What is the difference between supervised, unsupervised and reinforcement learning?

• Supervised learning — you have labelled examples, and the model learns to map inputs to known outputs. Split into classification (discrete labels: spam or not, which of five categories) and regression (continuous values: price, temperature, demand). This covers the large majority of applied ML because business problems usually come with historical outcomes.
• Unsupervised learning — no labels; the model finds structure in the data itself. Clustering (k-means, DBSCAN, hierarchical) groups similar records; dimensionality reduction (PCA, t-SNE, UMAP) compresses features while preserving structure; association rules find items that co-occur. Used for customer segmentation, anomaly detection, and exploration.
• Reinforcement learning — an agent takes actions in an environment and learns from rewards. There are no correct answers given, only feedback on outcomes, and the agent must balance exploring new actions against exploiting what it knows. Used in robotics, game playing, recommendation sequencing, and RLHF for language models.

Note: Semi-supervised and self-supervised learning are worth mentioning. Self-supervised in particular — creating labels from the data itself, such as predicting a masked word — is what made modern language models possible, since it removes the labelling bottleneck entirely.