Learning algorithms are a set of rules or procedures that enable computers to learn from data. They analyze patterns and make predictions or decisions without being explicitly programmed for each task. Common types of learning algorithms include supervised learning, unsupervised learning, and reinforcement learning, each serving different purposes in data analysis. These algorithms are widely used in various applications, such as image recognition, natural language processing, and recommendation systems. By improving their performance over time through experience, learning algorithms help enhance the efficiency and accuracy of tasks across many fields, including artificial intelligence and machine learning.