Machine Learning and Data Science (Paperback)

Daniel D. Gutierrez

ISBN 10: 1634620968 / ISBN 13: 9781634620963

Editore: Technics Publications LLC, Bradley Beach, 2015

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Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9781634620963

Dati bibliografici

• Titolo: Machine Learning and Data Science (Paperback)
• Editore: Technics Publications LLC, Bradley Beach
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 1634620968
• ISBN 13: 9781634620963
• Rilegatura: Paperback
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Daniel D. Gutierrez is a practicing data scientist through his Santa Monica, Calif. consulting firm AMULET Analytics. Daniel also serves as Managing Editor for insideBIGDATA.com where he keeps a pulse on this dynamic industry. He is also an educator and teaches classes in data science, machine learning and R for universities and large enterprises. Daniel holds a BS degree in mathematics and computer science from UCLA.