This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own.
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Hardcover. Etat : new. Hardcover. This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naive Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9789819811236
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Hardback. Etat : New. This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. N° de réf. du vendeur LU-9789819811236
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Hardcover. Etat : new. Hardcover. This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naive Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9789819811236
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Buch. Etat : Neu. MACHINE LEARNING IN BUSINESS FINANCE USING PYTHON | Lim Kian Guan | Buch | Englisch | 2025 | World Scientific | EAN 9789819811236 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 133789452
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Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. N° de réf. du vendeur 9789819811236
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Vendeur : Rarewaves.com UK, London, Royaume-Uni
Hardback. Etat : New. This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. N° de réf. du vendeur LU-9789819811236
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