Machine Learning Algorithms

Machine learning algorithms are software tools that enable computers to learn from data without being explicitly programmed. They analyze patterns and relationships in datasets to make predictions or take actions. Algorithms can be supervised, unsupervised, or reinforcement-based, depending on the type of problem they're designed to solve. Supervised algorithms learn from labeled training data to predict outcomes. Unsupervised algorithms discover hidden structures or patterns in unlabeled data. Reinforcement algorithms interact with environments and receive feedback to make decisions. Neural networks are a type of algorithm that mimic human brain function. Decision trees and random forests are popular ensemble methods that combine multiple models. Support vector machines separate classes by maximizing distances between decision boundaries. K-means clustering groups similar data points into clusters based on their characteristics.