Machine Learning for Decision Makers

Explains the business challenges and applications of machine learning in big data, IoT, and cloud and cognitive computing in different fields and domains
Includes matrices, KPIs, and performance measures for the machine learning ecosystemCovers recent advancements and future directions of machine learning

Autorentext

Dr. Patanjali Kashyap hold a degree in Ph.D. (physics) and MCA. Currently he is working as a technology manager in a leading American bank. Professionally he deals with high impact mission critical financial and innovative new generation technology projects on day to day basis. He has worked with the technology giants like Infosys and Cognizant technology solutions. He is an expert of the agile process, machine learning, big data, and cloud computing paradigm. He possesses sound understanding of Microsoft Azure and cognitive computing platforms like Watson and Microsoft cognitive services. He introduces .net technologies as his first love to his friends and colleague. Patanjali has worked on spectrum of .net and associated technologies like Sql server and component based architecture from their inception. Few other technologies on which he loves to work on are SharePoint (content management in general), knowledge management, positive technology, psychological computing and UNIX. He is vastly experienced in Software development methodologies, Application support and maintenance.

He possesses a restless mind which is always looking for innovation and is involved in idea generation for all walks of life including spirituality, positive psychology, brain science and cutting-edge technologies. He is a strong believer in cross/ inter disciplinary study. His view of everything is linked with the other reflects in his work. For example, he has filed a patent on improving and measuring the performance of an individual by using emotional, social, moral and vadantic intelligence. Which presents a unique novel synthesis of management science, physics, information technology and organizational behaviour.

Patanjali has published several research and white papers on multiple topics. He is involved in a lot of organizational initiatives like building world class teams and dynamic culture across enterprises. He is a go-to person for incorporating positivity and enthusiasm in the enterprises. His fresh way of synthesizing Indian Vedic philosophies with the western practical management insight for building flawless organizational dynamics is much appreciated in the corporate circle. He is a real implementer of ancient mythologies at modern work place. Patanjali is also involved in the leadership development and building growth frameworks for the same.

Apart from MCA patanjali holds masters in bioinformatics, physics and computer science (M.Phil.).

Klappentext
Take a deep dive into the essential elements of machine learning. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Managers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing.
This book introduces a collection of the most important fundamental concepts of machine learning and its associated fields. These concepts span the process from envisioning the problem to applying machine-learning techniques to the enterprise. This discussion also provides an insight to help deploy the results to improve decision-making.

The book uses practical examples and use cases that will help you grasp the concepts of machine learning quickly. It concludes with a section on how using this technology will become a game-changer in the years to come.
You will:

• 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
• See the latest research, trends, and security frameworks in the machine learning space

• Use machine-learning best practices

  Inhalt
  Chapter 1: Introduction.- Chapter Goal: This chapter will set the stage. It will talk about the main technologies and topics which are going to be used in the book. IT would also provide brief description of the same.No of pages : 30-40Sub -Topics1. What is Machine Learning2. DNA of ML3. Big Data and associated technologies4. What is cognitive computing by the way5. Let's talk about internet of things (IOT)6. All this happens in cloud .. Really!!7. Putting it all together8. Few professional point of views on Machine Learning technologies9. Mind Map for the chapter10. Visual and text summary of the chapter11. Ready to use diagrams for decision makers12. Conclusion
  Chapter 2: Fundamentals of Machine Learning and its technical ecosystemChapter Goal: This chapter will explain the fundamental concepts of ML, Its uses in relevant business scenarios. Also takes deep die into business challenges where ML will be used as a solution. Apart from this chapter would cover architectures and other important aspects which are associated with the Machine Learning.No of pages: 40-50Sub - Topics1. Evolution of ML2. Need for Machine Learning3. The Machine Learning business opportunity4. Concepts of Machine Learning4.1 Algorithm types for Machine Learning4.2 Supervised learning4.3 Machine Learning models4.5 Machine Learning life cycle5. Common programing languages for ML6. Data mining and Machine Learning7. Knowledge discovery and ML8. Types and architecture of Machine Learning9. Application and uses of Machine Learning10. Tools and frameworks of Machine Learning11. New advances in Machine Learning12. Tenets for large scale ML applications13. Machine Learning in IT organizations14. Machine Learning value creation 15. Case study16. Authors interpretation of case studies17. Few professional point of views18. Mind map for the chapter19. Some important questions and their answers20. Your notes . My notes21. Visual and text summary of the chapter22. Ready to use diagram for the decision makers23. Conclusion
  Chapter 3: Methods and techniques of Machine LearningChapter Goal: This chapter will discuss in details about the common methods and techniques of Machine LearningNo of pages: - 40-50 Sub - Topics: 1. Quick look on required mathematical concepts2. Decision trees2.1 The basic of decision tree2.2 How decision tree works2.3 Different algorithm types in decision tree2.4 Uses and applications of decision trees in enterprise2.5 Get maximum out of decision tree3. Bayesian networks 3.1 The basics of Bayesian networks 3.2 Hoe Bayesian network works 3.3 Different algorithm types in Bayesian network 3.4 Uses and applications of Bayesian network in enterprise 3.5 Get maximum out of Bayesian networks4. Artificial neural networks 4.1 The basics of Artificial neural networks 4.2 How Artificial neural networks 4.3 Different algorithm types in Artificial neural networks 4.4 Uses and applications of Artificial neural networks in enterprise 4.5 Get maximum out of Artificial neural networks5. Association rules learning 5.1 The basics of Association rules learning 5.2 How artificial Association rules learning 5.3 Different algorithm types in Association rules learning 5.4 Uses and applications of Association rules learning in enterprise 5.6 Get maximum out of Association rules learning6. Support vector machines7. Few professional point of views on Machine Learning technologies8. Case study9. Mind map for the chapter10. Some important questions and their answers11. Your notesmy notes12 Visual and text summary of the chapter13 Ready to use diagram of the decision makers14 Conclusion
  Chapter 4: Machine Learning and its relationship with cloud, IOT, big data and cognitive computing in business perspectiveChapter Goal: This Chapter will discuss brief…