Machine Learning (ML)

A subset of AI that involves training algorithms to learn from data and make predictions.

How it works

Machine learning systems learn by example rather than by explicit programming. A training dataset of input-output pairs is used to optimise a model's parameters — its internal representation of patterns — by minimising a loss function that measures prediction error. The learned model can then generalise to new, unseen inputs. Three major paradigms exist: supervised learning (labelled data), unsupervised learning (unlabelled data), and reinforcement learning (learning from interaction and reward).

Why it matters

Machine learning has made previously intractable problems tractable by replacing brittle hand-written rules with systems that learn from data. Spam filters, fraud detection, medical imaging, and recommendation engines all rely on ML. It is the engine of the modern AI industry and a foundational skill for any engineer or data scientist working on intelligent systems. The transition from rule-based to ML-based systems is often the defining upgrade that makes a product genuinely useful at scale.