Review Paper on Machine Learning Algorithms

Review Paper on Machine Learning Algorithms

Nikhil Kumar1, Gulshan2, Simranjot Kaur3

1, 2 student, IV SEM, M.C.A, DAV Institute of Engineering and Technology, Jalandhar, Punjab, India

3Assistance Professor, M.C.A, DAV Institute of Engineering and Technology, Jalandhar, Punjab, India

1hnik6431gmail.com 2gulshankumar783796mail.com

3Simranjotkaursukhijagmail.com

ABSTRACT: 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.
Keywords:- Machine learning · Deep learning, Gradient Descent, Logistic Regression, Support Vector Machine, K Nearest Neighbor, Predictive analytics,