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

✍ Scribed by Patanjali Kashyap

• Publisher: Apress
• Year: 2024
• Tongue: English
• Leaves: 676
• Category: Library

✦ Synopsis

This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry problems.

This second edition builds upon the foundation of the first book, revises all of the chapters, and updates the research, case studies, and practical examples to bring the book up to date with changes that have occurred in machine learning. A new chapter on quantum computers and machine learning is included to prepare you for future challenges. Insights for decision makers will help you understand machine learning and associated technologies and make efficient, reliable, smart, and efficient business decisions. All aspects of machine learning are covered, ranging from algorithms to industry applications. Wherever possible, required practical guidelines and best practices related to machine learning and associated technologies are discussed. Also covered in this edition are hot-button topics such as ChatGPT, superposition, quantum machine learning, and reinforcement learning from human feedback (RLHF) technology.

Upon completing this book, you will understand machine learning, IoT, and cognitive computing and be prepared to cope with future challenges related to machine learning.

What You Will Learn
Master the essentials of machine learning, AI, cloud, and the cognitive computing technology stack
Understand business and enterprise decision-making using machine learning
Become familiar with machine learning best practices
Gain knowledge of quantum computing and quantum machine learning

Who This Book Is For
Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them

✦ Table of Contents

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