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In den WarenkorbHardcover. Zustand: Good. 2. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
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In den WarenkorbZustand: acceptable. This copy has clearly been enjoyedâ"expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
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In den WarenkorbHardcover. Zustand: New. 2nd Edition. Brand New US Edition textbook. Ship from Multiple Locations, including Asia , Hong Kong ,Taiwan , US or Canada depend on stock location.
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In den WarenkorbBrand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 2nd edition. 672 pages. 10.28x7.20x1.18 inches. In Stock.
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In den WarenkorbZustand: New. In.
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In den WarenkorbHardcover. Zustand: New.
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In den WarenkorbZustand: New.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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In den WarenkorbZustand: As New. Unread book in perfect condition.
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In den WarenkorbZustand: NEW.
Anbieter: Mooney's bookstore, Den Helder, Niederlande
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In den WarenkorbZustand: Very good.
Verlag: John Wiley and Sons Inc, US, 2023
ISBN 10: 1119835178 ISBN 13: 9781119835172
Sprache: Englisch
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
EUR 182,35
Währung umrechnenAnzahl: 10 verfügbar
In den WarenkorbHardback. Zustand: New. MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning -also known as data mining or data analytics- is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
Anbieter: Ubiquity Trade, Miami, FL, USA
EUR 223,34
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In den WarenkorbZustand: New. Brand new! Please provide a physical shipping address.
Verlag: John Wiley and Sons Inc, US, 2023
ISBN 10: 1119835178 ISBN 13: 9781119835172
Sprache: Englisch
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 172,10
Währung umrechnenAnzahl: 10 verfügbar
In den WarenkorbHardback. Zustand: New. MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning -also known as data mining or data analytics- is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.