Example Concept: In supervised learning, the model is trained using a dataset that includes both input data and the corresponding correct output. The model learns to map inputs to outputs by minimizing the error between its predictions and the actual outputs. In contrast, unsupervised learning involves training a model on data without explicit labels, aiming to discover inherent structures or patterns, such as clustering similar data points or reducing dimensionality.