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Machine Learning for Beginners - A Curriculum

🌍 Make we waka round di world as we dey explore Machine Learning through different cultures 🌍

Cloud Advocates for Microsoft dey happy to give una one 12-week, 26-lesson curriculum wey dey all about Machine Learning. For dis curriculum, you go learn wetin people dey call classic machine learning, we go mainly use Scikit-learn as library and sidon comot deep learning, wey dey inside our AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum too!

Waka with us round di world as we apply these classic techniques to data wey come from plenty different places. Every lesson get pre- and post-lesson quizzes, written instructions to finish di lesson, one solution, one assignment, and more. Our project-based way of teaching make you dey learn as you dey build — na better way make new skills hold.

✍️ Big tnx 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

🎨 Tnx to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper

🙏 Special tnx 🙏 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 tnx to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!

How to start

Follow these steps:

1. Fork di Repository: Click on the "Fork" button for di top-right corner of dis page.
2. Clone di 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 this curriculum, fork the whole repo to your own GitHub account and do the exercises on your own or with group:

• Start wit pre-lecture quiz.
• Read di lecture and do di activities, stop small and reason for each knowledge check.
• Try build di projects by understanding di lessons instead of just running di solution code; still di code dey for /solution folders for each project-oriented lesson.
• Do di post-lecture quiz.
• Finish di challenge.
• Finish di assignment.
• After you don finish one lesson group, visit di Discussion Board and "learn out loud" by filling di correct PAT rubric. PAT na Progress Assessment Tool — na one rubric wey you go fill to help your learning. You fit also react to other PATs so we all fit learn together.

If you wan study more, we recommend make you follow these Microsoft Learn modules and learnin paths.

Teachers, we don include some suggestions on how make you use this curriculum.

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Video walkthroughs

Some lessons get short video form. You fit find dem 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 image wey dey above for video about di project and di people wey build am!

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Pedagogy

We pick two teaching principles when we dey build this curriculum: make am hands-on and project-based, and include frequent quizzes. Also, dis curriculum get one common theme to hold everything together.

By make content follow projects, students go dey more engaged and dem go remember di concepts well. Plus, one low-stakes quiz before class dey set student intention, and another quiz after class go help memory stay. This curriculum make am flexible and fun — you fit take am full or small part. Projects start small and dem go dey more complex as di 12-week cycle dey finish. E still get one postscript about how ML dey work for real world, wey teachers fit use as extra credit or base for discussion.

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

A note about languages: These lessons mainly dem write for Python, but many still dey available for R. To finish an R lesson, go to di /solution folder and find di R lessons. Dem get an .rmd extension wey mean R Markdown file — na basically Markdown wey fit hold code chunks (R or other languages) and one YAML header (wey dey guide how outputs go format like PDF). As e be, e good for data science authoring cos e allow you join your code, di output, and your notes inside Markdown. R Markdown documents fit render go output formats like PDF, HTML, or Word.

A note about quizzes: All quizzes dey inside di Quiz App folder, total 52 quizzes, each get three questions. Dem link the quizzes from di lessons but you fit run di quiz app locally; follow di instruction inside the quiz-app folder to host am locally or deploy to Azure.

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Intro wey dey explain machine learning	Introduction	Learn di basic concepts wey dey behind machine learning	Lesson	Muhammad
02	Di 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 mata about fairness wey students suppose consider when dem dey build an apply ML models?	Lesson	Tomomi
04	Techniques wey dem dey use for machine learning	Introduction	Which 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	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 wey go 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 dey 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 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 a simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks wey dey necessary 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 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

Access wey no need internet

You fit run dis documentation offline by using Docsify. Fork dis repo, install Docsify for your local machine, and then for the root folder of dis repo, type docsify serve. Di website go dey served for port 3000 on your localhost: localhost:3000.

PDFs

Find pdf of di curriculum wit links here.

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How to Get Help

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Abeg note: We use AI translation service (Co-op Translator: https://github.com/Azure/co-op-translator) take translate dis document. Even though we dey try make am correct, make you sabi say automatic translations fit get mistakes or wrong parts. The original document for im own language na the main source wey get authority. If na important information, make you use professional human translator. We no be liable for any misunderstanding or misinterpretation wey fit happen because of this translation.