Start here
Everything on this site is free and self-paced: 12 modules that take you from linear regression to transformers and generative AI. This page tells you what to know before Module 1 and how to get the most out of the course.
- Calculus: multivariable derivatives, the chain rule, and gradient computation
- Linear algebra: vectors, matrices, eigenvalues, rank, and least squares
- Probability & statistics: distributions, expectations, maximum likelihood estimation
- Programming: Python fluency with NumPy; comfort writing and debugging functions
- ML fundamentals: supervised vs. unsupervised learning, overfitting, train/test splits
Rusty on any of these?
Spend a few days with these free refreshers before starting — Module 1 will feel much smoother:
Every module page is a complete, self-contained hub. Work through it in this order:
- 1Watch — the module's lecture videos (most are board lectures — have pen and paper ready).
- 2Read — the linked course-book chapters first, then use the alternative readings where useful.
- 3Self-check — answer the "Test your understanding" questions before revealing the answers.
- 4Code — open the module's Colab notebook and run/modify the lecture code yourself.
Reviewed chapters from the free course book are the primary reading, and a module may link several chapters because the two structures do not map one-to-one. The exact D2L sections remain as alternatives. The linked course-book route now spans every module; slides and papers remain available when a second treatment is useful.
Suggested pacing: budget 5–7 focused hours per module, roughly one module every 1–2 weeks. The modules build on each other — resist skipping ahead unless the prerequisites note on a module page says you're clear.
The fastest path is Google Colab — free GPU access, nothing to install, and every module's notebook opens directly in it. Prefer working locally? The setup guide covers Python, PyTorch, and Jupyter.