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In supervised learning, the algorithm learns from labeled data, where each input is associated with a corresponding output or target. The goal is to learn a mapping from inputs to outputs, making predictions on new, unseen data. Examples include classification and regression tasks.
In unsupervised learning, the algorithm learns from unlabeled data, where there are no predefined output labels. The goal is to discover hidden patterns or structures within the data, such as clustering similar data points together or reducing the dimensionality of the data. Examples include clustering, dimensionality reduction, and generative modeling tasks.
Supervised learning involves training a model on labeled data, where the algorithm learns to predict outcomes based on input-output pairs. Unsupervised learning, on the other hand, involves training on unlabeled data, where the algorithm discovers patterns and structures within the data without explicit guidance.