Patanjali Kashyap
Machine Learning for Decision Makers
Cognitive Computing Fundamentals for Better Decision Making
Paperback Engels 2018 9781484229873
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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 LearnDiscover 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 learningAbsorb 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.
Specificaties
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
<div><div>Chapter 1: Introduction.- </div><div>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.</div><div>No of pages : 30-40</div><div>Sub -Topics</div><div>1. What is Machine Learning</div><div>2. DNA of ML</div><div>3. Big Data and associated technologies</div><div>4. What is cognitive computing by the way</div><div>5. Let’s talk about internet of things (IOT)</div><div>6. All this happens in cloud ….. Really!!</div><div>7. Putting it all together</div><div>8. Few professional point of views on Machine Learning technologies</div><div>9. Mind Map for the chapter</div><div>10. Visual and text summary of the chapter</div><div>11. Ready to use diagrams for decision makers</div><div>12. Conclusion</div><div><br></div><div>Chapter 2: Fundamentals of Machine Learning and its technical ecosystem</div><div>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.</div><div>No of pages: 40-50</div><div>Sub - Topics</div><div>1. Evolution of ML</div><div>2. Need for Machine Learning</div><div>3. The Machine Learning business opportunity</div><div>4. Concepts of Machine Learning</div><div>4.1 Algorithm types for Machine Learning</div><div>4.2 Supervised learning</div><div>4.3 Machine Learning models</div><div>4.5 Machine Learning life cycle</div><div>5. Common programing languages for ML</div><div>6. Data mining and Machine Learning</div><div>7. Knowledge discovery and ML</div><div>8. Types and architecture of Machine Learning</div><div>9. Application and uses of Machine Learning</div><div>10. Tools and frameworks of Machine Learning</div><div>11. New advances in Machine Learning</div><div>12. Tenets for large scale ML applications</div><div>13. Machine Learning in IT organizations</div><div>14. Machine Learning value creation </div><div>15. Case study</div><div>16. Authors interpretation of case studies</div><div>17. Few professional point of views</div><div>18. Mind map for the chapter</div><div>19. Some important questions and their answers</div><div>20. Your notes …. My notes</div><div>21. Visual and text summary of the chapter</div><div>22. Ready to use diagram for the decision makers</div><div>23. Conclusion</div><div><br></div><div>Chapter 3: Methods and techniques of Machine Learning</div><div>Chapter Goal: This chapter will discuss in details about the common methods and techniques of Machine Learning</div><div>No of pages: - 40-50 </div><div>Sub - Topics: </div><div>1. Quick look on required mathematical concepts</div><div>2. Decision trees</div><div>2.1 The basic of decision tree</div><div>2.2 How decision tree works</div><div>2.3 Different algorithm types in decision tree</div><div>2.4 Uses and applications of decision trees in enterprise</div><div>2.5 Get maximum out of decision tree</div><div>3. Bayesian networks</div><div> 3.1 The basics of Bayesian networks</div><div> 3.2 Hoe Bayesian network works</div><div> 3.3 Different algorithm types in Bayesian network</div><div> 3.4 Uses and applications of Bayesian network in enterprise</div><div> 3.5 Get maximum out of Bayesian networks</div><div>4. Artificial neural networks</div><div> 4.1 The basics of Artificial neural networks</div><div> 4.2 How Artificial neural networks </div><div> 4.3 Different algorithm types in Artificial neural networks</div><div> 4.4 Uses and applications of Artificial neural networks in enterprise</div><div> 4.5 Get maximum out of Artificial neural networks</div><div>5. Association rules learning</div><div> 5.1 The basics of Association rules learning</div><div> 5.2 How artificial Association rules learning</div><div> 5.3 Different algorithm types in Association rules learning</div><div> 5.4 Uses and applications of Association rules learning in enterprise</div><div> 5.6 Get maximum out of Association rules learning</div><div>6. Support vector machines</div><div>7. Few professional point of views on Machine Learning technologies</div><div>8. Case study</div><div>9. Mind map for the chapter</div><div>10. Some important questions and their answers</div><div>11. Your notes…my notes</div><div>12 Visual and text summary of the chapter</div><div>13 Ready to use diagram of the decision makers</div><div>14 Conclusion</div><div><br></div><div>Chapter 4: Machine Learning and its relationship with cloud, IOT, big data and cognitive computing in business perspective</div><div>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.</div><div>No of pages: 40-50</div><div>Sub - Topics: </div><div>1. What is big about big data</div><div>2. Introduction to big data concepts</div><div>3. Big data technologies</div><div>4. Big data solutions</div><div>5. Fundamentals of cloud computing</div>6. Cloud computing technology stacks</div><div>7. Internet of things …. what is it all about</div><div>8. IOT technology stack</div><div>9. Modern solution architectures with real world IOT</div><div> 10. Building blocks of cognitive computing</div><div> 11. Big data and cognitive computing</div><div> 12. Cloud and cognitive computing</div><div> 13. Emerging cognitive computing areas</div><div> 14. Putting it all together</div><div> 15. Business insight</div><div> 16. Business optimization </div><div> 17. Case study 1</div><div> 18. Case study 2</div><div> 19. Authors interpretation of case studies</div><div> 20. Some important questions and their answers</div><div> 21. Few professional point of views</div><div> 22. Mind map for the chapter</div><div> 23. Your notes …… My notes</div><div> 24. Visual and text summary of the chapter</div><div> 25. Ready to use diagram for decision makers</div><div> 26. Conclusion</div><div><br></div><div>Chapter 5: Business challenges and applications of Machine Learning big data, IOT, cloud and cognitive computing in different fields and domains</div><div><br></div><div>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</div><div>NO of pages: 20-30</div><div>Sub-Topics:</div><div>1. Machine Learning and business value</div><div>2. Drivers of business value</div><div>3. Achieving customer delight and engagement with ML</div><div>4. Responsive systems and ML</div><div>5. Self-healing and Machine Learning</div><div>6. How advance analytics will take you</div><div>7. Case study- can we predict salary from historic data</div><div>8. Case study-big data as a service</div><div>9. Case study-connected cars</div><div>10. Application of ML across industries</div><div>10.1 Retail</div><div>10.2 Airline</div><div>10.3 Auto</div><div>10.4 Financial services</div><div>10.5 Energy</div><div>10.6 Data Warehousing</div><div>11. Few professional point of views on Machine Learning technologies</div><div>12. Mind map for the chapter</div><div>13. Some important questions and their answers</div><div>14. Your notes ….. my notes</div><div>15. Visual and text summary of the chapter</div><div>16. Ready to use diagram for decision makers</div><div>17. Conclusion</div><div><br></div><div>Chapter 6: Technology offered by different vendors for Machine Learning.</div><div><br></div><div>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</div><div>NO of pages: 20-30</div><div>Sub-Topics:</div><div>1. Machine Learning @ Microsoft</div><div>2. Big Data @ Microsoft</div><div>3. IOT @ Microsoft</div><div>4. HDInsight and data analytics … case study</div><div>5. Cortana analytics suit- case study</div><div>6. IBM Watson-Case study</div><div>7. Cognitive internet of things -Case study</div><div>8. Mind map for the chapter</div><div>9. Some important questions and their answers</div><div>10. Your notes …. My notes</div><div>11. Visual and text summery of the chapter</div><div>12. Ready to use diagram for decision makers</div><div>13. Conclusion</div><div><br></div><div><br></div><div>Chapter 7: Security and Machine Learning</div><div><br></div><div>Chapter Goal: This chapter will discuss about role of Machine Learning in the areas of security in different fields and domains</div><div>NO of pages: 20-30</div><div>Sub-Topics:</div><div>1. How Machine Learning is reshaping security</div><2. Machine Learning forensics for law enforcement, security and intelligence<div>3. Data mining and Machine Learning in cybersecurity</div><div>4. Machine Learning approach to phishing detection and defense</div><div>5. Mind map for the chapter</div><div>6. Some important questions and their answers</div><div>7. Your notes …. My notes</div><div>8. Visual and text summery of the chapter</div><div>9. Ready to use diagram for decision makers</div><div>10. Conclusion</div><div><br></div><div>Chapter 8: Matrices, KPI’s and more …. For Machine Learning ecosystem</div><div>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</div><div>NO of pages: 20-30</div><div>Sub-Topics:</div><div>1. Machine Learning matrixes</div><div>1.1 accuracy</div><div>1.2 Confusion Matrix</div><div>1.3 Prediction Threshold</div><div>2. Big Data related performance matrix</div><div>2.1 CPU time consumed</div><div>2.2 I/O wait time</div><div>2.3 Number of asynchronous prefaces</div><div>2.4 Objects accessed</div><div>2.5 Total elapsed time</div><div>3. IOT related matrix</div><div>3.1Mesuring all connected devices with IOT analytics</div><div> 4. Cloud computing related matrix (generic)</div><div> 4.1 Percentage of monitored applications</div><div> 4.2 Percentage of apps met SLA</div><div> 4.3 Average time to provision a node</div><div> 4.5 Average time to deploy an application</div><div> 5. Average delivery time of new products or services</div><div> 6. Mind map for the chapter 7. Some important questions and their answers</div><div> 8. Your notes …. My notes</div><div> 9. Visual and text summery of the chapter</div><div> 9.1 Ready to use diagram for decision makers</div><div> 9.2 Conclusion </div><div> </div><div>Chapter 9: Best practices and pattern for Machine Learning</div><div>Chapter Goal: This chapter will discuss some relevant best practices and pattern for Machine Learning and allied technologies.</div><div>NO of pages: 20-30</div><div>Sub-Topics: </div><div>1. Network security best practice</div><div>2. Data security and encryption best practices</div><div>3. Identity management and access control security best practices</div><div>4. Internet of things security best practices</div><div>5. Best practices for software update on Microsoft Azure Iaas</div><div>6. Azure boundary security best practices</div><div>7. Mind map for the chapter</div><div>8. Some important questions and their answers</div><div>9. Your notes …. My notes</div><div>10. Visual and text summary of the chapter</div><div>11. Ready to use diagram for decision makers</div><div>12. conclusion</div><div><br></div><div>Chapter 10: Recent advancement and future directions of Machine Learning</div><div><br></div><div>Chapter Goal: This chapter will discuss recent advancement and future directions of ML</div><div>NO of pages: 10-20</div><div>Sub-Topics:</div><div>1. BOT Framework</div><div>2. Case study - Microsoft chat BOT</div><div>3. Case study – Google Sheri</div><div>4. Microsoft Band</div><div>5. Collaborative IOT</div><div>6. Microsoft Cortana</div><div>7. IBM Bluemix</div><div>8. Amazon Alexa</div><div><br></div><div>Chapter 11: Conclusion</div><div><br></div><div>NO of pages: 3-5</div><div>Sub-Topics:</div><div>1.History and evaluation of Machine Learning</div><div>2.Human brain, AI, Big data, Cognitive computing, cloud and Machine Learning</div><div>3.Innovative new models and methodologies for Machine Learning</div><div>4.Important questions and their answers for Machine Learning</div><div>5.Further reading, bibliography, notes and references.</div><div><br></div><div>1.</div><div><br></div>
Rubrieken
- advisering
- algemeen management
- coaching en trainen
- communicatie en media
- economie
- financieel management
- inkoop en logistiek
- internet en social media
- it-management / ict
- juridisch
- leiderschap
- marketing
- mens en maatschappij
- non-profit
- ondernemen
- organisatiekunde
- personal finance
- personeelsmanagement
- persoonlijke effectiviteit
- projectmanagement
- psychologie
- reclame en verkoop
- strategisch management
- verandermanagement
- werk en loopbaan