Introduction

We're living through an odd moment: your calendar can draft emails, your IDE autocompletes whole functions, and your browser chats like a colleague. With the rise of products like ChatGPT in the last few years, the demand for AI skills and AI-powered features has shot up.

Still, none of this came out of thin air. AI and machine learning have been around for a long time. They quietly run spam detection, fare calculation, ranking, and semantic search etc.

So how did we get from those workhorses to systems that write, reason, and explain? To answer that, we have to rewind and follow the thread from simple predictors to modern language models.

In this module, we'll follow that thread step by step — starting with the simplest statistical models, moving through embeddings and neural networks, and ending with transformers and tokenization. You'll see how each breakthrough built on the last, and why these fundamentals still matter when building and deploying AI systems today.