Verlag: Technics Publications (edition First Edition), 2015
ISBN 10: 1634620968 ISBN 13: 9781634620963
Sprache: Englisch
Anbieter: BooksRun, Philadelphia, PA, USA
Erstausgabe
Paperback. Zustand: Good. First Edition. 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.
Verlag: Technics Publications LLC, 2015
ISBN 10: 1634620968 ISBN 13: 9781634620963
Sprache: Englisch
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
EUR 45,33
Anzahl: 15 verfügbar
In den WarenkorbPAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Verlag: Technics Publications LLC, 2015
ISBN 10: 1634620968 ISBN 13: 9781634620963
Sprache: Englisch
Anbieter: Kennys Bookstore, Olney, MD, USA
EUR 65,07
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. Num Pages: 230 pages. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 235 x 190. . . 2015. First. Paperback. . . . . Books ship from the US and Ireland.
Verlag: Technics Publications LLC, 2015
ISBN 10: 1634620968 ISBN 13: 9781634620963
Sprache: Englisch
Anbieter: moluna, Greven, Deutschland
EUR 41,39
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In den WarenkorbZustand: New. KlappentextrnrnA practitioner s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access.
Verlag: Technics Publications Okt 2015, 2015
ISBN 10: 1634620968 ISBN 13: 9781634620963
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.