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E-raamat: Machine Learning and Cognition in Enterprises: Business Intelligence Transformed

  • Formaat: EPUB+DRM
  • Ilmumisaeg: 13-Nov-2017
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484230695
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 13-Nov-2017
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484230695

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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.
About the Author xix
About the Technical Reviewer xxi
Acknowledgments xxiii
Introduction xxv
Chapter 1 Journey of Business Intelligence
1(26)
Business Intelligence
1(3)
Why & How It Started
4(3)
Going Ahead
5(1)
By the 1980s
5(2)
On Entering the 2000s
7(1)
Initial Use Cases
7(3)
Later Use Cases
10(2)
Shifting Paradigm
12(6)
Customer Relationship Management
15(1)
Market Research Analysis
16(1)
Loyalty Management
16(1)
Product Release
16(2)
Case Study
18(9)
BI Before Paradigm Shift
19(5)
BI With Paradigm Shift
24(3)
Chapter 2 Why Cognitive and Machine Learning?
27(6)
Artificial Intelligence (AI) and Machine Learning (ML)
27(1)
Why Artificial Intelligence and Machine Learning?
28(2)
Why Cognitive?
30(3)
Chapter 3 Artificial Intelligence---Basics
33(18)
Overview
33(7)
Goals of Artificial Intelligence
34(1)
Components of Artificial Intelligence
35(5)
Why AI?
40(1)
Approaches
41(4)
Symbolic Approaches
43(1)
Mixed Symbolic Approaches
44(1)
Agent-Oriented and Distributive Approaches
44(1)
Integrative Approaches
44(1)
Tools
45(3)
Logic Programming
45(1)
Automated Reasoning
46(1)
Search Algorithms
46(2)
Artificial Neural Networks
48(1)
Summary
48(3)
Chapter 4 Machine Learning---Basics
51(14)
Machine Learning
51(4)
Machine Learning Tasks
55(2)
Classification
55(1)
Clustering
55(2)
Regression
57(1)
Connected Key Concepts
57(4)
Deep Learning
57(3)
Genetic Algorithms
60(1)
Decision Tree and Association Rule
60(1)
Bayesian Network
60(1)
Speech Recognition
60(1)
Biosurveillance
61(1)
Machine Learning vs. Statistics
61(1)
Business Use Case Example
62(3)
Chapter 5 Natural Language Processing
65(10)
Natural Language
65(1)
Natural Language Processing---Overview
66(2)
NLP and Machine Learning
68(1)
How NLP Works
69(3)
Words and Letters First
69(1)
Sentences Come After
70(2)
Pragmatics
72(1)
Business Cases
72(1)
Chatbots
72(1)
Spam Filters
72(1)
Sentiment Analysis
73(1)
Search Engines
73(1)
Question Answering
73(1)
Summary
73(2)
Chapter 6 Predictive Analytics
75(24)
Overview
75(2)
Data Relevancy
77(4)
Fresh and Genuine
78(1)
Avoid Noise
79(1)
Avoid Personal or Sensitive Data
80(1)
Data Retention Period
81(1)
Past, Current, and Future Value
81(1)
Consistent and Not a Liability
81(1)
Outdated or Out of Purpose
82(1)
Predictive Analytics---Process
82(3)
Sources and Storage
84(1)
Data Modeling
84(1)
Analytics
85(1)
Reporting
85(1)
Types of Analytics
85(6)
Descriptive Analytics
88(1)
Diagnostic Analytics
89(1)
Prescriptive Analytics
90(1)
Tools
91(2)
SAP HANA Predictive Analytics
92(1)
Apache Mahout
92(1)
IBM SPSS
92(1)
SAS
92(1)
Statistical
92(1)
Oracle Advanced Analytics
92(1)
Actuate
93(1)
Mathematica
93(1)
Some Applications
93(6)
Manufacturing
93(1)
Marketing and Demand Management
94(1)
Predictive Maintenance
95(1)
Flexi Pricing
96(1)
Weather Forecast
97(1)
Epidemic Management
97(1)
R&D
97(2)
Chapter 7 Cognitive Computing
99(30)
Cognition
99(4)
Cognitive Computing
103(4)
Cognitive Era
107(2)
Cognitive Architecture
109(2)
Soar
110(1)
ACT-R
110(1)
CMAC
110(1)
CLARION
110(1)
Cognitive Chip
111(1)
Why Cognitive?
111(5)
Was Always Desired
113(1)
Big Data and Insights
113(1)
Advisory
114(1)
IoT Leverage
114(1)
Business Continuity
114(1)
Utilize Resources
114(1)
Efficiency
114(1)
Customer Satisfaction
115(1)
Customized Care
115(1)
More Ad Hoc
115(1)
Generate What's Required
116(1)
Look Inside
116(5)
Cognitive + IoT
121(1)
Use Cases
122(4)
Cybersecurity
122(1)
Oil and Gas
123(1)
Healthcare
124(1)
Clinical Trials
125(1)
Summary
126(3)
Chapter 8 Principles for Cognitive Systems
129(34)
Design Principles
129(10)
Identify Problem Area or Need
131(1)
High-Level Scoping
131(1)
References
132(1)
Feasibility and Rescoping
132(1)
Identify Finer Requirements or Associations
133(1)
Blueprinting
134(1)
Detailed Execution Plan
135(2)
Identify Validation Checks
137(1)
Develop Pilot or Test
137(1)
Validation
138(1)
Fix Defects or Deviations
138(1)
Optimize
138(1)
Feedback Assessment
138(1)
Cognitive Design Principle
139(7)
Identify Problem Area or Need
139(2)
High-Level Scoping
141(1)
References
141(1)
Feasibility and Rescoping
141(1)
Identify Finer Requirements or Associations
141(1)
Blueprinting
141(1)
Detailed Execution Plan
142(3)
Identify Validation Checks
145(1)
Develop Pilot or Test
146(1)
Fix Defects or Deviations
146(1)
Optimize
146(1)
Feedback Assessment
146(1)
Cognitive Work Analysis
146(2)
Workspace Design
148(1)
Cognitive Knowledge Generation and Sources
148(8)
Language Capabilities (Natural Language Processing)
150(1)
Conversation or Communication Capabilities
150(1)
Connectivity with All Sources of Data
151(1)
Ability to Process All Types of Data
151(1)
Knowledge Generation Capability
152(1)
Recommendation Capabilities
152(1)
Knowledge Generation---Some Details
152(4)
Interfaces
156(2)
Web-Based
156(1)
IoT Interfaces
157(1)
User Inputs
157(1)
Business Data
157(1)
Standard Data Feeds
158(1)
Financial and Business Networks
158(1)
Itself
158(1)
Failed Machines
158(1)
Relation with Artificial Intelligence and Machine Learning
159(2)
Summary
161(2)
Chapter 9 Parallel Evolving IT-BI Systems
163(32)
Where Do We Go from Here?
163(1)
IT and Business Relations Today
164(6)
Business and IT Scopes
165(1)
Business & IT Relationship: Boundary
166(4)
IT and Business Relationship: Where Is It Heading?
170(4)
Global Economics
171(1)
Bountiful Data
171(1)
Analytics
172(1)
Speed to Market
172(1)
Customer Relations
172(1)
Look Inside
173(1)
Collaboration Is Key
173(1)
What Are Parallel Evolving IT-BI Systems?
174(8)
Where Is This Placed?
178(3)
Working Through a Plan
181(1)
Properties of PEIB Framework
182(7)
Cognitive Capabilities
183(1)
No Differentiation
183(1)
Filling the Gaps
184(1)
Single Version of Truth
184(1)
Somatic Control
185(1)
Sensory Reflexes
185(1)
Unitary Decision Model
185(1)
Responsive
186(1)
Collaborative to Core
187(1)
Optimal Effort
187(1)
Simplicity
187(1)
Data Driven
188(1)
Beyond the Surface
188(1)
Is It Complicated?
189(1)
Why Is This a Game Changer?
190(1)
Case Study
191(2)
Summary
193(2)
Chapter 10 Transformation Roadmap
195(38)
Some Facts
195(3)
IT Expense Priorities
195(2)
Latest Trends
197(1)
Enterprise or Business Size
197(1)
PEIB Framework
198(2)
Initialize
200(1)
Formalize
201(23)
Scenario Arrival Matrix
202(4)
Scenario Checklist
206(9)
Scenario Priority List
215(1)
Knowledge and KPI Checklist
216(8)
Develop
224(4)
Blueprint and Design
225(1)
Development and Training
226(1)
Validation and User Feedback
227(1)
Deployment
227(1)
Integrate
228(1)
About the Costs
228(1)
Digital Transformation
229(1)
A Holistic Transformation
230(1)
Cognitive in Manufacturing
230(1)
Summary
231(2)
Chapter 11 Transformation Road Blockers
233(28)
Method or Scope Related
234(2)
Use Case Relevance
234(1)
Assorted Demos
235(1)
Traditional Methods
236(1)
Planning Related
236(6)
Strategy
236(1)
Top Down
237(1)
Wrong Initiation
238(1)
Vendor Entry
238(1)
Milestone Tracking
239(1)
Right Involvement
239(1)
Knowledge Resource
240(1)
Resource Alignment
240(2)
Implementation Related
242(2)
Scope and Blueprint Alignment
242(1)
Anchor Points
243(1)
System Training
243(1)
Adoption Related
244(5)
Ignorance
244(1)
Cost Factor
245(1)
Industry Core Absence
245(3)
Culture Shock
248(1)
Maturity and Enrichment Plan
248(1)
Limited Release
249(1)
Risks
249(9)
People-Related Risks
250(3)
Process-Related Risks
253(3)
Technology-Related Risks
256(1)
Management-Related Risks
257(1)
Summary
258(3)
Chapter 12 Self-Evolving Cognitive Business
261(18)
Conventional vs. Cognitive Self-Evolving Systems
262(10)
Silos
262(4)
Mixing Areas for Decision
266(1)
Leadership Effectiveness
267(2)
Expansion and Integration Issues
269(2)
Wastage and Overkill
271(1)
HR Management
271(1)
What Then Is Self-Evolving Business?
272(1)
Reality Check About Cognitive
273(4)
Is Cognitive for Everyone?
274(1)
Is Cognitive Foolproof?
275(1)
Are All Vendors/Products Good?
276(1)
Self-Cognitive Capability?
276(1)
Is Cognitive Taking Jobs Away?
277(1)
Summary
277(2)
Chapter 13 Path Ahead
279(20)
Path Ahead: Implementing Organizations
279(7)
Strategic Value
280(1)
Transform People and Culture
281(1)
Top Down
282(1)
Time Taking
282(1)
Customization and Usage
282(1)
Right Partner and Solution
283(1)
Three Keys
283(2)
Decentralization
285(1)
Momentum
285(1)
Security
285(1)
Path Ahead: IT Service and Product Companies
286(7)
Strategy Build
287(1)
Product Quality
288(2)
Incubation and Inculcation
290(1)
Market Outreach
290(1)
Center of Excellence (COE)
291(1)
Project Management COE
292(1)
Collaboration
292(1)
Skill Development and Maturity
292(1)
Path Ahead: IT Consultants
293(4)
Existing Workforce
294(2)
New Workforce
296(1)
Thanks and Goodbye!
297(2)
Index 299
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.