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Machine Learning for Decision Makers

Language EnglishEnglish
Book Paperback
Book Machine Learning for Decision Makers Dr. Patanjali Kashyap
Libristo code: 16505383
Publishers APress, January 2018
Take a deep dive into the concepts of machine learning as they apply to contemporary business and ma... Full description
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Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

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About the book

Full name Machine Learning for Decision Makers
Language English
Binding Book - Paperback
Date of issue 2018
Number of pages 355
EAN 9781484229873
ISBN 1484229878
Libristo code 16505383
Publishers APress
Weight 634
Dimensions 238 x 157 x 25
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