Machine learning
Machine learning
Regression, classification, resampling, trees and model selection, with honest attention to what can go wrong.
About 60 hours of study.
Where to learn it
Read
An Introduction to Statistical Learning, with Python (ISLP)
James, Witten, Hastie, Tibshirani, Taylor
Chapters 2–9
Section not checked yet
Watch
Introduction to Machine Learning (NPTEL)
Balaraman Ravindran, IIT Madras
The full course
A rigorous IIT Madras course that suits a maths background.
Section not checked yet
Practise
Kaggle Learn
Kaggle
Intro to Machine Learning, then Intermediate Machine Learning
Short hands-on courses that run in the browser.
Other options
- Mathematics for Machine Learning (read, to go deeper)
Part I, if your linear algebra or probability is rusty - Machine Learning Specialization (Coursera) (watch)
All three courses - Kaggle competitions (practise)
The Getting Started competitions
Wrong chapter, dead link, or know something better? Tell us on GitHub.
Which roles ask for it
- Data analyst good to have
- Machine learning engineer to get hired
- Deep learning engineer (vision / language) to get hired
- Quantitative researcher / analyst to get hired
- Risk analyst good to have
- Economist / economic research analyst good to have
- Statistician / biostatistician in the first year
- MLOps engineer to get hired
- Product analyst good to have
- Data scientist to get hired
- AI / LLM engineer in the first year
- GATE data science and AI to get hired