1Preface
1657Everything about Text Mining, Sentiment Analysis, and Social Analytics
2PART I - Introduction to Analytics and AI
1667.1Introduction to Text mining
31Introduction to Business Analytics and AI
1677.2Text Mining Process
41.1Business Analytics
1687.3Text Mining Applications: 7.3.1Domains of Text Mining
51.1.1Types of Business Analytics
1697.4Introduction to Sentiment Analysis
61.1.2Challenges to Business Analytics
1707.4.1Types of Sentiment Analysis
71.1.3How Business Analytics Work
1717.4.2Sentiment Analysis Approaches
81.1.4Applications of Business Analytics
1727.4.3Sentiment Analysis Metrics and Evaluation
91.1.5Business Analytics vs Data Science
1737.4.4Sentiment Analysis Challenges
101.2Decision Support Systems (DSS)
1747.4.5Sentiment Analysis Applications
111.2.1Development Framework
1757.5Social Media Analytics: 7.5.1Types of Social Media Analytics
121.2.2Applications of Decision Support Systems
1767.6Role in Business Intelligence
131.3Business Intelligence
1777.7References
141.3.1Business Intelligence vs Competitive Intelligence
178PART III - Prescriptive Analytics and Big Data
151.3.2Data
1798Prescriptive Analysis with Optimization and Simulation
161.3.3Applications of Business Intelligence
1808.1Introduction
171.4Data Science
1818.2Introduction to Prescriptive Analysis
181.4.1Data Science vs Business Intelligence
1828.3Using Prescriptive Analysis with Optimization Techniques
191.4.2Lifecycle of Data Science
1838.4Enhancing the Effect of Prescriptive Analysis using Simulation
201.5Artificial Intelligence: 1.5.1Applications of AI
1848.5References
211.6References
1859Brief about Big Data, Location Analytics, and Cloud Computing
222Artificial Intelligence: Concepts, Drivers, Major Technologies and Business Applications
1869.1Big Data
232.1Introduction
1879.1.1Volume
242.2Concept behind Artificial Intelligence
1889.1.2Variety
252.3Drivers to the Development of Artificial Intelligence
1899.1.3Veracity
262.4Major Technologies which are prevalent in Artificial Intelligence
1909.1.4Velocity
272.4.1Decision Management
1919.1.5Value
282.4.2Generation of Natural Language
1929.2Location Analytics
292.4.3Optimized Hardware
1939.2.1Ground Truth
302.4.4Biometrics
1949.2.2Placer.ai
312.4.5Analysis of Text
1959.2.3ShopperTrak
322.4.6Automation by Robotic Processes
1969.2.4RetailNext
332.4.7Virtual Agents
1979.2.5CountBOX
342.5Major Business Applications of Artificial Intelligence
1989.2.6RetailFlux
352.5.1Fraud Detection in Banking and Finance
1999.2.7Dor
362.5.2Customer Support
2009.3Cloud Computing: 9.3.1Three Models of Cloud Computing
372.5.3Security
2019.4References
382.5.4Human Resource Management
202PART IV - Everything about Robotics, Social Networks, AI, & IoT
392.5.5Forecasting Market Behavior
20310Robotics: Industrial and Consumer Applications
402.5.6Improved System Processes
20410.1Introduction
412.5.7Hazardous Business Operations
20510.2Industrial Applications of Robotics
422.6References
20610.2.1Material Handling Applications
433Nature of Data, Statistical Modeling, and Visualization
20710.2.2Processing Operations
443.1Nature of Data: 3.1.1Introduction
20810.2.3Assembly Applications
453.2Statistical Modeling
20910.2.4Inspection Operations
463.2.1Introduction
21010.3Consumer Application of Robotics
473.2.2Statistical Model Specification
21110.3.1Healthcare Assisting Robotics Applications
483.2.3Formal Definition and Dimensions of the Model
21210.3.2Education Applications of Robotics
493.2.4Statistical Inference
21310.3.3Personal Task Assistance Applications
503.2.5Statistical Hypothesis Testing
21410.3.4Consumer Goods Applications of Robotics
513.2.6Statistical Model Validation
21510.3.5Tax Automation Application
523.3Visualization
21610.4References
533.3.1Introduction
21711Brief about Group Decision Making, Collaborative Systems, and AI Support
543.3.2Uses of Data Visualization
21811.1Group Decision Making
553.3.3Tools and Common Types of Data Visualization
21911.2Group vs Individual Decision Making
563.4References
22011.3Techniques of the Effective Group Decision-making Process
57PART II-Predictive Analytics and Machine Learning
22111.4Important Tools for a Successful Decision Process
584Data Mining Process, Methods, and Applications
22211.5Collaboration System
594.1Introduction
22311.5.1History of Collaborative System
604.1.1Data from Databases
22411.5.2What is the Collaboration System?
614.1.2Data Warehouse
22511.5.3Circumstances of Collaborative System
624.1.3Transactional Data
22611.6Different Collaborations for Different Corporate Cultures
634.1.4Others
22711.6.1Positive Phases to Structured Collaboration
644.2Data Mining Models and Tasks
22811.6.2Weaknesses to Structured Collaboration
654.2.1Data Mining Tasks
22911.7How to Maintain a Collaborative System?
664.2.2Data Mining Models
23011.7.1Power Distribution
674.2.3Data Mining Techniques
23111.7.2Relationships
684.3Data Mining Process
23211.7.3Value
694.3.1Business Understanding
23311.7.4Preparing a Collaborative System
704.3.2Data Understanding
23411.8Artificial Intelligence History
714.3.3Data Preparation
23511.9Importance of Artificial Intelligence
724.3.4Modeling
23611.10Use of Artificial Intelligence
734.3.5Evaluation of the Model
23711.10.1In Health Care
744.3.6Plan Deployment
23811.10.2In Retail
754.4Issues in Data Mining
23911.10.3In Manufacturing
764.4.1Mining Various Kinds of Knowledge
24011.10.4In Banking
774.4.2User Interaction
24111.11How to Work with Artificial Intelligence?: 11.11.1Which way Artificial Intelligence works
784.4.3Efficiency and Scalability
24211.12Various Technologies that Allow and Help AI
794.5Applications of Data Mining
24311.13In Today’s World, What is AI?
804.6References
24411.14References
815Machine Learning Techniques for Predictive Analytics
24512Everything about Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors
825.1Machine Learning
24612.1What is Knowledge Systems?
835.2Predictive Analytics
24712.1.1Knowledgebase
845.3Working Pattern of Machine Learning
24812.1.2Inference Engine
855.4Machine Learning in Daily Life
24912.1.3The Advantages of Knowledge Systems
865.5Some Machine Learning Methods
25012.2Expert System (Skilled Systems)
875.5.1Supervised Machine Learning Algorithms
25112.2.1Definition
885.5.2Unsupervised Machine Learning Algorithms
25212.2.2Characteristics of Expert Systems
895.5.3Semi-supervised Machine Learning Algorithms
25312.2.3Capabilities of an Expert System
905.5.4Reinforcement Machine Learning Algorithms
25412.2.4Necessities of Economical Einsteinium User Interface
915.6Requirements for Creating Good Machine Learning Systems
25512.2.5Applications of Expert Systems
925.7Tools used in Machine Learning
25612.2.6Advantages of Expert Systems
935.7.1Scikit-learn
25712.2.7Disadvantages of Expert Systems
945.7.2PyTorch
25812.2.8Few Example Cases wherever a Skilled System is often Designed
955.7.3TensorFlow
25912.2.9Benefits of Skilled Systems
965.7.4Weka
26012.3Recommenders
975.7.5KNIME
26112.3.1What are Recommenders?
985.7.6Colab
26212.3.2Hybrid Systems
995.7.7Apache Mahout
26312.3.3Evaluation of Recommenders
1005.7.8Accord.Net
26412.3.4Domains
1015.7.9Shogun
26512.3.5Conversable Recommendation
1025.7.10Keras.io
26612.4Chatbots
1035.8Branches of Machine Learning
26712.4.1ELIZA
1045.8.1Computational Learning Theory
26812.4.2Development of Chatbots
1055.8.2Adversarial Machine Learning
26912.4.3Limitations of Chatbots
1065.8.3Quantum Machine Learning
27012.5Virtual Personal Assistants
1075.8.4Predictive Analysis
27112.5.1Presence of Virtual Personal Assistants
1085.8.5Robot Learning
27212.5.2Services
1095.8.6Grammar Induction
27312.5.3Security Concerns
1105.8.7Meta-learning
27412.6Robo Advisors
1115.9Comparison
27512.6.1Working of Robo Advisors
1125.9.1Machine Learning vs AI
27612.6.2Benefits of using a Robo Advisor
1135.9.2Machine Learning vs Deep Learning
27712.7References
1145.10Predictive Analytics Tools
27813Using IoT as a Platform for Intelligent Applications
1155.11Predictive Analytics Models
27913.1Introduction
1165.12Machine Learning and Predictive Analytics
28013.2Internet of Things (IoT)
1175.13Relation between Machine Learning and Predictive Analytics
28113.2.1Need for IoT
1185.145Application of Machine Learning
28213.2.2Applications of IoT
1195.14.1Genomics
28313.2.3Future Scope of IoT
1205.14.2Proteomics
28413.3Intelligent Applications: 13.3.1Categories
1215.14.3Microarrays
28513.4Use of IoT in Intelligent Applications
1225.14.4System Biology
28613.4.1IoT in Everyday Life
1235.14.5Text Mining
28713.4.2IoT in Healthcare
1245.14.6Web Search Engine
28813.4.3IoT in Smart cities
1255.14.7Photo Tagging Applications
28913.4.4IoT in Agriculture
1265.14.8Spam Detector
29013.4.5IoT in Industrial Automation
1275.15Application of Predictive Analytics
29113.4.6Role of IoT in Disaster Management
1285.15.1Price Prediction
29213.4.7IoT in Security
1295.15.2Dosage Prediction
29313.5Challenges in Building IoT Applications
1305.15.3Document Classification
29413.6References
1315.15.4Diagnosis
295PART V - Caveats of Analytics and AI
1325.16Application of Machine Learning and Predictive Analytics
29614Implementation Issues: From Ethics and Privacy to Organizational and Societal Impact
1335.16.1Banking and Financial Services
29714.1Ethical Issues
1345.16.2Security
29814.1.1Reinforcing Biases
1355.16.3Retail
29914.1.2Evil Slaves
1365.17References
30014.1.3Accountability
1376Brief about Deep Learning and Cognitive Computing
30114.1.4Currency
1386.1Deep Learning
30214.1.5Robotics in Warfare
1396.2History of Deep Learning
30314.1.6Robotics in Healthcare
1406.3Importance of Deep Learning
30414.1.7Digital Divide
1416.4Models of Deep Learning
30514.2Privacy Issues
1426.4.1DBN (Deep Belief Network)
30614.2.1Randomization Methods
1436.4.2BM (Boltzmann Machine)
30714.2.2The k-anonymity and l-diversity Methods
1446.4.3RBM (Restricted Boltzmann machine)
30814.2.3Distributed Privacy Preservation
1456.4.4DNN(Deep Neural Network)
30914.2.4Downgrading the Effectiveness of Data Mining Results
1466.5Algorithm
31014.3Differential Privacy
1476.6Classes of Deep Learning
31114.3.1Ownership of Data
1486.6.1Deep Networks for Unsupervised or Generative Learning
31214.3.2Transaction of Data
1496.6.2Deep Networks for Supervised Learning
31314.3.3Consent
1506.6.3Hybrid Deep Networks
31414.4Organizational Impact
1516.7Neural Network
31514.5Societal Impacts
1526.7.1ANN (Artificial Neural Networks) and Deep Learning
31614.5.1Singularity
1536.7.2Feed-forward Neural Network
31714.5.2Impact on Humanity
1546.8Applications of Deep Learning
31814.5.3Political Impact
1556.9Introduction to Cognitive Computing
31914.5.4Robot Rights
1566.10Cognitive Computing : 6.10.1Computer Vision
32014.5.5Environmental Concerns
1576.11Cognitive Analytics
32114.6Summary
1586.12Cognitive Models
32214.7References
1596.13Features of Cognitive Computing
323Appendix A: Abbreviations
1606.14Tools for Cognitive Systems Design
324Appendix B: Figures
1616.15Cognitive Computing and AI
325Appendix C: Graphs & Tables
1626.16Advantages of Cognitive Computing
326Graphs
1636.17Application of Cognitive Computing
327Tables
1646.18References
328Index
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