Foundations of Machine Learning: Concepts to Applications

Foundations of Machine Learning: Concepts to Applications

Foundations of Machine Learning: Concepts to Applications offers a clear and practical introduction to the fundamental concepts, algorithms, and applications of Machine Learning. Designed for students, educators, and beginners, the book bridges the gap between theoretical foundations and real-world implementation.

It covers essential topics including supervised and unsupervised learning, regression, classification, decision trees, Naïve Bayes, KNN, SVM, clustering, dimensionality reduction, performance evaluation, and neural networks. With simple explanations, illustrative examples, mathematical foundations, and Python-based implementations, the book helps readers understand not only how machine learning algorithms work, but also when and why to use them.

Whether you are beginning your journey in Machine Learning or seeking a practical reference for academic study and application, this book provides a structured pathway from fundamental concepts to hands-on implementation.