ML-For-Beginners
A free 12-week, 26-lesson beginner course on classic machine learning using Scikit-learn.
- Stars
- 91.9k
- Forks
- 22.8k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 5 days ago
- Type
- Learning resource
What it is
This is a free, open curriculum from Microsoft Cloud Advocates that teaches classic machine learning, meaning non-deep-learning methods, mainly with the Scikit-learn Python library. It is organized as 26 lessons over 12 weeks, and each lesson includes quizzes, written instructions, a solution, and an assignment. People use it to learn ML step by step through hands-on projects.
- Work through the lessons in order, completing quizzes and assignments
- Follow the project-based lessons to build small ML projects
- Use the /solution folders to check your work after attempting a lesson
- Pair it with the Data Science for Beginners curriculum, as the README suggests
Great for
- Beginners who want a structured, self-paced introduction to machine learning
- Students working through a course with quizzes and assignments
- Teachers looking for a ready-made curriculum
- Learners who want to practice with Scikit-learn in Python
Not ideal for
- People who want deep learning or neural networks, since the README says that topic is covered in a separate AI for Beginners curriculum
- Readers looking for a software product or library to add to an app, since this is a course
How to get it
- 1
Clone and build from source
You'll need: Git · Python (version not specified in the README)
Clone the repository
git clone https://github.com/microsoft/ML-For-Beginners.git - 2
Run it
- Fork the repository to your own GitHub account if you want to complete the exercises there
- Clone the repository to your computer, or open it in GitHub Codespaces from the Code menu
- Start with the pre-lecture quiz in the first lesson
- Read the lesson, complete the activities, then take the post-lecture quiz and assignment
- 3
Get the most out of it
- Use GitHub Codespaces to run it in the browser without local Python setup
- Use the sparse checkout commands in the README to download without translations
- Check the Troubleshooting Guide for common setup issues
- Post your progress on the Discussion Board using the PAT rubric after finishing a lesson group
- Follow the linked Microsoft Learn modules for further study
Downloads
Strengths
- Free and MIT-licensed, with a large and active community of learners
- Lessons include pre- and post-lesson quizzes and project-based assignments
- Available in many translations, with an option to sparse-clone to reduce download size
Trade-offs
- It focuses on classic ML and deliberately avoids deep learning
- The README does not state prerequisite Python knowledge, so assume some is needed
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| ML-For-Beginners (this) | 91.9k | Learning resource | beginner | 5 days ago | Beginners who want a structured, self-paced introduction to machine learning |
| python-machine-learning-book | 12.7k | Learning resource | beginner | 4 months ago | Readers who own the 1st edition and want to run its code |
| homemade-machine-learning | 24.8k | Learning resource | beginner | 12 months ago | Students learning the math behind machine learning algorithms |
| netron | 33.6k | App | beginner | 4 days ago | Engineers who need to check a model's layers and tensor connections before deploying it |
| nni | 14.4k | Library | intermediate | 2.3 years ago | Researchers comparing hyperparameter tuning algorithms such as TPE, Hyperband, or Bayesian methods |
| AI-For-Beginners | 70.5k | Learning resource | beginner | 13 days ago | Beginners who want a structured, self-paced introduction to AI |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.