Review Paper on Machine Learning Algorithms

This paper comprehensively reviews widely used machine learning algorithms across supervised, unsupervised, and reinforcement learning paradigms. It covers linear models, decision trees, support vector machines, neural networks, clustering techniques, dimensionality reduction methods, and ensemble approaches. For each algorithm, theoretical foundations, mathematical formulations, practical considerations like parameter tuning and computational complexity, and real-world applications across domains like computer vision and finance are discussed. Challenges and limitations such as overfitting and scalability are explored. Recent advancements like deep learning and transfer learning are highlighted. Finally, a comparative analysis evaluating strengths, weaknesses, and suitable problem domains for the algorithms is provided, serving as a guide for effective utilization of machine learning techniques.