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kashyap patanjali - machine learning for decision makers

Machine Learning for Decision Makers Cognitive Computing Fundamentals for Better Decision Making

Name: Machine Learning for Decision Makers
Price: 46,53 € EUR
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Author: Kashyap Patanjali
ISBN: 9781484229873

kashyap patanjali

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Dettagli

Genere:Libro

Lingua: Inglese

Editore:

Trama

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.

Sommario

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 stacks

7. 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 ind

Autore

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.).