Classification Techniques: Machine Learning, Neural Networks, and Statistics

Classification Techniques: Machine Learning, Neural Networks, and Statistics offers a comprehensive and accessible exploration of classification methods at the intersection of computer science and statistical analysis. Designed for students, researchers, and professionals, this textbook brings clarity to the essential algorithms and models that drive decision-making in data science, artificial intelligence, and predictive analytics.

The book begins by introducing foundational concepts in data science, machine learning, neural networks, and statistical classification. It then guides readers through both classical and modern statistical methods, including regression, feature selection, and hypothesis testing. Special attention is given to rule-based systems, decision trees, and ensemble techniques, followed by a focused examination of neural networks and their real-world applications.

With chapters dedicated to model comparison, empirical studies, descriptive statistics, and dynamic systems learning, the text ensures a well-rounded understanding of both theory and practice. The final section ties in data mining techniques—highlighting their role in knowledge discovery, predictive modeling, and classification.

Filled with thought-provoking questions, practical examples, and visual aids, this book is an ideal resource for anyone seeking to master classification techniques across diverse fields and data environments.