# ML for Beginners

Source: https://freeaicourses.com/courses/ml-for-beginners
Last checked: 2026-09-15

## Who it is for

- People comfortable reading a GitHub repo
- Self-starters who like project-based lessons

## Who should skip it

- Anyone who needs video lectures
- People looking for deep learning

## What you can do afterwards

- Train regression, classification and clustering models
- Clean and prepare a real dataset
- Pick a sensible algorithm for a problem

## What the course covers

- Introduction and regression: What ML is and how to fit a line to data.
- Classification and clustering: Sorting things into groups, with and without labels.
- NLP, time series and reinforcement learning: Short tours of more specialised areas.

## What works

- Completely free and open source
- Lots of small projects
- Quizzes after every lesson

## What does not

- You need to be comfortable with GitHub
- No certificate
- No deep learning

## The catch

It's a GitHub repository, not a course platform, so you have to motivate yourself.

## Take after

- Kaggle Intro to Machine Learning
- Machine Learning Specialization

## Verdict

The best free option if you learn by working through lessons at your own pace in a code editor. It covers classic ML rather than neural networks, which is what most real-world data work still uses.

## Related pages

- [Kaggle Intro to Machine Learning](https://freeaicourses.com/courses/kaggle-intro-machine-learning)
- [Machine Learning Specialization](https://freeaicourses.com/courses/machine-learning-specialization)
- [Microsoft Learn](https://freeaicourses.com/platforms/microsoft)
- [All free AI courses, rated](https://freeaicourses.com/courses)