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Author Kumar, Rohit, author.

Title Machine learning and cognition in enterprises : business intelligence transformed / Rohit Kumar.

Publication Info. [Berkeley, CA] : Apress, [2017]


Location Call No. OPAC Message Status
 Axe Books 24x7 IT E-Book  Electronic Book    ---  Available
Description 1 online resource (xxviii, 306 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Note Online resource; title from digital title page (viewed on June 15, 2018).
Summary 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.
Contents At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Journey of Business Intelligence; Business Intelligence; Why & How It Started; Going Ahead; By the 1980s; On Entering the 2000s; Initial Use Cases; Later Use Cases; Shifting Paradigm; Customer Relationship Management; Market Research Analysis; Loyalty Management; Product Release; Case Study; BI Before Paradigm Shift; BI With Paradigm Shift; Chapter 2: Why Cognitive and Machine Learning?; Artificial Intelligence (AI) and Machine Learning (ML).
Why Artificial Intelligence and Machine Learning?Why Cognitive?; Chapter 3: Artificial Intelligence‚#x80;#x94;Basics; Overview; Goals of Artificial Intelligence; Components of Artificial Intelligence; Learning; Supervised Learning; Unsupervised Learning; Reinforcement Learning; Sensing; Acting; Reasoning and Problem Solving; Interpreting Language; Planning; Why AI?; Approaches; Symbolic Approaches; Mixed Symbolic Approaches; Agent-Oriented and Distributive Approaches; Integrative Approaches; Tools; Logic Programming; Automated Reasoning; Search Algorithms; Artificial Neural Networks; Summary.
Chapter 4: Machine Learning‚#x80;#x94;BasicsMachine Learning; Machine Learning Tasks; Classification; Clustering; Regression; Connected Key Concepts; Deep Learning; Genetic Algorithms; Decision Tree and Association Rule; Bayesian Network; Speech Recognition; Biosurveillance; Machine Learning vs. Statistics; Business Use Case Example; Chapter 5: Natural Language Processing; Natural Language; Natural Language Processing‚#x80;#x94;Overview; NLP and Machine Learning; How NLP Works; Words and Letters First; Sentences Come After; Pragmatics; Business Cases; Chatbots; Spam Filters; Sentiment Analysis.
Search EnginesQuestion Answering; Summary; Chapter 6: Predictive Analytics; Overview; Data Relevancy; Fresh and Genuine; Avoid Noise; Avoid Personal or Sensitive Data; Data Retention Period; Past, Current, and Future Value; Consistent and Not a Liability; Outdated or Out of Purpose; Predictive Analytics‚#x80;#x94;Process; Sources and Storage; Data Modeling; Analytics; Reporting; Types of Analytics; Descriptive Analytics; Diagnostic Analytics; Prescriptive Analytics; Tools; SAP HANA Predictive Analytics; Apache Mahout; IBM SPSS; SAS; Statistical; Oracle Advanced Analytics; Actuate; Mathematica.
Some ApplicationsManufacturing; Marketing and Demand Management; Predictive Maintenance; Flexi Pricing; Weather Forecast; Epidemic Management; R Chapter 7: Cognitive Computing; Cognition; Cognitive Computing; Cognitive Era; Cognitive Architecture; Soar; ACT-R; CMAC; CLARION; Cognitive Chip; Why Cognitive?; Was Always Desired; Big Data and Insights; Advisory; IoT Leverage; Business Continuity; Utilize Resources; Efficiency; Customer Satisfaction; Customized Care; More Ad Hoc; Generate What‚#x80;#x99;s Required; Look Inside; Cognitive + IoT; Use Cases; Cybersecurity; Oil and Gas; Healthcare.
Bibliography Includes bibliographical references.
Subject Machine learning.
COMPUTERS -- Computer Literacy.
COMPUTERS -- Computer Science.
COMPUTERS -- Data Processing.
COMPUTERS -- Hardware -- General.
COMPUTERS -- Information Technology.
COMPUTERS -- Machine Theory.
COMPUTERS -- Reference.
Machine learning. (OCoLC)fst01004795
Machine Learning.
Computers -- Machine Theory.
Psychology -- Cognitive Psychology & Cognition.
Software Engineering.
Algorithms & data structures.
Business mathematics & systems.
Artificial intelligence.
Genre/Form Electronic books.
Other Form: Print version: Kumar, Rohit. Machine Learning and Cognition in Enterprises : Business Intelligence Transformed. Berkeley, CA : Apress, ©2017 9781484230688
ISBN 9781484230695 (electronic bk.)
1484230698 (electronic bk.)
Standard No. 10.1007/978-1-4842-3069-5 doi
AU@ 000061333849
UKMGB 019127075

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