Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making

Table of Contents
About the Author
About the Technical Reviewer
Chapter 1: Let’s Integrate with Machine Learning
	Your Business, My Technology, and Our Interplay of Thoughts
	General Introduction to Machine Learning
	The Details of Machine Learning
	Quick Bytes
	Supervised Learning
	Unsupervised Learning
	Characteristics of Machine Learning
	Current Business Challenges for Machine Learning
		Handling, Managing, and Using Complex and Heterogeneous Data
		Dynamic Business Scenarios, Systems, and Methods
		Unpredictable System Behavior
	The Needs and Business Drivers of Machine Learning
	What Are Big Data and Big Data Analytics?
		The Major Sources of Big Data
		The Three Vs of Big Data
			Velocity
			Variety
			Volume
	What Is Analytics?
	What Is Cloud Computing?
		Essential Characteristics of Cloud Computing
		Cloud Computing Deployment Methodologies
		Cloud Computing Service Models
		Challenges of Cloud Computing
	What Is IoT?
		Evolution, Development, and the Future of IoT
		Jargon Buster
		Characteristics of IoT
			Connecting Non-Living and Living Things
			Collecting and Transmitting Data Through Sensors
			Communicating Over an IP Network
		Challenges with the Internet of Things
		How IoT Works
	What Is Cognitive Computing?
		How Cognitive Computing Works
		Characteristics of Cognitive Computing
		Nervana Systems: A Machine Learning Startup
	How the Cloud, IoT, Machine Learning, Big Data Analytics, and Cognitive Computing Work Together
	Video Link
	Summary
	Mind Map
Chapter 2: The Practical Concepts of Machine Learning
	Linking History, Evolution, Machine Learning, and Artificial Intelligence
	Jargon Buster
	Machine Learning, AI, the Brain, and the Business of Intelligence
	Jargon Buster
	General Architecture of Machine Learning
	Machine Learning: You and Your Data
	Technology Related to Machine Learning
	The Need for Machine Learning
	Machine Learning Business Opportunities
	Customer Experience Enrichment
		Automated Machine Learning Based Customer Support Systems
		A Tale of Customer Support and Automation
		Machine Learning Customer Retention Systems
		Business Success, Customer Engagement, and Machine Learning
			Appropriate Customer Acquisition
			Better Customer Support
			Customer Base Expands
			Customer Retention
		Customer Segmentation Applications and Products
		Intelligent Customer Prioritization and Classification Products, Applications, and Services (APS)
		Autonomous and Intuitive Systems
			Autonomous Systems
			The Latest Trends
			Self-Learning Machines Products, Applications, and Services
	How Are Big Language Models Like ChatGPT Using RLHF?
	Deep Learning and Simulated Neuron Based APS
	Emotions and Sentiment Analysis Based APS
	Other Intuitive Applications, Products, and Services
	Prediction, Digital Assistance, and Recommendation APS
		Recommendations Based Applications, Products, and Services
		Virtual Digital Assistance
	Advertising
	Phototagging
	Domain-Specific APS
	Financial and Insurance Services
	Telecom Network, Products, and Services
	Professional Services
	Public Sector and Government Initiatives
	Retail and Wholesale
	Transport
	Utilities, Oil, and Gas
	Manufacturing
		Machine Learning for Legal Activities
		Machine Learning to Prevent Money Laundering
		Improving Cybersecurity
	Science and Technology
		Medical Science
		Space Science
		Physics
		Biology
	Types of Machine Learning
		Reinforcement Learning
		Supervised Learning
		Unsupervised Learning
		Semi-Supervised Learning: A Quick Look
	Machine Learning Models
		Training ML Models
		Different Types of Algorithm-Based Models for Machine Learning
			Binary Classification Model
			Multiclass Classification Model
			Regression Model
	Tools for Machine Learning
		Jargon Buster
	Frameworks for Machine Learning
	Distributed Machine Learning
	Large-Scale Machine Learning
	Programming Languages for Machine Learning
		R
		Scala
		Python
	The Latest Advancements in Machine Learning
		Image-Based Recognition
		Case Study: Face Recognition
			Challenge
			Approach
			Result
		Healthcare
		Travel and Communications
		Advertising
		Jargon Buster
	More Case Studies
		Case Study: Machine Learning Text Analytics
			Challenges
			Approach
			Result
		Case Study: Automation Reduces Resolution Time by 50 Percent
			Challenges
			Approach
			Results
	Audio and Video Links
	Summary
	Mind Map
	Reference, Web Links, Notes, and Bibliography
Chapter 3: Machine Learning Algorithms and Their Relationship with Modern Technologies
	Algorithms, Algorithms, Everywhere
		Jargon Buster
		Machine Learning Algorithm Classifications
		Clustering
			Applications and Use Cases for Clustering
			When to Use Clustering
		Regression
			Applications and Use Cases of Regression
			When to Use Regression
		Classification
		Differences Between Classification and Regression
			Applications and Use Case for Classification
			When to Use Classification
		Anomaly Detection
			Applications and Use Cases of Anomaly Detection
			When to Use Anomaly Detection
	Building a Machine Learning Model
		Selecting the Right Algorithm/Model for Your Requirements
		Approaching the Problem
		Choosing the Correct Algorithm
			Step 1: Data Investigation and Finding Relationships Between Variables
			Step 2: Rational Choice and Efficient Comparison of Algorithms and Models
			Step 3: Cross-Validation
			Step 4: Properly Researched, Carefully Studied, Purified Data
			Step 5: Tool Selection, Ease of Use, and Availability of Infrastructure, Talent, and Other Resources
			Step 6: Determining Appropriate Objectives and Business Value
			Step 7: Learning and Developing Flexibility, Adaptability, Innovation, and Out-of-the-Box Thinking
	Expert Opinion
	A Review of Some Important Machine Learning Algorithms
		The Random Forest Algorithm
			Advantages of Random Forest
			Disadvantages of Random Forest
			Success Stories of Random Forest
		The Decision Tree Algorithm
			Advantages of Decision Trees
			Disadvantages of Decision Trees
			Applications of Decision Trees
			Success Stories
		Logistic (Classification) and Linear Regression
			Advantages of Logistic Regression
			Disadvantages of Logistic Regression
			Applications of Logistic Regression
			Success Stories
		Support Vector Machine Algorithms
			Advantages of SVM
			Disadvantages of SVM
			Applications of SVM
			Success Stories
		Naïve Bayes Algorithms
			Advantages of Naïve Bayes
			Disadvantages of Naïve Bayes
			Applications of Naïve Bayes
			Success Stories
		k-Means Clustering Algorithms
			Advantages of k-Means
			Disadvantages of k-Means
			Applications of k-Means
			Success Stories
		Apriori
			Advantages of Apriori
			Disadvantages of Apriori
			Applications of Apriori
			Success Stories
		Markov and Hidden Markov Models
			Advantages of Markov Models
			Disadvantages of Markov Models
			Success Stories
		Bayesian Networks and Artificial Neural Networks (ANNs)
			Advantages of ANN
			Disadvantages of ANN
			Applications of ANN
			Success Stories
	Machine Learning Application Building
	Agility, Machine Learning, and Analytics
		Why Do You Need Agile?
		Show Me Some Water Please
		Agile’s Disadvantages
		Agile Usage
	Some Machine Learning Algorithm-Based Products and Applications
	Algorithm-Based Themes and Trends for Businesses
		The Economy of Wearables
		New Shared Economy-Based Business Models
		Connectivity-Based Economies
		New Ways to Manage in the Era of the Always-On Economy
		Macro-Level Changes and Disrupted Economies
		The Marriage of IoT, Big Data Analytics, Machine Learning, and Industrial Security
		Startup Case Study: Belong
	Industry 4.0: IoT and Machine Learning Algorithms
	Review: Generative AI: A Miracle Lead by Machine Learning Technologies
		ChatGPT in the Corporation
		Risks with ChatGPT
		Trustworthy AI
	The Audio and Video Links
	Before Winding Up
	Summary
	Mind Map
Chapter 4: Technology Stack for Machine Learning and Associated Technologies
	Software Stacks
	Chapter Map
	The Internet of Things Technology Stack
	IoT, You, and Your Organization
	The Device and Sensor Layer
		Facts for You
	The Communication, Protocol, and Transportation Layer
	The Data Processing Layer
	The Presentation and Application Layer
		IoT Solution Availability
		Real-Life Scenarios
	The Big Data Analytics Technology Stack
	The Data Acquisition Integration and Storage Layer
		Hadoop Distributed Filesystem (HDFS)
			The Core Hadoop Architecture
			Salient Features of HDFS
		Amazon Simple Storage Service (S3)
	The Analytics Layer
		Hadoop MapReduce
			MapReduce Word Count Example
			Quick Facts About MapReduce
		Pig
		Apache Hive
		HBase
		MangoDB
	Apache Storm
		Apache Solr
	Apache Spark
		Azure HDInsight
	The Presentation and Application Layer
		Offerings from Vendors in the Big Data Space
		Real-Life Scenarios
	The Machine Learning Technology Stack
		The Connector Layer
			Logic Apps
			Apache Flume
			MQTT
			Apache Kafka
			Apache Sqoop
		The Storage Layer
		The Processing Layer
		The Model and Runtime Layer
			Apache Mahout
			Amazon’s Deep Scalable Sparse Tensor Network Engine (DSSTNE)
			Google TensorFlow
			Microsoft Cognitive Toolkit
			Microsoft M.NET
			Other Solutions
		The Presentation and Application Layer
	Real-Life Scenarios
	Role of Cloud Computing in the Machine Learning Technology Stack
	The Cognitive Computing Technology Stack
	Cognitive Computing vs Machine Learning
	Use Cases
	The Cloud Computing Technology Stack
	Audio and Video Links
	The Latest Research
	Summary
	Mind Map
Chapter 5: Industrial Applications of Machine Learning
	Abstract
	Data, Machine Learning, and Analytics
	What Is Machine Learning Analytics?
		The Need for Machine Learning Analytics
		Challenges Associated with Machine Learning Analytics
		Business Drivers of Machine Learning Analytics
		Industries, Domains, and Machine Learning Analytics
			Machine Learning-Based Manufacturing Analytics
			Challenges in Implementing Machine Learning in the Manufacturing Industry
		The Case of SCADA and PLC
		Tools for Data Analysis
			Automated Tools
			Integration of Data Analysis and Automation
			Benefits of Data Analysis and Automation
			Drivers of Machine Learning Analytics in the Manufacturing Industry
			Machine Learning-Based Analytics: Applications in the Manufacturing Industry
			Other Uses of Machine Learning Analytics in the Manufacturing Industry
		Machine Learning-Based Finance and Banking Analytics
			Challenges of Implementing Machine Learning Analytics in Bank and Financial Institutions
			Drivers of Machine Learning Analytics for Financial Institutions
			Machine Learning-Based Analytics: Applications in Financial Institutions
			Other Uses of Machine Learning Analytics in Financial Institutions
		Machine Learning-Based Healthcare Analytics
			Challenges in Implementing Machine Learning Analytics in the Healthcare Sector
			Drivers of Machine Learning Analytics in the Healthcare Industry
			Machine Learning Based Analytics: Applications in the Healthcare Industry
			Other Uses of Machine Learning Analytics in the Healthcare Industry
			Unique Applications of VR in Healthcare
		Machine Learning-Based Marketing Analytics
			Challenges of Machine Learning Analytics in Marketing
			Drivers of Machine Learning Analytics for Marketing
			Machine Learning Based Analytics: Applications in Marketing Analytics
			Jargon Buster
			Other Uses of Machine Learning Analytics in Marketing
		Audio and Video
		Machine Learning-Based Analytics in the Retail Industry
			Challenges in Implementing Machine Learning Analytics in the Retail Industry
			Drivers of Machine Learning Analytics in the Retail Industry
			Machine Learning Analytics Based Analytics: Applications in the Retail Industry
			Other Uses of Retail Machine Learning Analytics
		Customer Machine Learning Analytics
			Challenges in Implementing Customer Machine Learning Analytics
			Drivers of Customer Machine Learning Analytics
			Other Uses of Customer Machine Learning Analytics
		Machine Learning Analytics in Real Life
		Machine Learning Analytics in Other Industries
			Video Games
			Disaster and Hazards Management
			Transportation
			Hospitality
			Aviation
			Fitness
			Fashion
			Oil and Gas
			Advertising
			Entertainment
			Agriculture
			Telecommunications
			Insurance
	A Curious Case of Bots and Chatbots: A Journey from Physicality to Mindfulness
		How Bots Work
		Usability of Bots
		Bots and Job Loss
	Summary
	Mind Map
Chapter 6: I Am the Future: Machine Learning in Action
	State of the Art Examples
		Siri
		Alexa
		Google Assistant
		IBM Watson
		Microsoft Cortana
		Connected Cars
			Highlights of the Connected Car System
		Driverless Cars
	Machine and Human Brain Interfaces
	Virtual, Immersive, Augmented Reality
		Mixed Reality
			Different Mixed Reality Algorithms
		The Metaverse
			Infrastructure and Hardware of the Metaverse
	Startup Case Study: Absentia
	Google Home and Amazon Alexa
	Google Now
	Brain Waves and Conciseness Computing
	Machine Learning Platforms and Solutions
		SAP Leonardo
		Salesforce Einstein
	Security and Machine Learning
	The Indian Software Industry and Machine Learning
		Use Cases for These Products
	Quantum Machine Learning
	Practical Innovations
	Machine Learning Adoption Scorecard
	Summary
	Mind Map
Chapter 7: Innovation, KPIs, Best Practices, and More for Machine Learning
	Abstract
	IT, Machine Learning, Vendors, Clients, and Changing Times
	Designing Key Performance Indicators (KPIs) for Machine Learning Analytics-Based Domains
	The KPI and ML Teams
	Monitoring the KPIs
	Designing Effective KPIs Using a Balanced Scorecard
	Preparation
	Measurement Categories
	Benefits of KPIs
	Some Important KPIs from Specific Organization and Industry Perspectives
		Organization/Enterprise Specific Machine Learning KPIs
		Industry-Specific KPIs
		Stock and Customer Analytics KPIs
	Differences Between KPIs and Metrics
	Risk, Compliances, and Machine Learning
		Risk and Risk Management Processes for Machine Learning Projects
		Risk Identification
		Risk Assessment
		Risk Response Plan
		Monitoring and Controlling Risks
	Best Practices for Machine Learning
	Evolving Technologies and Machine Learning
	Summary
	Mind Map
Chapter 8: Do Not Forget Me: The Human Side of Machine Learning
	Economy, Workplace, Knowledge, You, and Technology
	Jargon Buster
	Key Characteristics of Intellectual Assets
		Bottom-Up Innovation
		Teamwork and Knowledge Sharing
		Adaptability to Change
		Customer Focus
		Spirituality
	Key Performance Drivers of Individuals
		Measuring Intelligence
		Benefits of the Intelligence Competencies
	Gamification
		Comics and Gamification
		Corporate Storytelling
	Building an Efficient ML Team in Relation to EQ, SQ, MQ, and Social Q
		Team Leader
		Technology Manager
		Team Members
		Organizational Leader
		The Differences Between a Leader and a Manager
	How to Build a Data Culture for Machine Learning
	Questions for Bringing Transparency to the Team and Enterprise
	Machine Learning-Specific Roles and Responsibilities
		Role 1: Deep Learning/Machine Learning Engineer
		Role 2: Data Scientist
		Other Important Roles
	Lean Project Management and Machine Learning Projects
	How to Do the Right Resourcing and Find the Best Match
	DevOps
		The Need for DevOps
		The Benefits of DevOps
	Summary
	Mind Map
Chapter 9: Quantum Computers, Computing, and Machine Learning: A Review
	Introduction
	Quantum Computers and Computing
		The Wave of Quantum
	Fundamentals of Quantum Computing
	Traditional Quantum Calculations
		Logic Gates
	Universal Computing Machine
		Quantum Mechanics
		Further Advancements of Quantum Theory
		The Structure Blocks of Quantum Mechanics
		Quantum Entanglement in Detail
		Superposition and Entanglement in a Quantum Computer
		Quantum Computing, Classical Computing, and Data Innovation
	Quantum Programming
		Algorithmic Complexity
		Quantum Gates
		The Quantum Gate Is a Unitary Matrix
		Quantum Algorithms
		Quantum Circuits
		Computations
		Quantum Registers vs Classical Registers
		Quantum Computer Algorithms
		Main Classes of Quantum Algorithms
		Important Quantum Algorithms
			Shor’s Algorithm
			Grover’s Algorithm
			Quantum Approximate Optimization Algorithm (QAOA)
		Translating Algorithms Into Programming Languages
		Qubit Details
		General Structure of a Quantum Computer System
		Quantum Software Example: Qiskit Aqua
			Input Generation
			Quantum Algorithms on Aqua
			User Experience
			Functionality
		Debugging a Quantum Program
		Quantum Simulators and Computers
		Quantum Computing, Artificial Intelligence and Machine Learning: The Basics
		The Interface Between Machine Learning and Quantum Computing
		Artificial Quantum Intelligence
		Quantum Machine Learning (QML)
		Machine Learning with Quantum Computers
		Quantum Neural Networks
		Quantum Computing Applications
		Cloud Quantum Computing
		Quantum Computing as a Service (QCaaS)
		Amazon Web Services (AWS) Runs Braket, A Quantum Computer as a Service
			How Amazon Braket Can Help
		The Current State of Quantum Computing
	Summary
Chapter 10: Let’s Wrap Up: The Final Destination
Index