Research on Machine Learning and Its Algorithms and Development

Abstract

This article examines the fundamental categories of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It reviews common machine learning algorithms such as decision trees, random forests, artificial neural networks, SVMs, Boosting and Bagging techniques, and the BP algorithm. By advancing theoretical frameworks, enhancing autonomous learning capabilities, integrating various digital technologies, and promoting personalized services, the aim is to increase public understanding of machine learning and accelerate its widespread adoption.