Articles liés à Machine Learning for Decision Makers: Cognitive Computing...
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making - Couverture souple
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Synopsis
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 applicat
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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