Publicado por LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 48,77
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Publicado por LAP Lambert Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
EUR 53,10
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Añadir al carritoPaperback. Condición: new. Paperback. This book offers an in-depth exploration of the principles, techniques, and applications of machine learning. Starting with foundational concepts such as data preprocessing and model evaluation, the book covers both supervised learning models like regression and classification, and advanced topics like ensemble learning, neural networks, and deep learning. Practical considerations, including handling imbalanced data, feature engineering, and preventing data leakage, are thoroughly discussed to help build robust models. Designed for students, professionals, and enthusiasts alike, this guide provides valuable insights and practical knowledge to navigate and excel in the field of machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 44,56
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Publicado por LAP Lambert Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 50,18
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Añadir al carritoPaperback. Condición: new. Paperback. This book offers an in-depth exploration of the principles, techniques, and applications of machine learning. Starting with foundational concepts such as data preprocessing and model evaluation, the book covers both supervised learning models like regression and classification, and advanced topics like ensemble learning, neural networks, and deep learning. Practical considerations, including handling imbalanced data, feature engineering, and preventing data leakage, are thoroughly discussed to help build robust models. Designed for students, professionals, and enthusiasts alike, this guide provides valuable insights and practical knowledge to navigate and excel in the field of machine learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por KS Omniscriptum Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
EUR 48,92
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por KS Omniscriptum Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 51,87
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Añadir al carritoPAP. 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.
Publicado por LAP LAMBERT Academic Publishing Jun 2024, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 43,90
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 76 pp. Englisch.
Publicado por LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 44,59
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book offers an in-depth exploration of the principles, techniques, and applications of machine learning. Starting with foundational concepts such as data preprocessing and model evaluation, the book covers both supervised learning models like regression and classification, and advanced topics like ensemble learning, neural networks, and deep learning. Practical considerations, including handling imbalanced data, feature engineering, and preventing data leakage, are thoroughly discussed to help build robust models. Designed for students, professionals, and enthusiasts alike, this guide provides valuable insights and practical knowledge to navigate and excel in the field of machine learning.