What are the key differences between supervised and unsupervised learning?
Supervised learning is a type of machine learning in which an algorithm is trained on labeled data to learn patterns and make predictions. The algorithm is given a set of examples, each with a label that indicates what the example represents, and it learns how to identify similar examples and assign them the same label.

Unsupervised learning, on the other hand, is a type of machine learning in which no labels are provided for the training data. Unsupervised learning algorithms use statistical techniques to discover patterns and relationships in the data without any human intervention or guidance. These algorithms can be used to cluster data into different groups, find outliers, or discover hidden structures within the data.

In summary, supervised learning uses labeled data to train an algorithm to predict outcomes, while unsupervised learning uses statistical methods to uncover patterns and structure in the data without any prior knowledge about its contents.