AI Fundamentals
A free, from-scratch guide to artificial intelligence: the math, the core machine learning ideas, neural networks, computer vision, and natural language processing — each module has diagrams, a hands-on interactive demo, and a short quiz. Everything here is free to read for anyone; sign in only if you'd like your progress tracked.
12 modules — sign in to track your progress
1
What Is Artificial Intelligence?
Definitions, narrow vs. general AI, and how AI, machine learning, and deep learning relate.
2
The Math Toolkit: Vectors & Matrices
The handful of linear algebra ideas that everything else in this guide builds on.
3
How Machines Learn From Data
Features, labels, training data vs. test data, and why bad data means a bad model.
4
The Three Types of Machine Learning
Supervised, unsupervised, and reinforcement learning, and when each one is used.
5
Your First Model: Linear Regression
Fit a line to data by hand and watch the error shrink as you improve it.
6
Teaching a Machine to Classify
Sort points into categories with k-Nearest Neighbors and see decision boundaries form.
7
Neural Networks: The Basics
Neurons, weights, biases, and activation functions, built up from the dot product.
8
How Networks Learn: Loss & Gradient Descent
The hill-climbing (well, hill-descending) algorithm that trains almost every model.
9
Computer Vision & Image Processing
Images are just grids of numbers. Filters, convolutions, and what a CNN actually sees.
10
Natural Language Processing & Language Models
Tokens, embeddings, and how models like Claude predict the next word.
11
AI Ethics, Bias & Limitations
Bias, hallucination, and a practical checklist for using AI responsibly.
12
Where to Go Next
A glossary and a curated list of free, authoritative resources to go deeper.
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