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.
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 76 pp. Englisch. N° de réf. du vendeur 9786207805693
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. N° de réf. du vendeur 9786207805693
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : 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. N° de réf. du vendeur 9786207805693
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