Supervised learning is the process of using labeled data to train a model to make predictions on new, unseen data. The data is labeled, meaning that the output (or target) is known. For example, a supervised learning model could be used to predict the price of a house, given its features (such as size, location, etc).
Unsupervised learning is the process of using unlabeled data to train a model to discover patterns in the data. Unlike supervised learning, the output (or target) is not known. For example, an unsupervised learning model could be used to cluster data points into groups based on their similarities.