Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: California Books, Miami, FL, U.S.A.
Condition: New.
Published by LAP Lambert Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Paperback. Condition: 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.
Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 36.40
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Add to basketCondition: New. In.
Published by LAP Lambert Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: CitiRetail, Stevenage, United Kingdom
£ 40.99
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Add to basketPaperback. Condition: 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.
Published by KS Omniscriptum Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by KS Omniscriptum Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
£ 42.37
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Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by LAP LAMBERT Academic Publishing Jun 2024, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
£ 38.12
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 76 pp. Englisch.
Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805690 ISBN 13: 9786207805693
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 38.72
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Add to basketTaschenbuch. Condition: 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.