Tutorials
How to Navigate
If you’re a complete beginner or have some scattered knowledge and want to build a solid, well-rounded understanding of machine learning, I recommend starting with the math courses. They’ll give you a strong foundation to confidently explore more advanced topics later on.
That said, if you’re mainly interested in a specific area—like deep learning—you’re welcome to jump straight to it. Each course is designed to stand on its own, so you can learn in any order and revisit topics whenever you need.
You can explore the full course overview to see everything we’ll cover—just click any topic to go directly to the section that interests you.
Math Courses
Linear Algebra
Linear algebra is a core foundation of machine learning. From simple regression to deep neural networks, almost every algorithm relies on it to represent and process data efficiently.
It gives us the tools to work with vectors and matrices, the basic structures used to describe data, model parameters, and transformations.
This course starts from scratch and builds your intuition step by step, so you can confidently apply linear algebra concepts in real machine learning algorithms.
Course Overview
- Motivation
- Defintions
- Pillars of Machine Learning
- Getting Started With Python
- Writing Your First Python Code
- Wrap-up