Machine Learning and Cognition in Enterprises: Business Intelligence Transformed

ISBN-13: 9781484230688 / Angielski / Miękka / 2017 / 306 str.

Rohit Kumar

Opis

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 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.

Spis treści

Chapter 1:  Journey of Business Intelligence

Chapter 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 : 20-25

Sub -Topics

1. Business Intelligence 

2. Why it started?

3. Initial use cases

4. Later use cases

5. Shifting paradigm of Business Intelligence 

6. Case Studies to differentiate

Chapter 2:  Why Cognitive and Machine Learning

Chapter Goal: This chapter moves around as extension to previous one and covers why cognitive was a natural choice of evolution

No of pages: 4-5

Sub - Topics

1.  Why Machine Learning and Artificial Intelligence were required

2. Why towards cognitive

Chapter 3: Machine Learning Basics

Chapter Goal: Introduction to Machine Learning basic and business examples to understand Business Intelligence.

No of pages : 30-35

Sub - Topics:  

1. Definition

2. Problem and task differentiation

3. Approaches to Machine Learning

4. Knowledge Discovery and search Methods

5. Statistics part of it

6. Applications  

7. Business use case examples

Chapter 4: Artificial Intelligence Basics

Chapter Goal: Introduction to Artificial Learning basics and business examples to understand usability from business user perspective.

No of pages: 20-25

Sub - Topics: 

1. Background and Overview

2. Why Artificial Intelligence 

3. Tools and approaches

4. Applications

5. Business use case examples

Chapt

er 5: Natural language Processing

Chapter Goal: Introduction to Natural Language processing basics and examples to understand usability user perspective 

Pages: 20-25

1. Overview

2. NLP and Machine Learning

3. How it works

4. Business use case examples

Chapter 6: Predictive analytics

Chapter Goal: Introduction to basics and examples to understand Business Intelligence from business user perspective 

Pages: 30-35

1. Overview

2. Types

3. Applications

4. Tools

5. Business use case examples

Chapter 7: Cognitive Computing

Chapter Goal: Introduction to basics and examples to understand Business Intelligence from business user perspective 

Pages: 30-35

1. What is cognition

2. Cognitive Architecture

3. Cognitive Computing Overview

4. Why cogni

tive

5.

How much is available now

6. Cognitive computer

7. Applications

8. Business use cases

Chapter 8: Principle for cognitive designed systems

Chapter 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: 35-40

1. What it means

2. Approach

3. Cognitive Work Analysis

4. Functional Workspace design

5. Corpus building and indexing

6. Sources of data and knowledge

7. Training cognitive systems

8. Interfaces

9. Do they fail too?

10. Components of Machine Learning and Artificial Intelligence 

Chapter 9: New Term : Self/Parallel Evolving IT-Business Intelligence systems

Chapter 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: 35-40

1. Boundary between IT and Business

2. Where they stand today

3. Where they head to

4. Why this is game changer

5. Is this too complicated

6. What is the new IT for business?

7. Future trends and predictions

8. Hypothetical case study to summarize

9. Summary

Chapter 10: Roadmap for Transformation

Chapter Goal: Extension to previous chapter and talks about how to achieve the same.

Pages: 40-45

1. Digital business transformation

2. Why required?

3. Elements of digital transformation

4. Holistic Transformation and Optimization

5. Some industry sector e

xamples

6. Sample/Example Roadmaps

7. Summary

Chapter 11: Roadblocks for Transformation

Chapter Goal: Extension to previous chapter and talks about roadblocks.

Pages: 20-25

1. Bare reality of transformation

2. Cognitive vs Other Transformation

3. Change the way you draw transformation path

4. Prime stakeholders

5. Timing right entry at right time

6. Risks and Perceptions

7. What to expect

8. Reality check

9. Summary

Chapter 12: Self evolving Cognitive business systems

Chapter 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: 20-25

1. What it is and what not

2. New

age of Business and IT collaboration

3. Future of IT and Business Intelligence for business

4. Some business scenario use cases

5. Summay

Chapter 13: Case Studies

Chapter Goal: Case studies from 5 industries.

Pages: 20-25

1. Case study 1

2. Case study 2

3. Case Study 3

4. Case study 4

5. Case study 5

Chapter 14: IBM Watson

Chapter Goal: This chapter describes Watson

Pages: 20-25

6. Know Watson up to barebones

7. Jeopardy

8. Applications

9. Learning options

10. Interface resources

Chapter 15: Path ahead

Chapter 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: 10-15

1. Path Ahead:  Organizat

ions

2.

Path Ahead: IT Service Organizations

3. Path Ahead: IT consultants.

O autorze

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.

Blurb

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.

You will:

• 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