Smart Python for Machine Learning and Intelligent Modeling - Couverture souple

Iliev, Alexander I

9786630107265: Smart Python for Machine Learning and Intelligent Modeling

Couverture souple

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

Editeur : LAP Lambert Academic Publishing, 2026

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2026
Langue: anglais
ISBN 10: 6630107268
ISBN 13: 9786630107265
Reliure: Broch�
Nombre de pages: 316
Coordonn�es du fabricant: non disponible
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Smart Python for Machine Learning and Intelligent Modeling

Iliev, Alexander I

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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Smart Python for Machine Learning and Intelligent Modeling

Iliev, Alexander I.

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N� de r�f. du vendeur L2-9786630107265

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Smart Python for Machine Learning and Intelligent Modeling

Alexander I. Iliev

Edit� par LAP LAMBERT Academic Publishing Jul 2026, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 316 pp. Englisch. N� de r�f. du vendeur 9786630107265

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Smart Python for Machine Learning and Intelligent Modeling : Foundations and Classical Machine Learning | Part 1

Alexander I. Iliev

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9786630107265

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Smart Python for Machine Learning and Intelligent Modeling | Foundations and Classical Machine Learning | Part 1

Alexander I. Iliev

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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Smart Python for Machine Learning and Intelligent Modeling

Alexander I. Iliev

Edit� par LAP LAMBERT Academic Publishing Jul 2026, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware 316 pp. Englisch. N� de r�f. du vendeur 9786630107265

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Smart Python for Machine Learning and Intelligent Modeling (Paperback)

Alexander I. Iliev

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6630107268 ISBN 13 : 9786630107265

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Paperback. Etat : 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. N� de r�f. du vendeur 9786630107265

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