Example Concept: In supervised learning, the model is trained on a dataset containing input-output pairs, where the output is the label or target. The model learns to map inputs to the correct outputs. In contrast, unsupervised learning involves training a model on data without explicit labels, aiming to identify inherent patterns, groupings, or structures within the data, such as clustering similar data points together.