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Aggiungi al carrelloPaperback. Condizione: Brand New. reprint edition. 232 pages. 9.25x6.10x0.53 inches. In Stock.
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Aggiungi al carrelloPaperback. Condizione: New. Brand New ! Fast Delivery "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 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore, 2018
ISBN 10: 9811349509 ISBN 13: 9789811349508
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 64,39
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readerscan modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Machine Learning with R | Abhijit Ghatak | Taschenbuch | xix | Englisch | 2018 | Springer | EAN 9789811349508 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore, 2017
ISBN 10: 9811068070 ISBN 13: 9789811068072
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 90,34
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readerscan modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 51,83
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 70,24
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer Nature Singapore Dez 2018, 2018
ISBN 10: 9811349509 ISBN 13: 9789811349508
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 60,98
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning. 232 pp. Englisch.
Da: moluna, Greven, Germania
EUR 53,22
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Abhijit Ghatak is a Data Scientist and holds an ME in Engineering and MS in Data Science from Stevens Institute of Technology, USA. He started his career as a submarine engineer officer in the Indian Navy and worked on multiple data-intensive projects in.
Lingua: Inglese
Editore: Springer Nature Singapore Dez 2017, 2017
ISBN 10: 9811068070 ISBN 13: 9789811068072
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 85,59
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning. 232 pp. Englisch.
Lingua: Inglese
Editore: Springer, Springer Dez 2018, 2018
ISBN 10: 9811349509 ISBN 13: 9789811349508
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 60,98
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -¿Help readers understand the mathematical interpretation of learning algorithmsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 232 pp. Englisch.
Da: moluna, Greven, Germania
EUR 72,89
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Abhijit Ghatak is a Data Scientist and holds an ME in Engineering and MS in Data Science from Stevens Institute of Technology, USA. He started his career as a submarine engineer officer in the Indian Navy and worked on multiple data-intensive projects in.
Lingua: Inglese
Editore: Springer, Springer Dez 2017, 2017
ISBN 10: 9811068070 ISBN 13: 9789811068072
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 85,59
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -¿Help readers understand the mathematical interpretation of learning algorithmsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 232 pp. Englisch.