Example Concept: Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. The model learns to map inputs to outputs and is evaluated based on its accuracy in predicting the labels. In contrast, unsupervised learning deals with unlabeled data, where the model tries to identify patterns or structures, such as clustering similar data points or reducing dimensionality without explicit guidance on the output.