A machine learning approach that finds patterns and structure in data that has no labels or predefined answers.
In unsupervised learning, the algorithm is given raw data and must discover structure by itself. A common task is clustering, which groups similar items together. An online store might cluster customers by purchasing behavior and discover segments such as bargain hunters or frequent gift buyers without anyone defining those groups in advance.
Other unsupervised techniques include dimensionality reduction, which simplifies complex data while keeping important patterns, often for visualization, and anomaly detection, which flags unusual events such as suspicious transactions or failing sensors. Results can be harder to evaluate than in supervised learning because there is no correct answer to compare against. Self-supervised learning, used to pre-train large language models, creates its own labels from unlabeled data.
Datasets so large, fast-moving, or varied that traditional tools struggle to store and process them efficiently.
Machine LearningA branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case.
Supervised LearningA machine learning approach where a model learns from labeled examples, each pairing an input with the correct output.
A plain-English guide to reinforcement learning: agents, environments, rewards and policies, with game, robotics and chatbot examples and where it fits in business.