文件名称：Machine Learning for Decision Makers
文件大小：4.81MB
文件格式：PDF
更新时间：2021-01-28 04:28:04
机器学习
Book Description 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. Table of Contents Chapter 1: Let’s Integrate with Machine Learning Chapter 2: The Practical Concepts of Machine Learning Chapter 3: Machine Learning Algorithms and Their Relationship with Modern Technologies Chapter 4: Technology Stack for Machine Learning and Associated Technologies Chapter 5: Industrial Applications of Machine Learning Chapter 6: I Am the Future: Machine Learning in Action Chapter 7: Innovation, KPIs, Best Practices, and More for Machine Learning Chapter 8: Do Not Forget Me: The Human Side of Machine Learning Chapter 9: Let’s Wrap Up: The Final Destination Appendix A: How to Architect and Build a Machine Learning Solution Appendix B: A Holistic Machine Learning and Agile-Based Software Methodology Appendix C: Data Processing Technologies