Review of Machine Learning Algorithms and Their Applications

Abstract

A conceptual introduction is given to the topic of machine learning. Machine learning is primarily concerned with the process by which computers learn autonomously and modify their actions accordingly, without the need for human invention or assistance. Review the many viewpoints on machine learning algorithms and their uses found in the literature in this page. This review highlights that machine learning (ML) algorithms, including supervised, unsupervised, and reinforcement learning, have become integral to diverse real-world applications. From speech recognition, image classification, and medical diagnosis to fraud detection, self-driving cars, and online recommendations, ML demonstrates remarkable versatility. Findings indicate that supervised learning is effective with smaller labeled datasets, while unsupervised learning and deep learning perform better with large-scale data. Reinforcement learning adds value in dynamic environments requiring decision-making. In conclusion, ML continues to evolve as a transformative tool across domains, though challenges in scalability, interpretability, and ethical implementation remain critical areas for future research.