Machine Learning and Cognition in Enterprises (eBook)
XXVIII, 306 Seiten
Apress (Verlag)
978-1-4842-3069-5 (ISBN)
Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it.
Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin
g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment.What You'll Learn
- See the history of AI and how machine learning and cognitive computing evolved
- Discover why cognitive computing is so important and why your business needs it
- Master the details of modern AI as it applies to enterprises
- Map the path ahead in terms of your IT-business integration
- Avoid common road blocks in the process of adopting cognitive computing in your business
Who This Book Is For
Business managers and leadership teams.
Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it.Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transforming your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. What You'll Learn See the history of AI and how machine learning and cognitive computing evolvedDiscover why cognitive computing is so important and why your business needs itMaster the details of modern AI as it applies to enterprisesMap the path ahead in terms of your IT-business integrationAvoid common road blocks in the process of adopting cognitive computing in your business Who This Book Is For Business managers and leadership teams.
Rohit Kumar has a Masters in Computer Science including Artificial Intelligence and Business Intelligence. He has been working as Sr. Enterprise Architect into Business Intelligence Data, Business. He has experience of consulting 30+ clients across globe in multiple industry verticals towards IT Transformation. Have experience with customers in various industries - e.g. FMCG, RetArtificial Intelligence, Pharmaceutical, Telecommunication, Electronic, Education, Manufacturing, Healthcare, Logistics, Utilities, Banking, Real Estate, Artificial Intelligence, E-Commerce, Publishing. Rohit also serves as guest lecturer for faculty development and PHD scholars for various universities while creating an extensive learning programs in SAP and Analytics for various organisations.
Chapter 1: Journey of Business IntelligenceChapter Goal: How and why Business Intelligence term was coined. What were the various phases of the same and until now why need is there to move to a different model with focus on Cognitive computing is required?No of pages : 20-25Sub -Topics1. Business Intelligence 2. Why it started?3. Initial use cases4. Later use cases5. Shifting paradigm of Business Intelligence 6. Case Studies to differentiateChapter 2: Why Cognitive and Machine LearningChapter Goal: This chapter moves around as extension to previous one and covers why cognitive was a natural choice of evolutionNo of pages: 4-5Sub - Topics 1. Why Machine Learning and Artificial Intelligence were required2. Why towards cognitiveChapter 3: Machine Learning BasicsChapter Goal: Introduction to Machine Learning basic and business examples to understand Business Intelligence.No of pages : 30-35Sub - Topics: 1. Definition2. Problem and task differentiation3. Approaches to Machine Learning4. Knowledge Discovery and search Methods5. Statistics part of it6. Applications 7. Business use case examplesChapter 4: Artificial Intelligence BasicsChapter Goal: Introduction to Artificial Learning basics and business examples to understand usability from business user perspective.No of pages: 20-25Sub - Topics: 1. Background and Overview2. Why Artificial Intelligence 3. Tools and approaches4. Applications5. Business use case examplesChapter 5: Natural language ProcessingChapter Goal: Introduction to Natural Language processing basics and examples to understand usability user perspective Pages: 20-251. Overview2. NLP and Machine Learning3. How it works4. Business use case examplesChapter 6: Predictive analyticsChapter Goal: Introduction to basics and examples to understand Business Intelligence from business user perspective Pages: 30-351. Overview2. Types3. Applications4. Tools5. Business use case examplesChapter 7: Cognitive ComputingChapter Goal: Introduction to basics and examples to understand Business Intelligence from business user perspective Pages: 30-351. What is cognition2. Cognitive Architecture3. Cognitive Computing Overview4. Why cognitive5. How much is available now6. Cognitive computer7. Applications8. Business use casesChapter 8: Principle for cognitive designed systemsChapter Goal: Introduction to concept. How a problem to solution map is created for enterprise IT – Business Intelligence landscape to create a cognitive and learning system. Pages: 35-401. What it means2. Approach3. Cognitive Work Analysis4. Functional Workspace design5. Corpus building and indexing6. Sources of data and knowledge7. Training cognitive systems8. Interfaces9. Do they fail too?10. Components of Machine Learning and Artificial Intelligence Chapter 9: New Term : Self/Parallel Evolving IT-Business Intelligence systemsChapter Goal:I have come up with this new concept and termed it as above. This has been largely appreciated and is a new dimension thought provoking concept of how cognition can go to the extent to become a self-evolving IT-Business Intelligence landscape and how it can grow parallel to business.Pages: 35-401. Boundary between IT and Business2. Where they stand today3. Where they head to4. Why this is game changer5. Is this too complicated6. What is the new IT for business?7. Future trends and predictions8. Hypothetical case study to summarize9. SummaryChapter 10: Roadmap for TransformationChapter Goal: Extension to previous chapter and talks about how to achieve the same.Pages: 40-451. Digital business transformation2. Why required?3. Elements of digital transformation4. Holistic Transformation and Optimization5. Some industry sector examples6. Sample/Example Roadmaps7. SummaryChapter 11: Roadblocks for TransformationChapter Goal: Extension to previous chapter and talks about roadblocks.Pages: 20-251. Bare reality of transformation2. Cognitive vs Other Transformation3. Change the way you draw transformation path4. Prime stakeholders5. Timing right entry at right time6. Risks and Perceptions7. What to expect8. Reality check9. SummaryChapter 12: Self evolving Cognitive business systemsChapter Goal: Talks about the concept of self-evolving business systems. Core concept dealt in chap 9. This extends after understanding complete concept up to transformation and roadblock to explain what it is and what not. And some detailed business examples.Pages: 20-251. What it is and what not2. New age of Business and IT collaboration3. Future of IT and Business Intelligence for business4. Some business scenario use cases5. SummayChapter 13: Case StudiesChapter Goal: Case studies from 5 industries.Pages: 20-251. Case study 12. Case study 23. Case Study 34. Case study 45. Case study 5Chapter 14: IBM WatsonChapter Goal: This chapter describes WatsonPages: 20-256. Know Watson up to barebones7. Jeopardy8. Applications9. Learning options10. Interface resourcesChapter 15: Path aheadChapter Goal: This chapter specifically answers in light of complete content of book three areas. Path ahead for organizations, IT Service organizations and IT consultants.Pages: 10-151. Path Ahead: Organizations2. Path Ahead: IT Service Organizations3. Path Ahead: IT consultants.
| Erscheint lt. Verlag | 13.11.2017 |
|---|---|
| Zusatzinfo | XXVIII, 306 p. 74 illus., 23 illus. in color. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik | |
| Schlagworte | Algorithm analysis and problem complexity • Artificial Intelligence • cognitive computing • IBM Watson • machine learning • Natural language understanding |
| ISBN-10 | 1-4842-3069-8 / 1484230698 |
| ISBN-13 | 978-1-4842-3069-5 / 9781484230695 |
| Haben Sie eine Frage zum Produkt? |
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