Artificial Intelligence for Business

Doug Rose has been transforming organizations through technology, training, and process optimization for more than 25 years. He is the author of the Project Management Institute (PMI) first major publication on the agile framework, Leading Agile Teams. He is also the author of Data Science: Create Teams That Ask the Right Questions and Deliver Real Value and Enterprise Agility for Dummies. Doug has a master degree (MS) in information management, a law degree (JD) from Syracuse University, and a BA from the University of Wisconsin-Madison. He is also a Scaled Agile Framework Program Consultant (SPC), Certified Technical Trainer (CTT+), Certified Scrum Professional (CSP-SM), Certified Scrum Master (CSM), PMI Agile Certified Professional (PMI-ACP), Project Management Professional (PMP), and Certified Developer for Apache Hadoop (CCDH). You can attend his lively and engaging business and project management courses at the University of Chicago or online through LinkedIn Learning. Doug works through Doug Enterprises, an organization with an office in whatever city he lives. Currently he lives in Atlanta, Georgia, where he spends his free time either riding a stationary recumbent bike or explaining the Marvel Universe to his son.

Foreword     xv

Preface     xix

PART I:  Thinking Machines: An Overview of Artificial Intelligence     1

Chapter 1:  What Is Artificial Intelligence?     3

What Is Intelligence?     4

Testing Machine Intelligence     6

The General Problem Solver     8

Strong and Weak Artificial Intelligence     11

Artificial Intelligence Planning     14

Learning over Memorizing     15

Chapter Takeaways     18

Chapter 2:  The Rise of Machine Learning     19

Practical Applications of Machine Learning     22

Artificial Neural Networks     24

The Fall and Rise of the Perceptron     27

Big Data Arrives     30

Chapter Takeaways     33

Chapter 3:  Zeroing in on the Best Approach     35

Expert System Versus Machine Learning     35

Supervised Versus Unsupervised Learning     37

Backpropagation of Errors     38

Regression Analysis     41

Chapter Takeaways     43

Chapter 4:  Common AI Applications     45

Intelligent Robots     45

Natural Language Processing     48

The Internet of Things     50

Chapter Takeaways     51

Chapter 5:  Putting AI to Work on Big Data     53

Understanding the Concept of Big Data     54

Teaming Up with a Data Scientist     54

Machine Learning and Data Mining: What's the Difference?     55

Making the Leap from Data Mining to Machine Learning     56

Taking the Right Approach     57

Chapter Takeaways     59

Chapter 6:  Weighing Your Options     61

Chapter Takeaways     64

PART II:  Machine Learning     65

Chapter 7:  What Is Machine Learning?     67

How a Machine Learns     71

Working with Data     74

Applying Machine Learning     77

Different Types of Learning     79

Chapter Takeaways     81

Chapter 8:  Different Ways a Machine Learns     83

Supervised Machine Learning     83

Unsupervised Machine Learning     86

Semi-Supervised Machine Learning     89

Reinforcement Learning     91

Chapter Takeaways     93

Chapter 9:  Popular Machine Learning Algorithms     95

Decision Trees     99

k-Nearest Neighbor     101

k-Means Clustering     104

Regression Analysis     108

Naive Bayes     110

Chapter Takeaways     113

Chapter 10:  Applying Machine Learning Algorithms     115

Fitting the Model to Your Data     119

Choosing Algorithms     120

Ensemble Modeling     121

Deciding on a Machine Learning Approach     123

Chapter Takeaways     124

Chapter 11:  Words of Advice     125

Start Asking Questions     125

Don't Mix Training Data with Test Data     127

Don't Overstate a Model's Accuracy     127

Know Your Algorithms     128

Chapter Takeaways     128

PART III:  Artificial Neural Networks     129

Chapter 12:  What Are Artificial Neural Networks?     131

Why the Brain Analogy?     133

Just Another Amazing Algorithm     133

Getting to Know the Perceptron     135

Squeezing Down a Sigmoid Neuron     138

Adding Bias     141

Chapter Takeaways     142

Chapter 13:  Artificial Neural Networks in Action     143

Feeding Data into the Network     143

What Goes on in the Hidden Layers     145

Understanding Activation Functions     149

Adding Weights     151

Adding Bias     152

Chapter Takeaways     153

Chapter 14:  Letting Your Network Learn     155

Starting with Random Weights and Biases     156

Making Your Network Pay for Its Mistakes: The Cost Function     157

Combining the Cost Function with Gradient Descent     158

Using Backpropagation to Correct for Errors     160

Tuning Your Network     163

Employing the Chain Rule     164

Batching the Data Set with Stochastic Gradient Descent     166

Chapter Takeaways     167

Chapter 15:  Using Neural Networks to Classify or Cluster     169

Solving Classification Problems     170

Solving Clustering Problems     172

Chapter Takeaways     174

Chapter 16:  Key Challenges     175

Obtaining Enough Quality Data     175

Keeping Training and Test Data Separate     176

Carefully Choosing Your Training Data     177

Taking an Exploratory Approach     177

Choosing the Right Tool for the Job     178

Chapter Takeaways     178

PART IV:  Putting Artificial Intelligence to Work     179

Chapter 17:  Harnessing the Power of Natural Language Processing     181

Extracting Meaning from Text and Speech with NLU     183

Delivering Sensible Responses with NLG     184

Automating Customer Service     186

Reviewing the Top NLP Tools and Resources     187

NLU Tools     189

NLG Tools     190

Chapter Takeaways     191

Chapter 18:  Automating Customer Interactions     193

Choosing Natural Language Technologies     195

Review the Top Tools for Creating Chatbots and Virtual Agents     196

Chapter Takeaways     198

Chapter 19:  Improving Data-Based Decision-Making     199

Choosing Between Automated and Intuitive Decision-Making     201

Gathering Data in Real Time from IoT Devices     202

Reviewing Automated Decision-Making Tools     204

Chapter Takeaways     205

Chapter 20:  Using Machine Learning to Predict Events and Outcomes     207

Machine Learning Is Really about Labeling Data     208

Looking at What Machine Learning Can Do     210

Predict What Customers Will Buy     210

Answer Questions Before They're Asked     210

Make Better Decisions Faster     212

Replicate Expertise in Your Business     213

Use Your Power for Good, Not Evil: Machine Learning Ethics     214

Review the Top Machine Learning Tools     216

Chapter Takeaways     218

Chapter 21:  Building Artificial Minds     219

Separating Intelligence from Automation     221

Adding Layers for Deep Learning     222

Considering Applications for Artificial Neural Networks     223

Classifying Your Best Customers     224

Recommending Store Layouts     225

Analyzing and Tracking Biometrics     226

Reviewing the Top Deep Learning Tools     228

Chapter Takeaways     229

Index     231