ISBN: 9786630285956 - machine learning techniques and model evaluation (10 resultados)

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. 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.

Paperback. Condición: new. Paperback. This book provides a comprehensive introduction to Machine Learning techniques and Model Evaluation, covering the complete lifecycle of developing intelligent predictive models. It explores fundamental learning approaches, including supervised, unsupervised, and reinforcement learning, along with widely used algorithms for classification, regression, clustering, and pattern discovery. The book also emphasizes the importance of evaluating machine learning models using appropriate performance metrics, validation techniques, and error analysis to ensure accuracy, reliability, and generalization. By integrating algorithmic concepts with practical evaluation methods, the book equips readers with the knowledge to build, assess, and optimize machine learning models for real-world applications across diverse domains. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 232 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive introduction to Machine Learning techniques and Model Evaluation, covering the complete lifecycle of developing intelligent predictive models. It explores fundamental learning approaches, including supervised, unsupervised, and reinforcement learning, along with widely used algorithms for classification, regression, clustering, and pattern discovery. The book also emphasizes the importance of evaluating machine learning models using appropriate performance metrics, validation techniques, and error analysis to ensure accuracy, reliability, and generalization. By integrating algorithmic concepts with practical evaluation methods, the book equips readers with the knowledge to build, assess, and optimize machine learning models for real-world applications across diverse domains.…

Paperback. Condición: new. Paperback. This book provides a comprehensive introduction to Machine Learning techniques and Model Evaluation, covering the complete lifecycle of developing intelligent predictive models. It explores fundamental learning approaches, including supervised, unsupervised, and reinforcement learning, along with widely used algorithms for classification, regression, clustering, and pattern discovery. The book also emphasizes the importance of evaluating machine learning models using appropriate performance metrics, validation techniques, and error analysis to ensure accuracy, reliability, and generalization. By integrating algorithmic concepts with practical evaluation methods, the book equips readers with the knowledge to build, assess, and optimize machine learning models for real-world applications across diverse domains. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Machine Learning Techniques And Model Evaluation | Vandana Vandana (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630285956 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware 232 pp. Englisch.

Paperback. Condición: new. Paperback. This book provides a comprehensive introduction to Machine Learning techniques and Model Evaluation, covering the complete lifecycle of developing intelligent predictive models. It explores fundamental learning approaches, including supervised, unsupervised, and reinforcement learning, along with widely used algorithms for classification, regression, clustering, and pattern discovery. The book also emphasizes the importance of evaluating machine learning models using appropriate performance metrics, validation techniques, and error analysis to ensure accuracy, reliability, and generalization. By integrating algorithmic concepts with practical evaluation methods, the book equips readers with the knowledge to build, assess, and optimize machine learning models for real-world applications across diverse domains. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…