Learning Paradigms

Learning paradigms are different approaches to teaching machines how to understand and perform tasks. Supervised learning uses labeled data to guide the model, like teaching with answer keys. Unsupervised learning finds patterns in unlabeled data, such as grouping similar items. Reinforcement learning involves learning through trial and error, receiving rewards or penalties, much like training a pet. Semi-supervised combines both labeled and unlabeled data. These paradigms guide how algorithms learn, helping them solve problems in areas like speech recognition, recommendation systems, and autonomous driving.