Machine learning algorithms are computational methods used to enable systems to learn from data and make predictions or decisions without being explicitly programmed for specific tasks. They use statistical techniques to identify patterns and relationships in data. Common types include supervised learning (e.g., linear regression, decision trees), unsupervised learning (e.g., clustering, principal component analysis), and reinforcement learning (e.g., Q-learning, deep Q-networks). These algorithms adapt and improve their performance as they are exposed to more data, making them powerful tools for tasks like image recognition, language processing, and predictive analytics.