Artificial Intelligence for Business, 2nd Edition

Book description

Millions of non-technical professionals and leaders want to understand Artificial Intelligence (AI) and Machine Learning (ML) — whether to improve their businesses, be more effective citizens, consumers or policymakers, or just out of sheer curiosity. Until now, most books on the subject have either been too complicated and mathematical, or have simply avoided the big picture by focusing on the use of specific software libraries. In Artificial Intelligence for Business, Doug Rose bridges the gap, offering today’s most accessible and useful introduction to AI and ML technologies — and what they can and can’t do.

Rose begins by tracing AI’s evolution from the early 1950s to the present, illuminating core ideas that still drive its development. Next, he explores recent innovations that have reinvigorated the field by providing the “big data” that makes machine learning so powerful – innovations such as GPS, social media and electronic transactions. Finally, he explains how today’s machines learn by combining powerful processing, advanced algorithms, and artificial neural networks that mimic the human brain.

Throughout, he illustrates key concepts with practical examples that help you connect AI, ML, and neural networks to specific problems and solutions. Step by step, he systematically demystifies these powerful technologies, removing the fear, bewilderment, and advanced math — so you can understand the new possibilities they create, and start using them.

Table of contents

  1. Cover Page
  2. About This eBook
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Contents at a Glance
  7. Contents
  8. Foreword
  9. Preface
  10. PART I Thinking Machines: An Overview of Artificial Intelligence
    1. Chapter 1 What Is Artificial Intelligence?
      1. What Is Intelligence?
      2. Testing Machine Intelligence
      3. The General Problem Solver
      4. Strong and Weak Artificial Intelligence
      5. Artificial Intelligence Planning
      6. Learning over Memorizing
      7. Chapter Takeaways
    2. Chapter 2 The Rise of Machine Learning
      1. Practical Applications of Machine Learning
      2. Artificial Neural Networks
      3. The Fall and Rise of the Perceptron
      4. Big Data Arrives
      5. Chapter Takeaways
    3. Chapter 3 Zeroing in on the Best Approach
      1. Expert System Versus Machine Learning
      2. Supervised Versus Unsupervised Learning
      3. Backpropagation of Errors
      4. Regression Analysis
      5. Chapter Takeaways
    4. Chapter 4 Common AI Applications
      1. Intelligent Robots
      2. Natural Language Processing
      3. The Internet of Things
      4. Chapter Takeaways
    5. Chapter 5 Putting AI to Work on Big Data
      1. Understanding the Concept of Big Data
      2. Teaming Up with a Data Scientist
      3. Machine Learning and Data Mining: What’s the Difference?
      4. Making the Leap from Data Mining to Machine Learning
      5. Taking the Right Approach
      6. Chapter Takeaways
    6. Chapter 6 Weighing Your Options
      1. Chapter Takeaways
  11. PART II Machine Learning
    1. Chapter 7 What Is Machine Learning?
      1. How a Machine Learns
      2. Working with Data
      3. Applying Machine Learning
      4. Different Types of Learning
      5. Chapter Takeaways
    2. Chapter 8 Different Ways a Machine Learns
      1. Supervised Machine Learning
      2. Unsupervised Machine Learning
      3. Semi-Supervised Machine Learning
      4. Reinforcement Learning
      5. Chapter Takeaways
    3. Chapter 9 Popular Machine Learning Algorithms
      1. Decision Trees
      2. k-Nearest Neighbor
      3. k-Means Clustering
      4. Regression Analysis
      5. Näive Bayes
      6. Chapter Takeaways
    4. Chapter 10 Applying Machine Learning Algorithms
      1. Fitting the Model to Your Data
      2. Choosing Algorithms
      3. Ensemble Modeling
      4. Deciding on a Machine Learning Approach
      5. Chapter Takeaways
    5. Chapter 11 Words of Advice
      1. Start Asking Questions
      2. Don’t Mix Training Data with Test Data
      3. Don’t Overstate a Model’s Accuracy
      4. Know Your Algorithms
      5. Chapter Takeaways
  12. PART III Artificial Neural Networks
    1. Chapter 12 What Are Artificial Neural Networks?
      1. Why the Brain Analogy?
      2. Just Another Amazing Algorithm
      3. Getting to Know the Perceptron
      4. Squeezing Down a Sigmoid Neuron
      5. Adding Bias
      6. Chapter Takeaways
    2. Chapter 13 Artificial Neural Networks in Action
      1. Feeding Data into the Network
      2. What Goes on in the Hidden Layers
      3. Understanding Activation Functions
      4. Adding Weights
      5. Adding Bias
      6. Chapter Takeaways
    3. Chapter 14 Letting Your Network Learn
      1. Starting with Random Weights and Biases
      2. Making Your Network Pay for Its Mistakes: The Cost Function
      3. Combining the Cost Function with Gradient Descent
      4. Using Backpropagation to Correct for Errors
      5. Tuning Your Network
      6. Employing the Chain Rule
      7. Batching the Data Set with Stochastic Gradient Descent
      8. Chapter Takeaways
    4. Chapter 15 Using Neural Networks to Classify or Cluster
      1. Solving Classification Problems
      2. Solving Clustering Problems
      3. Chapter Takeaways
    5. Chapter 16 Key Challenges
      1. Obtaining Enough Quality Data
      2. Keeping Training and Test Data Separate
      3. Carefully Choosing Your Training Data
      4. Taking an Exploratory Approach
      5. Choosing the Right Tool for the Job
      6. Chapter Takeaways
  13. PART IV Putting Artificial Intelligence to Work
    1. Chapter 17 Harnessing the Power of Natural Language Processing
      1. Extracting Meaning from Text and Speech with NLU
      2. Delivering Sensible Responses with NLG
      3. Automating Customer Service
      4. Reviewing the Top NLP Tools and Resources
      5. Chapter Takeaways
    2. Chapter 18 Automating Customer Interactions
      1. Choosing Natural Language Technologies
      2. Review the Top Tools for Creating Chatbots and Virtual Agents
      3. Chapter Takeaways
    3. Chapter 19 Improving Data-Based Decision-Making
      1. Choosing Between Automated and Intuitive Decision-Making
      2. Gathering Data in Real Time from IoT Devices
      3. Reviewing Automated Decision-Making Tools
      4. Chapter Takeaways
    4. Chapter 20 Using Machine Learning to Predict Events and Outcomes
      1. Machine Learning Is Really about Labeling Data
      2. Looking at What Machine Learning Can Do
      3. Use Your Power for Good, Not Evil: Machine Learning Ethics
      4. Review the Top Machine Learning Tools
      5. Chapter Takeaways
    5. Chapter 21 Building Artificial Minds
      1. Separating Intelligence from Automation
      2. Adding Layers for Deep Learning
      3. Considering Applications for Artificial Neural Networks
      4. Reviewing the Top Deep Learning Tools
      5. Chapter Takeaways
  14. Index

Product information

  • Title: Artificial Intelligence for Business, 2nd Edition
  • Author(s): Doug Rose
  • Release date: December 2020
  • Publisher(s): Pearson FT Press
  • ISBN: 9780136556565