Example Concept: In supervised learning, the model is trained using a dataset that includes input-output pairs, meaning each input has a corresponding correct output label. The model learns to map inputs to outputs based on this labeled data. In contrast, unsupervised learning involves using datasets that do not have labeled outputs. The model tries to identify patterns, groupings, or structures within the data, such as clustering similar data points or reducing dimensionality.