ML-For-Beginners

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🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Kannada | Khmer | Korean | Lithuanian | Malay | Malayalam | Marathi | Nepali | Nigerian Pidgin | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Telugu | Thai | Turkish | Ukrainian | Urdu | Vietnamese

Prefer to Clone Locally?

This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:

Bash / macOS / Linux:

```bash

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' '!translatedimages'

```

CMD (Windows):

```cmd

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" "!translatedimages"

```

This gives you everything you need to complete the course with a much faster download.

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Machine Learning for Beginners - A Curriculum

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

Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!

Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

✍️ Hearty 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, notably 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:

1. Fork the Repository: Click on the "Fork" button at the top-right corner of this 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 this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:

• Start with a pre-lecture quiz.
• Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
• Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
• Take the post-lecture quiz.
• Complete the challenge.
• Complete the assignment.
• After completing a lesson group, visit the Discussion Board and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.

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

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

Meet the Team

Promo video

Gif by Mohit Jaisal

🎥 Click the image above for a video about the project and the folks who created it!

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Pedagogy

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes. In addition, this curriculum has a common theme to give it cohesion.

By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.

Find our Code of Conduct, Contributing, Translations, 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 the project
• knowledge checks
• a challenge
• supplemental reading
• assignment
• post-lecture quiz

A note about languages: These lessons are primarily written in Python, but many are also available