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Machine Learning for Decision Makers

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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 : 30-40 Sub -Topics 1. What is Machine Learning 2. DNA of ML 3. Big Data and associated technologies 4. What is cognitive computing by the way 5. Let's talk about internet of things (IOT) 6. All this happens in cloud ..... Really!! 7. Putting it all together 8. Few professional point of views on Machine Learning technologies 9. Mind Map for the chapter 10. Visual and text summary of the chapter 11. Ready to use diagrams for decision makers 12. Conclusion Chapter 2: Fundamentals of Machine Learning and its technical ecosystem Chapter 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: 40-50 Sub - Topics 1. Evolution of ML 2. Need for Machine Learning 3. The Machine Learning business opportunity 4. Concepts of Machine Learning 4.1 Algorithm types for Machine Learning 4.2 Supervised learning 4.3 Machine Learning models 4.5 Machine Learning life cycle 5. Common programing languages for ML 6. Data mining and Machine Learning 7. Knowledge discovery and ML 8. Types and architecture of Machine Learning 9. Application and uses of Machine Learning 10. Tools and frameworks of Machine Learning 11. New advances in Machine Learning 12. Tenets for large scale ML applications 13. Machine Learning in IT organizations 14. Machine Learning value creation 15. Case study 16. Authors interpretation of case studies 17. Few professional point of views 18. Mind map for the chapter 19. Some important questions and their answers 20. Your notes .... My notes 21. Visual and text summary of the chapter 22. Ready to use diagram for the decision makers 23. Conclusion Chapter 3: Methods and techniques of Machine Learning Chapter Goal: This chapter will discuss in details about the common methods and techniques of Machine Learning No of pages: - 40-50 Sub - Topics: 1. Quick look on required mathematical concepts 2. Decision trees 2.1 The basic of decision tree 2.2 How decision tree works 2.3 Different algorithm types in decision tree 2.4 Uses and applications of decision trees in enterprise 2.5 Get maximum out of decision tree 3. 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 networks 4. 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 networks 5. 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 learning 6. Support vector machines 7. Few professional point of views on Machine Learning technologies 8. Case study 9. Mind map for the chapter 10. Some important questions and their answers 11. Your notes...my notes 12 Visual and text summary of the chapter 13 Ready to use diagram of the decision makers 14 Conclusion Chapter 4: Machine Learning and its relationship with cloud, IOT, big data and cognitive computing in business perspective Chapter Goal: This Chapter will discuss briefly about Machine Learning associated technologies, like big data, internet of things(IOT), cognitive computing and cloud computing.