Machine learning is a branch of artificial intelligence in which computers improve their performance on a task by finding patterns in data — rather than by following explicit, hand-written instructions for every situation.
Learning from data instead of rules
In traditional programming, a developer writes the rules: if this, then that. In machine learning, the developer provides examples and a learning algorithm, and the system derives its own rules. Show a model thousands of labelled emails, and it learns to distinguish spam from legitimate mail — including tricks its programmers never anticipated.
The three main types
Supervised learning trains on labelled examples (photos tagged "cat" or "dog") to predict labels for new inputs. Unsupervised learning looks for structure in unlabelled data, such as grouping customers with similar behaviour. Reinforcement learning trains an agent through trial and error, rewarding actions that lead to good outcomes — the approach behind game-playing AIs and some robotics.
Where you encounter it
Machine learning is embedded in everyday products: search ranking, recommendation feeds, voice assistants, fraud detection, medical image screening, translation, predictive text and navigation ETAs. Behind the scenes it also optimizes logistics, energy grids and manufacturing quality control.
Data quality and limits
A model's abilities are bounded by its training data. Biased, incomplete or stale data produces biased, unreliable predictions — the principle of "garbage in, garbage out." Machine learning systems also struggle with situations unlike anything in their training data, which is why human oversight remains essential for high-stakes decisions.