What is the difference between supervised and unsupervised learning?

Answer

Supervised learning trains a model on labeled examples, where each input has a known target, so the model learns to predict labels for new data in tasks like classification and regression. Unsupervised learning works with unlabeled data and finds structure on its own, such as grouping similar items through clustering or reducing dimensionality. Supervised learning needs costly labeled datasets but gives precise targets, while unsupervised learning explores patterns without labels.