Machine learning algorithms can learn through supervised, unsupervised, or reinforcement learning. Supervised learning involves providing labeled examples to learn a function that maps inputs to outputs. Unsupervised learning identifies hidden patterns in unlabeled data. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment. Machine learning has applications in areas like computer vision, natural language processing, medical diagnosis, and more.