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