Kashyap Machine Learning for Decision Makers
1. Auflage 2018
ISBN: 978-1-4842-2988-0
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark
Cognitive Computing Fundamentals for Better Decision Making
E-Book, Englisch, 355 Seiten
Reihe: Apress Access Books
ISBN: 978-1-4842-2988-0
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark
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 usescase 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
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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
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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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
Chapter 1: Introduction.- Chapter 2: Fundamentals of Machine Learning and its technical ecosystem.- Chapter 3: Methods and techniques of Machine Learning.- Chapter 4: Machine Learning and its relationship with cloud, IOT, big data and cognitive computing in business perspective.- Chapter 5: Business challenges and applications of Machine Learning big data, IOT, cloud and cognitive computing in different fields and domains.- Chapter 6: Technology offered by different vendors for Machine Learning.- Chapter 7: Security and Machine Learning.- Visual and text summery of the chapter.- Chapter 8: Matrices, KPI’s and more.For Machine Learning ecosystem.- Chapter 9: Best practices and pattern for Machine Learning.- Chapter 10: Recent advancement and future directions of Machine Learning.- Chapter 11: Conclusion.




