What is supervised and unsupervised learning?

Supervised and unsupervised learning are two primary types of machine learning. Supervised learning involves training a model on a labeled dataset, where the desired output is known for each input. This allows the model to learn a mapping from inputs to outputs and make predictions on new, unseen data. Common applications include image classification, spam detection, and regression tasks like predicting housing prices. Unsupervised learning, on the other hand, involves training a model on an unlabeled dataset, where the desired output is not known. The goal is to discover patterns, structures, and relationships within the data. Common applications include clustering customers into segments, dimensionality reduction, and anomaly detection.