Machine learning algorithms are at the core of AI and data science, enabling systems to learn from data and make predictions. Common algorithms include decision trees, which split data into decision nodes to predict outcomes, and k-means clustering, which groups data based on similarities. Neural networks, which simulate the human brain, are used for complex tasks like image recognition and natural language processing.
Supervised learning algorithms require labeled data to learn from, while unsupervised learning algorithms, like clustering, find patterns without predefined labels. Reinforcement learning algorithms learn by interacting with an environment, receiving feedback, and adjusting actions accordingly.