9781119828792 - machine learning for business analytics: concepts, techniques and applications in rapidminer de shmueli, galit; bruce, peter c.; deokar, amit v.; patel, nitin r. (25 resultados)
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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hardcover. Condición: New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Machine Learning for Business Analytics : Concepts, Techniques and Applications in Rapidminer
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Machine Learning for Business Analytics : Concepts, Techniques and Applications in Rapidminer
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Machine Learning for Business Analytics : Concepts, Techniques and Applications in Rapidminer
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Machine Learning for Business Analytics : Concepts, Techniques and Applications in Rapidminer
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Machine Learning for Business Analytics : Concepts, Techniques and Applications in Rapidminer
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Hardcover. Condición: new. Hardcover. Machine Learning for Business Analytics Machine learningalso known as data mining or data analyticsis 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, T…echniques and Applications in RapidMiner 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 seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 140,92
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Condición: New. In English.
Machine Learning for Business Analytics
Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Amit V. Deokar
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 158,28
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Hardback. Condición: 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 RapidMiner 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 seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A 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.
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 152,87
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Librería: THE SAINT BOOKSTORE, Southport, Reino UnidoTHE SAINT BOOKSTORE
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EUR 138,57
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Hardback. Condición: New. New copy - Usually dispatched within 4 working days.
Machine Learning for Business Analytics : Concepts, Techniques and Applications in Rapidminer
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 145,20
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 150,42
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Condición: New. 2023. 1st Edition. Hardcover. . . . . .
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 166,27
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EUR 142,32
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Hardcover. Condición: new. Hardcover. Machine Learning for Business Analytics Machine learningalso known as data mining or data analyticsis 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, T…echniques and Applications in RapidMiner 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 seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
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EUR 187,06
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Condición: New. 2023. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
MACHINE LEARNING FOR BUSINESS ANALYTICS CONCEPTS TECHNIQUES AND APPLICATIONS IN RAPIDMINER (HB 2023)
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- Edición internacional
Librería: UK BOOKS STORE, London, LONDO, Reino UnidoUK BOOKS STORE
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EUR 208,43
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Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if th…e Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Shmueli, Galit; Bruce, Peter C.; Deokar, Amit V.; Patel, Nitin R.
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Librería: Ubiquity Trade, Miami, FL, Estados Unidos de AmericaUbiquity Trade
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EUR 229,72
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Condición: New. Brand new! Please provide a physical shipping address.
Machine Learning for Business Analytics
Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Amit V. Deokar
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 154,86
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Hardback. Condición: 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 RapidMiner 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 seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A 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.
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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EUR 234,24
Envío por EUR 31,85Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: new. Hardcover. Machine Learning for Business Analytics Machine learningalso known as data mining or data analyticsis 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, T…echniques and Applications in RapidMiner 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 seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
