Machine Learning for Decision Makers

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 LearnDiscover the machine learning, big data, and cloud and cognitive computing technology stackGain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learningAbsorb machine-learning best practicesWho This Book Is ForManagers 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 : 30-40Sub -Topics1. What is Machine Learning2. DNA of ML3. Big Data and associated technologies4. What is cognitive computing by the way5. Let’s talk about internet of things (IOT)6. All this happens in cloud ….. Really!!7. Putting it all together8. Few professional point of views on Machine Learning technologies9. Mind Map for the chapter10. Visual and text summary of the chapter11. Ready to use diagrams for decision makers12. ConclusionChapter 2:  Fundamentals of Machine Learning and its technical ecosystemChapter 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: 40-50Sub - Topics1.  Evolution of ML2. Need for Machine Learning3. The Machine Learning business opportunity4. Concepts of Machine Learning4.1 Algorithm types for Machine Learning4.2 Supervised learning4.3 Machine Learning models4.5 Machine Learning life cycle5. Common programing languages for ML6. Data mining and Machine Learning7. Knowledge discovery and ML8. Types and architecture of Machine Learning9. Application and uses of Machine Learning10. Tools and frameworks of Machine Learning11. New advances in Machine Learning12. Tenets for large scale ML applications13. Machine Learning in IT organizations14. Machine Learning value creation 15. Case study16. Authors interpretation of case studies17. Few professional point of views18. Mind map for the chapter19. Some important questions and their answers20. Your notes …. My notes21. Visual and text summary of the chapter22. Ready to use diagram for the decision makers23. ConclusionChapter 3: Methods and techniques of Machine LearningChapter Goal: This chapter will discuss in details about the common methods and techniques of Machine LearningNo of pages: - 40-50 Sub - Topics:  1. Quick look on required mathematical concepts2. Decision trees2.1 The basic of decision tree2.2 How decision tree works2.3 Different algorithm types in decision tree2.4 Uses and applications of decision trees in enterprise2.5 Get maximum out of decision tree3.   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 networks4.    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 networks5.     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 learning6.      Support vector machines7.      Few professional point of views on Machine Learning technologies8.      Case study9.      Mind map for the chapter10.    Some important questions and their answers11.    Your notes…my notes12     Visual and text summary of the chapter13     Ready to use diagram of the decision makers14     ConclusionChapter 4: Machine Learning and its relationship with cloud, IOT, big data and cognitive computing in business perspectiveChapter 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: 40-50Sub - Topics: 1.     What is big about big data2.      Introduction to big data concepts3.      Big data technologies4.      Big data solutions5.      Fundamentals of cloud computing6.      Cloud computing technology stacks7.      Internet of things …. what is it all about8.      IOT technology stack9.      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.     ConclusionChapter 5: Business challenges and applications of Machine Learning big data, IOT, cloud and cognitive computing in different fields and domainsChapter 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.). 

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9781484229873
• Dimensioni: 235 x 155 mm Ø 813 gr
• Formato: Brossura
• Illustration Notes: XXXV, 355 p. 39 illus., 33 illus. in color.
• Pagine Arabe: 355
• Pagine Romane: xxxv