Inbal yahav (23 risultati)

Hardcover. Condizione: very good. Used items may not include media like access codes or CDs. Fast shipping! Expedited orders take 1-3 business days! Media mail may take up to 5 business days.

Hardcover. Condizione: Very Good+. In great condition. Few signs of wear. Clean with no highlighting or writing. 30-day returns. Free shipping within Canada. For shipments outside of Canada, import duties and other charges may be levied on receipt. ; 0 X 0 X 0 inches; 576 pages.

Hardcover. Condizione: New. 2nd Edition. Brand New US Edition textbook. Ship from Multiple Locations, including Asia , Hong Kong ,Taiwan , US or Canada depend on stock location.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Condizione: As New. Unread book in perfect condition.

Condizione: Very Good.

Condizione: New.

Condizione: New.

Hardcover. Condizione: Brand New. 2nd edition. 672 pages. 10.28x7.20x1.18 inches. In Stock.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: Brand New. har/psc edition. 544 pages. 11.00x8.00x1.25 inches. In Stock.

Condizione: New.

Condizione: NEW.

Condizione: New.

Condizione: Very Good.

Hardback. Condizione: 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.…

Condizione: New.

Condizione: New. Brand new! Please provide a physical shipping address.

Hardback. Condizione: 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.…

hardcover. Condizione: LikeNew. Pages are New and Unused, Front or Back cover is damaged- torn, wrinkled or dented., Used Like New, no missing pages, no damage to binding, may have a remainder mark.