Introduction xxvii Chapter 1 What is Machine Learning? 1 History of Machine Learning 1 Alan Turing 1 Arthur Samuel 2 Tom M. Mitchell 2 Summary Definition 3 Algorithm Types for Machine Learning 3 Supervised Learning 3 Unsupervised Learning 4 The Human Touch 4 Uses for Machine Learning 4 Software 4 Stock Trading 5 Robotics 6 Medicine and Healthcare 6 Advertising 7 Retail and E-commerce 7 Gaming Analytics 9 The Internet of Things 10 Languages for Machine Learning 10 Python 10 R 11 Matlab 11 Scala 11 Ruby 11 Software Used in This Book 11 Checking the Java Version 12 Weka Toolkit 12 DeepLearning4J 13 Kafka 13 Spark and Hadoop 13 Text Editors and IDEs 13 Data Repositories 14 UC Irvine Machine Learning Repository 14 Kaggle 14 Summary 14 Chapter 2 Planning for Machine Learning 15 The Machine Learning Cycle 15 It All Starts with a Question 16 I Don''t Have Data! 16 Starting Local 17 Transfer Learning 17 Competitions 17 One Solution Fits All? 18 Defining the Process 18 Planning 18 Developing 19 Testing 19 Reporting 19 Refining 19 Production 20 Avoiding Bias 20 Building a Data Team 20 Mathematics and Statistics 20 Programming 21 Graphic Design 21 Domain Knowledge 21 Data Processing 22 Using Your Computer 22 A Cluster of Machines 22 Cloud-Based Services 22 Data Storage 23 Physical Discs 23 Cloud-Based Storage 23 Data Privacy 23 Cultural Norms 24 Generational Expectations 24 The Anonymity of User Data 25 Don''t Cross the "Creepy Line" 25 Data Quality and Cleaning 26 Presence Checks 26 Type Checks 27 Length Checks 27 Range Checks 28 Format Checks 28 The Britney Dilemma 28 What''s in a Country Name? 31 Dates and Times 33 Final Thoughts on Data Cleaning 33 Thinking About Input Data 34 Raw Text 34 Comma-Separated Variables 34 JSON 35 YAML 37 XML 37 Spreadsheets 38 Databases 39 Thinking About Output Data 39 Don''t Be Afraid to Experiment 40 Summary 40 Chapter 3 Data Acquisition Techniques 43 Scraping Data 43 Copy and Paste 44 Google Sheets 46 Using an API 47 Acquiring Weather Data 48 Migrating Data 50 Installing Embulk 51 Using the Quick Run 51 Installing Plugins 52 Migrating Files to Database 53 Bulk Converting CSV to JSON 55 Summary 56 Chapter 4 Statistics, Linear Regression, and Randomness 57 Working with a Basic Dataset 57 Loading and Converting the Dataset 58 Introducing Basic Statistics 59 Minimum and Maximum Values 60 Sum 61 Mean 62 Arithmetic Mean 62 Harmonic Mean 62 Geometric Mean 63 The Relationship Between the Three Averages 63 Mode 65 Median 66 Range 67 Interquartile Ranges 67 Variance 68 Standard Deviation 69 Using Simple Linear Regression 70 Using Your Spreadsheet 70 Writing a Program 73 Embracing Randomness 75 Finding Pi with Random Numbers 76 Using Monte Carlo Pi in Clojure 77 Summary 80 Chapter 5 Working with Decision Trees 81 The Basics of Decision Trees 81 Uses for Decision Trees 81 Advantages of Decision Trees 82 Limitations of Decision Trees 82 Different Algorithm Types 82 How Decision Trees Work 84 Decision Trees in Weka 88 The Requirement 88 Training Data 89 Using Weka to Create a Decision Tree 90 Creating Java Code from the Classification 94 Testing the Classifier Code 99 Thinking About Future Iterations 101 Summary 101 Chapter 6 Clustering 103 What is Clustering? 103 Where is Clustering Used? 104 The Internet 104 Business and Retail 104 Law Enforcement 105 Computing 105 Clustering Models 105 How the K-Means Works 106 Calculating the Number of Clusters in a Dataset 108 K-Means Clustering with Weka 110 Preparing the Data 110 The Workbench Method 111 The Command-Line Method 116 Converting CSV File to ARFF 116 The Coded Method 120 Summary 128 Chapter 7 Association Rules Learning 129 Where is Association Rules Learning Used? 129 Web Usage Mining 130 Beer and Diapers 130 How Association Rules Learning Works 131 Support 133 Confidence 133 Lift 134 Conviction 134 Defining the Process 134 Algorithms 135 Apriori 135 FP-Growth 136 Mining the Baskets--A Walk-Through 136 The Raw Basket Data 136 Using the Weka Application 137 Inspecting the Results 141 Summary 142 Chapter 8 Support Vector Machines 143 What is a Support Vector Machine? 143 Where are Support Vector Machines Used? 144 The Basic Classification Principles 144 Binary and Multiclass Classification 144 Linear Classifiers 146 Confidence 147 Maximizing and Minimizing to Find the Line 147 How Support Vector Machines Approach Classification 148 Using Linear Classification 148 Using Non-Linear Classification 150 Using Support Vector Machines in Weka 151 Installing LibSVM 151 A Classification Walk-Through 152 Implementing LibSVM with Java 158 Summary 164 Chapter 9 Artificial Neural Networks 165 What is a Neural Network? 165 Artificial Neural Network Uses 166 High-Frequency Trading 166 Credit Applications 167 Data Center Management 167 Robotics 167 Medical Monitoring 168 Trusting the Black Box 168 Breaking Down the Artificial Neural Network 169 Perceptrons 169 Activation Functions 170 Multilayer Perceptrons 171 Back Propagation 173 Data Preparation for Artificial Neural Networks 174 Artificial Neural Networks with Weka 175 Generating a Dataset 175 Loading the Data into Weka 177 Configuring the Multilayer Perceptron 178 Training the Network 180 Altering the Network 182 Increasing the Test Data Size 183 Implementing a Neural Network in Java 183 Creating the Project 183 Writing the Code 185 Converting from CSV to Arff 188 Running the Neural Network 188 Developing Neural Networks with DeepLearning4J 189 Modifying the Data 189 Viewing Maven Dependencies 190 Handling the Training Data 191 Normalizing Data 191 Building the Model 192 Evaluating the Model 193 Saving the Model 193 Building and Executing the Program 194 Summary 195 Chapter 10 Machine Learning with Text Documents 197 Preparing Text for Analysis 198 Apache Tika 198 Cleaning the Text Data 203 Stopwords 205 Stemming 206 N-grams 206 TF/IDF 207 Loading the Documents 207 Calculating the Term Frequency 208 Calculating the Inverse Document Frequency 208 Computing the TF/IDF Score 209 Reviewing the Final Code Listing 209 Word2Vec 211 Loading the Raw Text Data 212 Tokenizing the Strings 212 Creating the Model 212 Evaluating the Model 213 Reviewing the Final Code 214 Basic Sentiment Analysis 216 Loading Positive and Negative Words 216 Loading Sentences 217 Calculating the Sentiment Score 217 Reviewing the Final Code 218 Performing a Test Run 220 Further Development 220 Summary 221 Chapter 11 Machine Learning with Images 223 What is an Image? 223 Introducing Color Depth 224 Images in Machine Learning 225 Basic Classifi cation with Neural Networks 226 Basic Settings 226 Loading the MNIST Images 226 Model Configuration 227 Model Training 228 Model Evaluation 228 Convolutional Neural Networks 228 How CNNs Work 228 CNN Demonstration 231 Downloading the Image Data 231 Basic Setup 232 Handling the Training and Test Data 233 Image Preparation 233 CNN Model Configuration 234 Model Training 236 Model Evaluation 236 Saving the Model 237 Transfer Learning 237 Summary 238 Chapter 12 Machine Learning Streaming with Kafka 239 What You Will Learn in This Chapter 239 From Machine Learning to Machine Learning Engineer 240 From Batch Processing to Streaming Data Processing 241 What is Kafka? 241 How Does It Work? 241 Fault Tolerance 243 Further Reading 243 Installing Kafka 243 Kafka as a Single-Node Cluster 244 Kafka as a Multinode Cluster 245 Topics Management 247 Creating Topics 248 Finding Out Information About Existing Topics 248 Deleting Topics 249 Sending Messages from the Command Line 249 Receiving Messages from the Command Line 250 Kafka Tool UI 250 Writing Your Own Producers and Consumers 251 Producers in Java 251 Consumers in Java 255 Building and Running the Applications 258 The Streaming API 260 Building a Streaming Machine Learning System 262 Pla.