AI Course

Artificial intelligence gets used as a label for everything from a chess engine to a chatbot, which makes it hard to know what's actually being talked about. This course builds a real foundation: what AI actually is, the core techniques underneath the term, and the tradeoffs and failure modes that don't show up in the marketing.

What you'll learn

You'll start with the basics of what "intelligence" even means in this context, and a fast, concrete tour of how the field got from hand-coded rules to the deep learning systems that dominate today. From there the course moves through the core techniques one at a time: search-based problem solving, machine learning's core supervised/unsupervised split, how a neural network actually computes something, and a grounded look at the two areas most people encounter AI in practice — language and vision.

This course is deliberately conceptual rather than code-heavy — a few small, real Python examples appear where they genuinely clarify an idea, but the goal is a solid mental model of how these systems work and where they break, not implementation from scratch. The last two lessons cover what doesn't get enough attention: bias and fairness as real, measurable problems, and an honest look at where the field is actually headed versus where the hype says it's headed.

Lessons

  1. What Is Artificial Intelligence?
  2. A Brief History of AI
  3. Search & Problem Solving
  4. Machine Learning Basics
  5. Supervised vs Unsupervised Learning
  6. Neural Networks Basics
  7. Natural Language Processing Basics
  8. Computer Vision Basics
  9. AI Ethics & Bias
  10. Where AI Is Headed
Where to start: begin with What Is Artificial Intelligence? and go in order — later lessons build on the search and learning concepts introduced early on, especially lessons 4 through 6.