Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making
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
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.
Lets Integrate with Machine Learning
The Practical Concepts of Machine Learning
Machine Learning Algorithms and Their Relationship with Modern Technologies
Technology Stack for Machine Learning and Associated Technologies
Industrial Applications of Machine Learning
I Am the Future Machine Learning in Action
Innovation KPIs Best Practices and More for Machine Learning
Do Not Forget Me The Human Side of Machine Learning
Other editions - View all
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for ...
No preview available - 2017
achieve action analysis applications appropriate areas associated automated become behavior better Big Data building called challenges changing chapter classification cloud cluster cognitive computing communication companies complex computing concepts connected create data sets decision detection devices discussed effective efficient enables enterprises environment example excellent existing functionality Google human identify implementation important improve increase individual industry innovative insight integrated intelligence knowledge language layer learning algorithms machine learning analytics maps measure multiple natural offering operations optimization organizations patterns performance personalized platform potential practical predict present problems programming regression requirements response risk role sensors smart social solutions sources specific stack strategies success tasks technical techniques technologies things thought typically understand users