*Machine Learning Basics: Building Intelligent Systems* is a foundational guide to understanding the principles and practices of machine learning. It covers core concepts such as supervised and unsupervised learning, data preprocessing, model selection, and evaluation. The book emphasizes practical applications, offering step-by-step explanations for implementing algorithms like decision trees, neural networks, and clustering methods. With a focus on problem-solving, it bridges theoretical knowledge and real-world use cases, making it an accessible resource for beginners and a solid refresher for experienced practitioners.
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - \*Machine Learning Basics: Building Intelligent Systems\* is a foundational guide to understanding the principles and practices of machine learning. It covers core concepts such as supervised and unsupervised learning, data preprocessing, model selection, and evaluation. The book emphasizes practical applications, offering step-by-step explanations for implementing algorithms like decision trees, neural networks, and clustering methods. With a focus on problem-solving, it bridges theoretical knowledge and real-world use cases, making it an accessible resource for beginners and a solid refresher for experienced practitioners. Nº de ref. del artículo: 9786208426897
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Taschenbuch. Condición: Neu. Machine Learning Basics: Building Intelligent Systems | Nandini K (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208426897 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 131756212
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -\*Machine Learning Basics: Building Intelligent Systems\* is a foundational guide to understanding the principles and practices of machine learning. It covers core concepts such as supervised and unsupervised learning, data preprocessing, model selection, and evaluation. The book emphasizes practical applications, offering step-by-step explanations for implementing algorithms like decision trees, neural networks, and clustering methods. With a focus on problem-solving, it bridges theoretical knowledge and real-world use cases, making it an accessible resource for beginners and a solid refresher for experienced practitioners.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch. Nº de ref. del artículo: 9786208426897
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