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Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805941 ISBN 13: 9786207805945
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Published by LAP LAMBERT Academic Publishing, 2024
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ISBN 10: 6207805941 ISBN 13: 9786207805945
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Taschenbuch. Condition: Neu. Neuware -'Hands-On Implementation of Machine Learning Models' is a practical guide designed to bridge the gap between machine learning theory and real-world applications. It provides clear explanations of fundamental and advanced machine learning models, including linear and logistic regression, decision trees, support vector machines, k-means clustering, PCA, hierarchical clustering, random forests, gradient boosting machines, and neural networks.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch.
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Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805941 ISBN 13: 9786207805945
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Taschenbuch. Condition: Neu. Hands-On Implementation of Machine Learning Models | Rupesh Kumar Tipu | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207805945 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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ISBN 10: 6207805941 ISBN 13: 9786207805945
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ISBN 10: 6207805941 ISBN 13: 9786207805945
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Published by LAP LAMBERT Academic Publishing Jun 2024, 2024
ISBN 10: 6207805941 ISBN 13: 9786207805945
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Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805941 ISBN 13: 9786207805945
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Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805941 ISBN 13: 9786207805945
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Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207805941 ISBN 13: 9786207805945
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Hands-On Implementation of Machine Learning Models' is a practical guide designed to bridge the gap between machine learning theory and real-world applications. It provides clear explanations of fundamental and advanced machine learning models, including linear and logistic regression, decision trees, support vector machines, k-means clustering, PCA, hierarchical clustering, random forests, gradient boosting machines, and neural networks.