Example Concept: In supervised learning, models are trained using input-output pairs, meaning each input comes with a corresponding correct output (label). This allows the model to learn a mapping from inputs to outputs, commonly used in tasks like classification and regression. In contrast, unsupervised learning involves training models on data without explicit labels, focusing on discovering hidden structures or patterns within the data, such as clustering similar data points or reducing dimensionality.