ML-For-Beginners/translations/pcm

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Prefer to Clone Locally?

Dis repository get more pass 50 language translations wey go big pass di download size. To clone without di 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 gi you everytin wey you need to finish di course with beta fast download.

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Machine Learning for Beginners - A Curriculum

🌍 Travel around di world as we explore Machine Learning using world cultures 🌍

Cloud Advocates for Microsoft dey 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, normally using Scikit-learn as library and we no dey do deep learning wey dey for our AI for Beginners' curriculum. Pair dis lessons with our 'Data Science for Beginners' curriculum, too!

Travel with us around di world as we apply dis classic techniques to data from different 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 of teaching make you dey learn while you dey build, na beta 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 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 for 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 dis course in our Microsoft Learn collection

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

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

• Start with pre-lecture quiz.
• Read di lecture and complete di activities, stop and think for every knowledge check.
• Try to build di projects by understanding di lessons instead of just running di solution code; but dat code dey for /solution folders for each project lesson.
• Take di post-lecture quiz.
• Complete di challenge.
• Complete di assignment.
• After you finish lesson group, visit di Discussion Board and "learn out loud" by filling di correct PAT rubric. A '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 fit learn together.

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

Teachers, we don put 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 inside di lessons, or for di ML for Beginners playlist for Microsoft Developer YouTube channel by clicking di image below.

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Meet the Team

Gif by Mohit Jaisal

🎥 Click di image up top for video about di project and di pipo wey create am!

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Pedagogy

We choose two teaching principles while we dey build dis curriculum: to make am hands-on project-based and to put frequent quizzes. Also, dis curriculum get one theme to make everything gel well.

By making sure say di content match projects, e make di learning dey more interesting for students and e go help dem remember di concepts better. Plus, low-stakes quiz before class dey set di student mind to learn di topic, while di second quiz after class dey help remember am more. Dis curriculum na to be easy and fun, and you fit do am whole or part. Di projects start small and dem go dey more complex by di end of di 12-week cycle. Dis curriculum get postscript on how ML dey work for real world, wey fit be extra credit or basis for talk.

Find our Code of Conduct, Contributing, Translation, and Troubleshooting guidelines. We 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
• challenge
• supplemental reading
• assignment
• post-lecture quiz

Talk about languages: These lessons mainly dey written for Python, but many dey also available for R. To finish R lesson, go di /solution folder and look for R lessons. Dem get .rmd extension wey mean R Markdown file wey be embedding of code chunks (R or other languages) and YAML header (which shows how to format output like PDF) inside one Markdown document. So, e act as good writing framework for data science because e allow you put your code, output, and your thoughts down for Markdown. More so, R Markdown documents fit render output formats like PDF, HTML, or Word. One note about quizzes: All quizzes dey for Quiz App folder, get total 52 quizzes with three questions each. Dem link dem from inside the lessons but you fit run the quiz app for your local machine; follow the instruction inside the quiz-app folder make you fit run am local or deploy am to 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 important philosophical issues around fairness dat 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 use Python and Scikit-learn for regression models	Python • R	Jen • Eric Wanjau
06	North American pumpkin prices 🎃	Regression	Make data visual and clean am so e go 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 take use your trained model	Python	Jen
10	Introduction to classification	Classification	Clean, prepare, and visualise 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, prepare, and visualise 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 one simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks wey you suppose do when you dey work 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 this documentation offline by using Docsify. Fork this repo, install Docsify for your local machine, then for the root folder of this repo, type docsify serve. The website go run for port 3000 for your localhost: localhost:3000.

PDFs

Find one pdf of the curriculum with links here.

🎒 Other Courses

Our team dey produce other courses! 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

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Disclaimer:
Dis document na so AI translation service wey dem call Co-op Translator translate am. Even though we dey try make everything correct, abeg sabi say automatic translation fit get some mistakes or no too correct. If you get original document for the original language, na im be correct source. For important information, e better make person wey sabi do translation do am. We no go take responsibility if person no understand well or wrong meaning comot from dis translation.