey fit be define as plenti code chunks (for R or oda languages) and wan YAML header (wey dey show how to make output dem like PDF) for Markdown document. Na so e be, e good for authoring framework for data science well well cos e dey allow you join your code, di output, and your thoughts by writing dem down for Markdown. More so, R Markdown documents fit turn to output formats like PDF, HTML, or Word.

Wan note about quizzes: All di quizzes dey for Quiz App folder, total na 52 quizzes with three questions each. Dem link am inside di lessons but di quiz app fit run for your local machine; follow di instruction for di quiz-app folder to run am for your side or use Azure make e deploy.

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Introduction to machine learning	Introduction	Learn di basic tins wey machine learning get behind am	Lesson	Muhammad
02	The 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 philosophy tins about fairness wey students suppose think about wen dem dey build and use ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	Wetin techniques dem ML researchers dey use to build ML models?	Lesson	Chris and Jen
05	Introduction to regression	Regression	Start wit 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 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, 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 di K-Means clustering method	Python • R	Jen • Eric Wanjau
16	Introduction to natural language processing ☕️	Natural language processing	Learn di basics about NLP by building 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 work with language structures	Python	Stephen
18	Translation and sentiment analysis ♥️	Natural language processing	Translation and sentiment analysis wit 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 wit ARIMA	Python	Francesca
23	⚡️ World Power Usage ⚡️ - time series forecasting with SVR	Time series	Time series forecasting wit 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 wit 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 machine, then for di root folder of dis repo, type docsify serve. Di website go run for port 3000 for your localhost: localhost:3000.

PDFs

Find pdf of di curriculum wit links here.

🎒 Other Courses

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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 developers wey sabi for discussions about MCP. Na community wey dey help, so questions dey welcome and knowledge dey share freely.

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

Additional Learning Tips

• Make you dey review notebooks after each lesson to understand better.
• Try practice to implement algorithms by yourself.
• Check 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 tho we dey try make am correct, abeg sabi say automatic translations fit get errors or mistakes. Di original document for dia own language na di correct source. For important info, e better make professional human translation do am. We no go responsible for any wrong understanding or misinterpretation wey fit come from dis translation.