Artificial Intelligence for Business

by Rose, Doug

• ISBN13: 9780136556619
• ISBN10: 0136556612
• Edition: 2nd
• Format: Paperback
• Copyright: 2020-12-15
• Publisher: PEARSON

Summary

The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond

Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early “expert systems” to advanced deep learning networks.


First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems.

• Compare the ways a machine can learn, and explore current leading ML algorithms
• Start with the right problems, and avoid common AI/ML project mistakes
• Use neural networks to automate decision-making and identify unexpected patterns
• Help neural networks learn more quickly and effectively
• Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications

Author Biography

Doug Rose (Atlanta, GA Area) specializes in organizational coaching, training, and change management. He's worked over twenty years transforming organizations with technology and training. Rose has helped several Fortune® 500 clients optimize business processes and improve productivity and delivery. He also teaches at the University of Chicago, Syracuse University, Emory University, and the University of Virginia.

Table of Contents

PART I: THINKING MACHINES: AN OVERVIEW OF ARTIFICIAL INTELLIGENCE

1. What Is Artificial Intelligence?

2. The Rise of Machine Learning

3. Zeroing in on the Best Approach

4. Common AI Applications

5. Putting AI to Work on Big Data

6. Weighing Your Options

PART II: MACHINE LEARNING

7. What Is Machine Learning?

8. Different Ways a Machine Learns

9. Popular Machine Learning Algorithms

10. Applying Machine Learning Algorithms

11. Words of Advice

PART III: ARTIFICIAL NEURAL NETWORKS

12. What Are Artificial Neural Networks?

13. Artificial Neural Networks in Action

14. Letting Your Network Learn

15. Using Neural Networks to Classify or Cluster

16. Key Challenges