ML for Beginners review
Microsoft · microsoft
A free, open-source curriculum on classic machine learning with scikit-learn, taught through small projects.
What does it take?
| Difficulty | 2 out of 5 |
|---|---|
| Hours | Not checked yet |
| Format | Reading, Code notebooks, Projects |
| Maths needed | School-level maths |
| Coding needed | Yes, you will write code |
| Hardware needed | Not checked yet |
| Certificate | No certificate |
| Free to audit | Yes |
| Pace | Self-paced |
| Last updated by the provider | Not checked yet |
Who should take it, and who should skip it?
Take it if you are
- People comfortable reading a GitHub repo
- Self-starters who like project-based lessons
Skip it if you are
- Anyone who needs video lectures
- People looking for deep learning
What will you be able to do after it?
- Train regression, classification and clustering models
- Clean and prepare a real dataset
- Pick a sensible algorithm for a problem
What's in each module?
| Module | What it 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 we liked, and what we didn't
What we liked
- Completely free and open source
- Lots of small projects
- Quizzes after every lesson
What we didn't
- You need to be comfortable with GitHub
- No certificate
- No deep learning
What's the catch?
It's a GitHub repository, not a course platform, so you have to motivate yourself.
What should you take after it?
- Kaggle Intro to Machine Learning
Kaggle's 7-lesson first machine learning course takes about 3 hours, costs nothing and ends with a competition. Kaggle gives a free completion certificate.
- Machine Learning Specialization
The Machine Learning Specialization has 3 courses and 95 hours of Andrew Ng's lessons. Coursera's FAQ says you cannot take it for free, so check the cost first.
How we rated it: 7.9 out of 10
| Criterion | Score out of 10 |
|---|---|
| Clarity | 8 |
| Hands-on practice | 9 |
| Up to date | 7 |
| Depth | 7 |
| Truly free | 10 |
So should you take it?
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.
Where to go next
- Microsoft Learn: our platform review
What else it offers, and what costs money.
- Kaggle Learn: Python
Kaggle's 7-lesson Python course takes about 5 hours, costs nothing and pairs every lesson with an exercise notebook. Kaggle gives a free completion certificate.
- Machine Learning Crash Course
Google's Machine Learning Crash Course has 12 modules and takes about 13.5 hours by Google's own timings. It is free, with quiz badges and no certificate.
- AI for Beginners
A 24-lesson open curriculum on GitHub covering classic AI, ML and a little deep learning.
Last checked .
Not sure this is the one?
Answer two questions and we'll point you to the course that fits where you are today.