Smart Python for Machine Learning and Intelligent Modeling (Paperback)

Alexander I. Iliev

ISBN 10: 6630107268 / ISBN 13: 9786630107265

Published by LAP Lambert Academic Publishing, 2026

About this Item

Paperback. This book introduces the fundamental principles of machine learning and intelligent modeling through practical Python implementations and real-world examples. It begins by establishing the foundations of smart systems, learning paradigms, evaluation metrics, and model validation before progressing to classical supervised and unsupervised machine learning algorithms. Readers are guided through linear regression, logistic regression, k-nearest neighbors, support vector machines, decision trees, ensemble learning, clustering techniques, and principal component analysis, with an emphasis on both the underlying mathematical concepts and their practical implementation using Python. Each chapter combines theoretical background, methodology, implementation, experimental evaluation, and discussion to provide a comprehensive learning experience. Intended for undergraduate and graduate students, researchers, and practitioners, this volume serves as both an academic textbook and a practical reference for developing reliable, interpretable, and data-driven intelligent systems using modern machine learning techniques. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9786630107265

Bibliographic Details

• Title: Smart Python for Machine Learning and Intelligent Modeling (Paperback)
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6630107268
• ISBN 13: 9786630107265
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book introduces the fundamental principles of machine learning and intelligent modeling through practical Python implementations and real-world examples. It begins by establishing the foundations of smart systems, learning paradigms, evaluation metrics, and model validation before progressing to classical supervised and unsupervised machine learning algorithms. Readers are guided through linear regression, logistic regression, k-nearest neighbors, support vector machines, decision trees, ensemble learning, clustering techniques, and principal component analysis, with an emphasis on both the underlying mathematical concepts and their practical implementation using Python. Each chapter combines theoretical background, methodology, implementation, experimental evaluation, and discussion to provide a comprehensive learning experience. Intended for undergraduate and graduate students, researchers, and practitioners, this volume serves as both an academic textbook and a practical reference for developing reliable, interpretable, and data-driven intelligent systems using modern machine learning techniques.