Paths/AI & ML

AI & MLintermediatev2.0.0

Machine Learning & AI

Build deep understanding of ML from mathematical foundations to practical implementation. Learn to think about learning algorithms, not just use them.

25 hours2 modules3 lessonsML Education Initiative

What you will learn

Understand the mathematical foundations of supervised learning
Implement core algorithms from scratch to build intuition
Develop frameworks for model selection, evaluation, and debugging
Build intuition for when and why different algorithms work

Prerequisites

Python programming (functions, classes, numpy basics)
High school math: algebra, basic calculus (derivatives), basic statistics (mean, variance)
Curiosity about how machines can 'learn' from data

Syllabus

What Does It Mean for a Machine to Learn?

Define machine learning formally and distinguish it from traditional programming.

Linear Regression: The Foundation

Derive, implement, and interpret linear regression, understanding it as the prototype of all supervised learning.