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: 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. Finally, I will conclude the chapter by establishing relationship among these.
No of pages: 40-50
Sub - Topics:
1. What is big about big data
2. Introduction to big data concepts
3. Big data technologies
4. Big data solutions
5. Fundamentals of cloud computing
6. Cloud computing technology stacks7. Internet of things …. what is it all about
8. IOT technology stack
9. Modern solution architectures with real world IOT
10. Building blocks of cognitive computing
11. Big data and cognitive computing
12. Cloud and cognitive computing
13. Emerging cognitive computing areas
14. Putting it all together
15. Business insight
16. Business optimization
17. Case study 1
18. Case study 2
19. Authors interpretation of case studies
20. Some important questions and their answers
21. Few professional point of views
22. Mind map for the chapter
23. Your notes …… My notes
24. Visual and text summary of the chapter
25. Ready to use diagram for decision makers
26. Conclusion
Chapter 5: Business challenges and applications of Machine Learning big data, IOT, cloud and cognitive computing in different fields and domains
Chapter Goal: This chapter will talk about business challenges associated with Machine Learning technologies and its solutions. Also discuss about few real time scenarios and used cases. Apart from this will throw light on application of ML across industries
NO of pages: 20-30
Sub-Topics:
1. Machine Learning and business value
2. Drivers of business value
3. Achieving customer delight and engagement with ML
4. Responsive systems and ML
5. Self-healing and Machine Learning
6. How advance analytics will take you
7. Case study- can we predict salary from historic data
8. Case study-big data as a service
9. Case study-connected cars
10. Application of ML across industries
10.1 Retail
10.2 Airline
10.3 Auto
10.4 Financial services
10.5 Energy
10.6 Data Warehousing
11. Few professional point of views on Machine Learning technologies
12. Mind map for the chapter
13. Some important questions and their answers
14. Your notes ….. my notes
15. Visual and text summary of the chapter
16. Ready to use diagram for decision makers
17. Conclusion
Chapter 6: Technology offered by different vendors for Machine Learning.
Chapter Goal: This chapter will discuss about the technology offering from different leading vendors and provide real time case studies, scenarios and point of views
NO of pages: 20-30
Sub-Topics:
1. Machine Learning @ Microsoft
2. Big Data @ Microsoft
3. IOT @ Microsoft
4. HDInsight and data analytics … case study
5. Cortana analytics suit- case study
6. IBM Watson-Case study
7. Cognitive internet of things -Case study
8. Mind map for the chapter
9. Some important questions and their answers
10. Your notes …. My notes
11. Visual and text summery of the chapter
12. Ready to use diagram for decision makers
13. Conclusion
Chapter 7: Security and Machine Learning
Chapter Goal: This chapter will discuss about role of Machine Learning in the areas of security in different fields and domains
NO of pages: 20-30
Sub-Topics:
1. How Machine Learning is reshaping security
<2. Machine Learning forensics for law enforcement, security and intelligence3. Data mining and Machine Learning in cybersecurity
4. Machine Learning approach to phishing detection and defense
5. Mind map for the chapter
6. Some important questions and their answers
7. Your notes …. My notes
8. Visual and text summery of the chapter
9. Ready to use diagram for decision makers
10. Conclusion
Chapter 8: Matrices, KPI’s and more …. For Machine Learning ecosystem
Chapter Goal: This chapter will discuss about metrics, performance measures and KPI’s for Machine Learning, big data, IOT, cloud and cognitive computing. Focus will be Machine Learning, however it summarizes the same for associated technologies as well
NO of pages: 20-30
Sub-Topics:
1. Machine Learning matrixes
1.1 accuracy
1.2 Confusion Matrix
1.3 Prediction Threshold
2. Big Data related performance matrix
2.1 CPU time consumed
2.2 I/O wait time
2.3 Number of asynchronous prefaces
2.4 Objects accessed
2.5 Total elapsed time
3. IOT related matrix
3.1Mesuring all connected devices with IOT analytics
4. Cloud computing related matrix (generic)
4.1 Percentage of monitored applications
4.2 Percentage of apps met SLA
4.3 Average time to provision a node
4.5 Average time to deploy an application
5. Average delivery time of new products or services
6. Mind map for the chapter 7. Some important questions and their answers
8. Your notes …. My notes
9. Visual and text summery of the chapter
9.1 Ready to use diagram for decision makers
9.2 Conclusion
Chapter 9: Best practices and pattern for Machine Learning
Chapter Goal: This chapter will discuss some relevant best practices and pattern for Machine Learning and allied technologies.
NO of pages: 20-30
Sub-Topics:
1. Network security best practice
2. Data security and encryption best practices
3. Identity management and access control security best practices
4. Internet of things security best practices
5. Best practices for software update on Microsoft Azure Iaas
6. Azure boundary security best practices
7. Mind map for the chapter
8. Some important questions and their answers
9. Your notes …. My notes
10. Visual and text summary of the chapter
11. Ready to use diagram for decision makers
12. conclusion
Chapter 10: Recent advancement and future directions of Machine Learning
Chapter Goal: This chapter will discuss recent advancement and future directions of ML
NO of pages: 10-20
Sub-Topics:
1. BOT Framework
2. Case study - Microsoft chat BOT
3. Case study – Google Sheri
4. Microsoft Band
5. Collaborative IOT
6. Microsoft Cortana
7. IBM Bluemix
8. Amazon Alexa
Chapter 11: Conclusion
NO of pages: 3-5
Sub-Topics:
1.History and evaluation of Machine Learning
2.Human brain, AI, Big data, Cognitive computing, cloud and Machine Learning
3.Innovative new models and methodologies for Machine Learning
4.Important questions and their answers for Machine Learning
5.Further reading, bibliography, notes and references.
1.
