Category: Machine Learning

Machine learning is a subfield of artificial intelligence that enables a system to learn from data rather than through explicit programming. It centers on the development of algorithms and statistical models that computers use to perform tasks without explicit instruction, by making data-driven predictions or decisions. These algorithms are often grouped into three main types: supervised learning (including key algorithms like linear regression, logistic regression, decision trees, and support vector machines), unsupervised learning (like clustering algorithms, such as K-means, and dimensionality reduction techniques, such as PCA), and reinforcement learning (such as Q-learning). Fundamental concepts in machine learning include training and testing data, loss functions, overfitting, underfitting, bias-variance trade-off, cross-validation, and feature engineering. The applications of these algorithms and concepts range from email filtering and computer vision to natural language processing and autonomous vehicle control.