Machine Learning Techniques

Author

Professor Lecturer Ganesh Namdeo Falake


Abstract

 Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed for every task. Various machine learning techniques, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, are widely used to solve real-world problems. Common algorithms such as linear regression, decision trees, support vector machines, k-nearest neighbors, clustering, and neural networks provide effective solutions for classification, prediction, pattern recognition, and data analysis. This paper explores the fundamental concepts, techniques, applications, advantages, and challenges of machine learning. It also highlights the importance of data quality, model selection, training, and evaluation in developing accurate and reliable ML systems. Machine learning continues to play a significant role in areas such as healthcare, finance, education, cybersecurity, recommendation systems, and autonomous technologies.



Keywords

Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Regression, Reinforcement Learning, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Clustering



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