Machine learning algorithms are a set of mathematical models and techniques that enable computers to learn from data. They analyze patterns and make predictions or decisions without being explicitly programmed for each task. These algorithms can be categorized into supervised, unsupervised, and reinforcement learning, depending on how they learn from the data. In supervised learning, the algorithm is trained on labeled data, while unsupervised learning deals with unlabeled data to find hidden patterns. Reinforcement learning involves learning through trial and error, receiving feedback from actions taken. Together, these approaches help improve the performance of various applications, from image recognition to natural language processing.