"AI" gets used to describe everything from a chess engine to a chatbot, which makes it a confusing term. Here is what it actually means, without the hype.
The core idea: learning from examples, not explicit rules
Traditional programming means a human writes exact rules: "if the email contains these words, mark it spam." Machine learning flips this - you show the system thousands of examples of spam and non-spam email, and it learns the patterns itself, without a human writing the rules by hand.
Machine learning vs AI vs deep learning
AI is the broad goal: getting computers to do things that normally require human intelligence. Machine learning is the dominant modern approach to AI - learning patterns from data. Deep learning is a specific technique within machine learning, using layered neural networks, and it is what powers the large language models behind tools like ChatGPT.
A simple mental model
Think of a machine learning model as a function with millions or billions of internal numbers ("parameters") tuned during training so that, given an input, it produces a useful output - text, a classification, a prediction. Training is the slow, expensive process of tuning those numbers on huge amounts of data; using the trained model afterward (called inference) is comparatively fast and cheap.
Why this matters for you as a developer
You do not need to train models from scratch to build useful AI features. Almost all practical AI development today means calling an existing trained model through an API - the same way you would call any other web API, covered in the next few lessons.