Example Concept: In supervised learning, models are trained using input-output pairs, where the output is known and used to guide the learning process. For example, a model might learn to predict house prices based on features like size and location, using historical sales data. In contrast, unsupervised learning involves models that explore data without explicit instructions on what to predict. Clustering algorithms, like k-means, are common in unsupervised learning, where the goal is to group similar data points together based on inherent characteristics.