What Is Machine Learning?
Instead of writing rules, show the computer examples. The core idea of machine learning, its three main types and the standard workflow.
📄 Illustrated notes · every chapter as a picture · printable
Quick quiz
3 questions to check your understanding.
Go deeper
University-level written lectures in The AI Lecture Hall:
Transcript
Introduction. Traditional programs follow rules that a programmer writes. Machine learning flips this around: we show the computer examples, and it works out the rules itself.
Rules vs examples. In traditional programming, you give the computer data and rules, and get answers. In machine learning, you give it data and the correct answers, and it produces the rules, which we call a model.
Types of learning. There are three main types. Supervised learning uses labelled examples. Unsupervised learning finds structure without labels. Reinforcement learning learns by trial and error, guided by rewards.
A first example. Here is supervised learning in action. Each dot is a student: hours studied and exam score. The model starts with a bad guess and adjusts itself until its line fits the data well. That adjusting process is called training.
Workflow. Real projects follow a workflow. Collect data. Prepare features. Train the model. Evaluate it on data it has never seen. Then deploy it and keep monitoring, because the world changes.
Recap. To recap. Machine learning learns rules from examples. There are three main types. Training adjusts the model to reduce its errors. And we always judge a model on data it has never seen.