ML-For-Beginners/translations/pcm

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Prefer to Clone Locally?

Dis repository get 50+ language translations wey dey make di download size big. To clone without translations, use sparse checkout:

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 wit much fasta download.

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Machine Learning for Beginners - A Curriculum

🌍 Travel around di world as we dey explore Machine Learning by di means of world cultures 🌍

Cloud Advocates for Microsoft happy to offer 12-week, 26-lesson curriculum wey na all about Machine Learning. Inside dis curriculum, you go learn wetin dem dey call classic machine learning, wey dem mainly dey use Scikit-learn as library and dem dey avoid deep learning, wey dey inside our AI for Beginners' curriculum. Pair these lessons wit our 'Data Science for Beginners' curriculum too!

Travel wit us around di world as we dey apply these classic techniques to data from many places for di world. Each lesson get pre- and post-lesson quizzes, written instructions to complete di lesson, solution, assignment, and more. Our project-based way to teach make you learn as you dey build, na good way for new skills to 'stick'.

✍️ Many tings 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 as well 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 wey dey 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 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 the exercises on your own or wit group:

• Start wit pre-lecture quiz.
• Read di lecture and complete di activities, pause and think at each knowledge check.
• Try to create di projects by understanding di lessons instead of just running di solution code; but di code dey inside /solution folders for each project lesson.
• Take post-lecture quiz.
• Complete di challenge.
• Complete di assignment.
• After you don complete one lesson group, visit di Discussion Board and "learn out loud" by filling di correct PAT rubric. 'PAT' na Progress Assessment Tool wey you go fill as you dey learn. You fit react to 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 include some suggestions on how to use dis curriculum.

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Video walkthroughs

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

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Meet the Team

Gif by Mohit Jaisal

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

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Pedagogy

We choose two main teaching rules when we dey build dis curriculum: to make sure say e hands-on project-based and e get frequent quizzes. Also, dis curriculum get common theme to make am hold together.

By making sure say di content go dey with projects, di process go sweet students and e go help dem remember wetin dem learn. Plus, low-stakes quiz before class dey make student ready to learn topic, then another quiz after class go make dem remember pass. Dis curriculum na flexible and fun and you fit do am all or just part. Di projects start small and get harder by di end of di 12-week period. This curriculum still get postscript on real-life use of ML, wey fit be extra credit or basis for talk.

Find our Code of Conduct, Contributing, Translation, and Troubleshooting rules. 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 the project
• knowledge checks
• challenge
• extra reading
• assignment
• post-lecture quiz

Note about languages: These lessons main for Python, but plenty still dey for R. To complete R lesson, go inside /solution folder and find R lessons. Dem get .rmd extension wey mean R Markdown file wey na mix of code chunks (R or other languages) and YAML header (wey control how to format outputs like PDF) inside Markdown document. So e good for authoring data science stuff cos e let you join your code, output, plus your ideas by writing dem down inside Markdown. R Markdown files fit also go output like PDF, HTML, or Word. Wan warning about quizzes: All quizzes dey for Quiz App folder, total na 52 quizzes with three questions each. Dem dey linked inside di lessons but di quiz app fit run local; follow di instruction for di quiz-app folder to host or deploy am for Azure local.

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 tins about fairness wey students suppose consider when 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 work 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 your trained model	Python	Jen
10	Introduction to classification	Classification	Clean, prepare, 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 wey use your model	Python	Jen
14	Introduction to clustering	Clustering	Clean, prepare, 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 dey do when 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 for 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 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 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 too! Check am out:

LangChain

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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 questions about how to build AI apps. Join other learners and experienced developers for talks about MCP. Na supportive community wey dey welcome questions and share knowledge freely.

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

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Disclaimer:
Dis document na AI translation service Co-op Translator wey do di translation. Even though we dey try make am correct, make you sabi seh automatic translation fit get errors or small mistake. Di original document for e own language na di correct one wey you suppose check. If na important tori, make you use human wey sabi language translate for you. We no go take responsibility if person no understand well or misinterpret anything from dis translation.