ISBN: 9786630107265 - smart python for machine learning and intelligent modeling: foundations and classical machine learning | part 1 (9 resultados)

Condición: New.

Paperback. Condición: new. 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 multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 316 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Taschenbuch. Condición: Neu. Smart Python for Machine Learning and Intelligent Modeling | Foundations and Classical Machine Learning Part 1 | Alexander I. Iliev | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630107265 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware 316 pp. Englisch.

Paperback. Condición: new. 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.…