Description
Machine Learning Models: Concepts, Algorithms and Python Implementations is a practical and accessible guide to understanding the fundamental concepts and algorithms of machine learning. The book is designed to help students, beginners, educators, and aspiring machine learning practitioners develop a strong foundation in both the theory and practical implementation of machine learning models. The book covers a carefully selected range of supervised and unsupervised learning algorithms, including Linear Regression, Random Forest, Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, K-Means Clustering, Hierarchical Clustering, Gaussian Mixture Models (GMM), and DBSCAN. Each chapter introduces the core concept of the algorithm and explains its working principle, important characteristics, advantages, limitations, applications, and Python implementation. The inclusion of practical examples and code helps readers connect theoretical concepts with real-world machine learning tasks. The book begins with fundamental supervised learning models and gradually progresses toward clustering techniques. This structured approach makes it suitable for readers who are new to machine learning while also providing a useful reference for learners who want to strengthen their understanding of commonly used algorithms. With its combination of conceptual explanations, algorithmic understanding, practical examples, and Python-based implementation, this book serves as a useful learning resource for students of Computer Science, Artificial Intelligence, Machine Learning, Data Science, and related disciplines. Whether used for classroom learning, self-study, laboratory practice, or as a quick reference, this book aims to make machine learning algorithms easier to understand, implement, and apply.