Guide · Open source
How to use ML-For-Beginners: clone the course and work through 26 lessons
Updated · written with AI from the project's own documentation and checked against our catalogue data
ML-For-Beginners is a free, MIT-licensed curriculum from Microsoft Cloud Advocates that teaches classic machine learning, meaning non-deep-learning methods, mainly with the Scikit-learn Python library. It is organised as 12 weeks and 26 lessons, each with quizzes, written instructions, a solution and an assignment. This guide shows you how to get the repository onto your machine and how to work through it in a way that actually sticks.
ML-For-Beginnersmicrosoft/ML-For-Beginners · ★ 91.9k · MIT Install options & downloads Quick start
- 1Clone the repository. Run git clone https://github.com/microsoft/ML-For-Beginners.git in a terminal to download the course.
- 2Fork it (optional). Fork the repo to your own GitHub account so you can keep your own work, as the README suggests for students.
- 3Set up Python. Install Python and check each lesson's dependencies, since the README does not state a Python version.
- 4Start the first lesson. Open the first lesson and begin with the pre-lecture quiz.
- 5Work through each lesson. Read the lecture, do the activities, try to build the project yourself, then take the post-lecture quiz.
- 6Complete the assignment and challenge. Finish the challenge and assignment for each lesson before moving on.
- 7Share progress. After each lesson group, fill in a PAT rubric and post it on the Discussion Board.
What you need before you start
You need Git to download the repository and Python to run the lesson code. The README does not specify a Python version, so check each lesson's requirements when you reach it.
If you do not want to set anything up locally, the README suggests GitHub Codespaces as a browser-based alternative.
- Git
- Python (version not specified in the README)
- Optional: a GitHub account for forking or using Codespaces
Get the course: clone the repository
Run the clone command below to download the full repository. The README's Getting Started section also recommends forking the repo to your own GitHub account first, so you can keep your own notes and work.
If you want a smaller download, the README describes a sparse checkout that skips the 50+ translations. The commands for Bash, macOS and Linux are shown here, and the README gives a CMD version for Windows.
git clone https://github.com/microsoft/ML-For-Beginners.gitSkip the translations with a sparse checkout (optional)
The repository includes 50+ language translations, which increases the download size. The README's sparse checkout method clones without them. Use the Bash, macOS or Linux version below, then run the sparse-checkout command inside the folder.
- Windows users: the README provides a CMD version with double quotes around the paths.
- The README says this still gives you everything you need to complete the course.
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'Follow the study routine for each lesson
The README lays out a fixed routine for every lesson. Following it in order gives you the intended learning loop: check what you know, learn, build, check again, then practise.
Try to build the projects from what you understand rather than by copying the solution code. The solution is available in the /solution folder of each project-oriented lesson when you get stuck.
- Start with the pre-lecture quiz.
- Read the lecture and do the activities, pausing at each knowledge check.
- Try to create the project from the lesson before looking at the solution.
- Take the post-lecture quiz.
- Complete the challenge and the assignment.
- After finishing a lesson group, post a PAT (Progress Assessment Tool) rubric on the Discussion Board.
Use GitHub Codespaces if local setup is a problem
The README's quick-start tip explains how to avoid local Python setup. Open the green Code menu on your fork, select Codespaces and create one. You then install each lesson's required dependencies inside the codespace as needed.
If you hit installation, setup or lesson-running problems, the README points to a TROUBLESHOOTING.md file in the repository.
Use the course as a student or a teacher
Students are asked to fork the whole repo, complete the exercises alone or in a group, and use the Discussion Board to learn out loud. Teachers can use the for-teachers.md file in the repo for suggestions on how to use the curriculum.
For more study, the README recommends the Microsoft Learn modules and learning paths linked from the repository.
What people use it for
- Learning classic machine learning step by step with hands-on projects
- Working through a course with built-in quizzes and assignments
- Teaching a class with a ready-made curriculum, using the teacher suggestions in the repo
- Practising Scikit-learn in Python on datasets from around the world
- Studying alongside the Data Science for Beginners curriculum, which the README recommends pairing with this course
Frequently asked questions
Is ML-For-Beginners free?
The repository is published under the MIT licence, and the README describes it as a free curriculum from Microsoft Cloud Advocates. Nothing in the README mentions a paid tier.
What do I need to install to use ML-For-Beginners?
Git and Python are the stated prerequisites. The README does not specify a Python version, so check the requirements for each lesson as you go. Codespaces is an option if you want to avoid local setup.
Does ML-For-Beginners cover deep learning?
No. The README says it focuses on classic machine learning and avoids deep learning, which is covered in the separate AI for Beginners curriculum.
Is ML-For-Beginners a library I can add to my app?
No. It is a course with lessons, quizzes and assignments, not a software library or product to integrate into an application.
What can I use instead of ML-For-Beginners?
Comparable options include Google's Machine Learning Crash Course, fast.ai course materials and the Scikit-learn official tutorials. Pick based on whether you want a structured course with quizzes or a more tool-focused guide.
Where do I get help if the lessons won't run?
The README points to a TROUBLESHOOTING.md file in the repository for common issues with installation, setup and running lessons.
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Our verdict
ML-For-Beginners suits beginners who want a free, structured, self-paced path through classic machine learning with Scikit-learn, and teachers who want a ready-made curriculum. If you need deep learning, neural networks or a library to build into an app, look at the AI for Beginners curriculum, a software library or a different course such as Google's Machine Learning Crash Course.
Install options & downloads