Description
Book SynopsisDoug 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 o
Table of ContentsForeword 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
