Example Concept: In supervised learning, models learn from input-output pairs, meaning each input data point is associated with a known output label. This allows the model to make predictions on new, unseen data. In contrast, unsupervised learning involves analyzing input data without any labeled responses, aiming to identify patterns, groupings, or structures within the data itself, such as clustering or dimensionality reduction.