ML-For-Beginners/translations/pcm

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Prefer to Clone Locally?

Dis repo get 50+ language translations wey dey make di download size big pass. To clone without di translation dem, 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 fast.

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Machine Learning for Beginners - A Curriculum

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

Cloud Advocates for Microsoft happy to offer 12-week, 26-lesson curriculum wey dey all about Machine Learning. Inside dis curriculum, you go learn wetin dem dey call classic machine learning, mainly using Scikit-learn as library and no go enter deep learning wey dey inside our AI for Beginners' curriculum. You fit also pair dis lessons with our 'Data Science for Beginners' curriculum.

Travel with us around di world as we apply di 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 methodology allow you to learn as you dey build, na better 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 also go 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 gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!

Getting Started

Follow dis 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 di extra resources for dis course inside our Microsoft Learn collection

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

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

• Start wit pre-lecture quiz.
• Read di lecture and complete di activities, pause and think for every knowledge check.
• Try make you create di projects by understanding di lessons well instead of just running di solution code; but if you want, dat code dey available inside di /solution folders for each project-oriented lesson.
• Take di post-lecture quiz.
• Complete di challenge.
• Complete di assignment.
• After you don finish one lesson group, go visit di Discussion Board, and "learn out loud" by filling di correct PAT rubric. PAT na Progress Assessment Tool, na rubric wey you fill to help your learning grow. You fit also respond to other PATs so we all fit learn together.

For more study, we recommend 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 get short video form. You fit find all dese inside di lessons or for di ML for Beginners playlist on the Microsoft Developer YouTube channel by clicking di image below.

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Meet the Team

Gif by Mohit Jaisal

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

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Pedagogy

We choose two teaching principles as we build dis curriculum: to make am hands-on project-based and get frequent quizzes inside. Plus, dis curriculum get one common theme to make am balanced.

By making sure say content match projects, e go make learning more interesting for students and e go help concepts stay for head well. Plus, low-stakes quiz before class dey help student focus to learn di topic, and another quiz after class go help keep di memory strong. Dis curriculum dey flexible and fun, and you fit take am whole or part. Projects start small and get more complex as di 12-week cycle dey end. Dis curriculum even get one postscript about real-world ways di ML fit take, for extra credit or discussion base.

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

Each lesson get

• 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
• supplemental reading
• assignment
• post-lecture quiz

Note about languages: Most lessons dey for Python, but plenty still dey for R too. To finish one R lesson, waka go /solution folder and find R lessons. Dem get .rmd extension wey mean R Markdown file, wey be embedding of code chunks (fit be R or other languages) and YAML header (wey show how to format outputs like PDF) for one Markdown document. So, e be one correct way make you combine your code, wetin e produce, and your thoughts, by writing all down for Markdown. Plus, R Markdown documents fit convert to output formats like PDF, HTML, or Word. One tok bout quizzes: All quizzes dey inside Quiz App folder, get 52 quizzes for three questions each. Dem link from inside di lessons but you fit run di quiz app for your own machine; follow di instruction wey dey inside di quiz-app folder make you fit host am local or deploy am for Azure.

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Introduction to machine learning	Introduction	Learn di basic tins wey dey back machine learning	Lesson	Muhammad
02	The History of machine learning	Introduction	Learn di history wey dey back dis kain field	Lesson	Jen and Amy
03	Fairness and machine learning	Introduction	Wetin be di important philosophical tins about fairness wey students suppose consider wen dem dey build and use ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	Wetin kain techniques ML researchers dey use make dem 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 diagram and clean data before 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 wey go use your trained model	Python	Jen
10	Introduction to classification	Classification	Clean, prepare, and make diagram of 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 wit your model	Python	Jen
14	Introduction to clustering	Clustering	Clean, prepare, and draw diagram for 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 basics about NLP by building simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deep your NLP knowledge by understanding common tasks wey dem need sometimes for 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 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 true true 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 dis documentation offline by using Docsify. Fork dis repo, install Docsify for your own machine, then for di root folder of dis repo, type docsify serve. Di website go dey for port 3000 for your localhost: localhost:3000.

PDFs

Find pdf of di curriculum with links here.

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Getting Help

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Disclaimer: Dis document dem translate am wit AI translation service Co-op Translator. Even though we dey try make am correct, abeg sabi say automated translation fit get some errors or wahala. Di original document wey dey dia for e own language na im be di main correct source. If na serious matter, better make person wey sabi language translate am. We no go take any blame if person no understand or misunderstand tins because of dis translation.