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Machine Learning for Beginners - A Curriculum

🌍 Travel around the world as we explore Machine Learning through world cultures 🌍

Microsoft's Cloud Advocates are excited to present a 12-week, 26-lesson curriculum focused on Machine Learning. This curriculum introduces you to classic machine learning, primarily using the Scikit-learn library, while avoiding deep learning (covered in our AI for Beginners' curriculum). Pair these lessons with our 'Data Science for Beginners' curriculum for a comprehensive learning experience!

Join us on a journey around the globe as we apply these techniques to datasets from various regions. Each lesson includes pre- and post-lesson quizzes, step-by-step instructions, solutions, assignments, and more. Our project-based approach ensures you learn by building, a proven method for retaining new skills.

✍️ Special 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 contributors, including 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 these steps:

Fork the Repository: Click the "Fork" button at the top-right corner of this page.
Clone the Repository: git clone https://github.com/microsoft/ML-For-Beginners.git

Find additional resources for this course in our Microsoft Learn collection

Students, to use this curriculum, fork the entire repo to your GitHub account and complete the exercises individually or in groups:

Start with a pre-lecture quiz.
Read the lecture and complete the activities, pausing to reflect at each knowledge check.
Try building the projects by understanding the lessons rather than running the solution code (available in the /solution folders for project-based lessons).
Take the post-lecture quiz.
Complete the challenge.
Finish the assignment.
After completing a lesson group, visit the Discussion Board and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' (Progress Assessment Tool) is a rubric to further your learning. You can also engage with other PATs to learn collaboratively.

For further study, we recommend exploring these Microsoft Learn modules and learning paths.

Teachers, we have included some suggestions on how to use this curriculum.

Video walkthroughs

Some lessons are available as short-form videos. You can find these embedded in the lessons or on the ML for Beginners playlist on the Microsoft Developer YouTube channel by clicking the image below.

Meet the Team

Gif by Mohit Jaisal

🎥 Click the image above for a video about the project and the team behind it!

Pedagogy

This curriculum is built on two pedagogical principles: hands-on project-based learning and frequent quizzes. Additionally, it follows a common theme for cohesion.

By aligning content with projects, the learning process becomes more engaging, and concept retention improves. Low-stakes quizzes before a class set the student's intention for learning, while post-class quizzes reinforce retention. This curriculum is designed to be flexible and enjoyable, allowing students to take it in full or in part. Projects start small and grow in complexity over the 12-week cycle. A postscript on real-world ML applications is included for extra credit or discussion.

Find our Code of Conduct, Contributing, and Translation 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
Step-by-step guides for project-based lessons
Knowledge checks
A challenge
Supplemental reading
Assignment
Post-lecture quiz

A note about languages: Lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the /solution folder and look for R lessons. These include an .rmd extension, representing R Markdown files. R Markdown combines code chunks (R or other languages), a YAML header (for formatting outputs like PDFs), and Markdown text. It’s an excellent framework for data science, allowing you to integrate code, output, and commentary. R Markdown documents can be rendered into formats like PDF, HTML, or Word.

A note about quizzes: All quizzes are in the Quiz App folder, totaling 52 quizzes with three questions each. They are linked within lessons but can also be run locally. Follow the instructions in the quiz-app folder to host locally or deploy 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	What are the key philosophical considerations about fairness that students should keep in mind when developing and using ML models?	Lesson	Tomomi
04	Techniques for machine learning	Introduction	What methods do ML researchers use to create ML models?	Lesson	Chris and Jen
05	Introduction to regression	Regression	Learn the basics of 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	Create linear and polynomial regression models	Python • R	Jen and Dmitry • Eric Wanjau
08	North American pumpkin prices 🎃	Regression	Develop a logistic regression model	Python • R	Jen • Eric Wanjau
09	A Web App 🔌	Web App	Build a web application to utilize 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	Additional classifiers	Python • R	Jen and Cassie • Eric Wanjau
13	Delicious Asian and Indian cuisines 🍜	Classification	Create a recommender web app using 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	Learn about the K-Means clustering method	Python • R	Jen • Eric Wanjau
16	Introduction to natural language processing ☕️	Natural language processing	Understand the basics of NLP by creating a simple bot	Python	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Expand your NLP knowledge by exploring common tasks involved in processing language structures	Python	Stephen
18	Translation and sentiment analysis ♥️	Natural language processing	Perform translation and sentiment analysis using Jane Austen's works	Python	Stephen
19	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews (Part 1)	Python	Stephen
20	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews (Part 2)	Python	Stephen
21	Introduction to time series forecasting	Time series	Learn the basics of time series forecasting	Python	Francesca
22	⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA	Time series	Time series forecasting using ARIMA	Python	Francesca
23	⚡️ World Power Usage ⚡️ - time series forecasting with SVR	Time series	Time series forecasting using Support Vector Regressor	Python	Anirban
24	Introduction to reinforcement learning	Reinforcement learning	Learn the basics of reinforcement learning with Q-Learning	Python	Dmitry
25	Help Peter avoid the wolf! 🐺	Reinforcement learning	Reinforcement learning with Gym	Python	Dmitry
Postscript	Real-World ML scenarios and applications	ML in the Wild	Fascinating and insightful real-world applications of classical ML	Lesson	Team
Postscript	Model Debugging in ML using RAI dashboard	ML in the Wild	Debugging ML models using Responsible AI dashboard components	Lesson	Ruth Yakubu

Find all additional resources for this course in our Microsoft Learn collection

Offline access

You can access this documentation offline using Docsify. Fork this repository, install Docsify on your local machine, and then in the root folder of this repository, type docsify serve. The website will be available on port 3000 at localhost:3000.

PDFs

Download a PDF version of the curriculum with links here.

🎒 Other Courses

Our team offers other courses! Check out:

Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we aim for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.

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