Machine generated contents note: ch. 1 What Is Machine Learning? -- History of Machine Learning -- Alan Turing -- Arthur Samuel -- Tom M. Mitchell -- Summary Definition -- Algorithm Types for Machine Learning -- Supervised Learning -- Unsupervised Learning -- The Human Touch -- Uses for Machine Learning -- Software -- Spam Detection -- Voice Recognition -- Stock Trading -- Robotics -- Medicine and Healthcare -- Advertising -- Retail and E-commerce -- Gaming Analytics -- The Internet of Things -- Languages for Machine Learning -- Python -- R -- Matlab -- Scala -- Ruby -- Software Used in This Book -- Checking the Java Version -- Weka Toolkit -- DeepLearning4J -- Kafka -- Spark and Hadoop -- Text Editors and IDEs -- Data Repositories -- UC Irvine Machine Learning Repository -- Kaggle -- Summary -- ch. 2 Planning for Machine Learning -- The Machine Learning Cycle -- It All Starts with a Question -- I Don't Have Data! -- Starting Local -- Transfer Learning -- Competitions -- One Solution Fits All? -- Defining the Process -- Planning -- Developing -- Testing -- Reporting -- Refining -- Production -- Avoiding Bias -- Building a Data Team -- Mathematics and Statistics -- Programming -- Graphic Design -- Domain Knowledge -- Data Processing -- Using Your Computer -- A Cluster of Machines -- Cloud-Based Services -- Data Storage -- Physical Discs -- Cloud-Based Storage -- Data Privacy -- Cultural Norms -- Generational Expectations -- The Anonymity of User Data -- Don't Cross the "Creepy Line" -- Data Quality and Cleaning -- Presence Checks -- Type Checks -- Length Checks -- Range Checks -- Format Checks -- The Britney Dilemma -- What's in a Country Name? -- Dates and Times -- Final Thoughts on Data Cleaning -- Thinking About Input Data -- Raw Text -- Comma-Separated Variables -- JSON -- YAML -- XML -- Spreadsheets -- Databases -- Images -- Thinking About Output Data -- Don't Be Afraid to Experiment -- Summary -- ch. 3 Data Acquisition Techniques -- Scraping Data -- Copy and Paste -- Google Sheets -- Using an API -- Acquiring Weather Data -- Using the Command Line -- Using Java -- Using Clojure -- Migrating Data -- Installing Embulk -- Using the Quick Run -- Installing Plugins -- Migrating Files to Database -- Bulk Converting CSV to JSON -- Summary -- ch. 4 Statistics, Linear Regression, and Randomness -- Working with a Basic Dataset -- Loading and Converting the Dataset -- Loading Data with Clojure -- Loading Data with Java -- Introducing Basic Statistics -- Minimum and Maximum Values -- Mathematical Notation -- Clojure -- Java -- Sum -- Mathematical Notation -- Clojure -- Java -- Mean -- Arithmetic Mean -- Harmonic Mean -- Geometric Mean -- The Relationship Between the Three Averages -- Clojure -- Java -- Mode -- Clojure -- Java -- Median -- Clojure -- Java -- Range -- Clojure -- Java -- Interquartile Ranges -- Clojure -- Java -- Variance -- Clojure -- Java -- Standard Deviation -- Clojure -- Java -- Using Simple Linear Regression -- Using Your Spreadsheet -- Using Excel -- Loading the CSV Data -- Creating a Scatter Plot -- Showing the Trendline -- Showing the Equation and R2 Value -- Making a Prediction -- Writing a Program -- Embracing Randomness -- Finding Pi with Random Numbers -- Using Monte Carlo Pi in Clojure -- Is the Dart Within the Circle? -- Now Throw Lots of Darts! -- Summary -- ch. 5 Working with Decision Trees -- The Basics of Decision Trees -- Uses for Decision Trees -- Advantages of Decision Trees -- Limitations of Decision Trees -- Different Algorithm Types -- ID3 -- C4.5 -- CHAID -- MARS -- How Decision Trees Work -- Building a Decision Tree -- Manually Walking Through an Example -- Calculating Entropy -- Information Gain -- Rinse and Repeat -- Decision Trees in Weka -- The Requirement -- Training Data -- Relation -- Attributes -- Data -- Using Weka to Create a Decision Tree -- Creating Java Code from the Classification -- Testing the Classifier Code -- Thinking About Future Iterations -- Summary -- ch. 6 Clustering -- What Is Clustering? -- Where Is Clustering Used? -- The Internet -- Business and Retail -- Law Enforcement -- Computing -- Clustering Models -- How the K-Means Works -- Initialization -- Assignments -- Update -- Calculating the Number of Clusters in a Dataset -- The Rule of Thumb Method -- The Elbow Method -- The Cross-Validation Method -- The Silhouette Method -- K-Means Clustering with Weka -- Preparing the Data -- The Workbench Method -- Loading Data -- Clustering the Data -- Visualizing the Data -- The Command-Line Method -- Converting CSV File to ARFF -- The First Run -- Refining the Optimum Clusters -- Name That Cluster -- The Coded Method -- Create the Project -- The Cluster Code -- Printing the Cluster Information -- Making Predictions -- The Final Code Listing -- Running the Program -- Further Development -- Summary -- ch. 7 Association Rules Learning -- Where Is Association Rules Learning Used? -- Web Usage Mining -- Beer and Diapers -- How Association Rules Learning Works -- Support -- Confidence -- Lift -- Conviction -- Defining the Process -- Algorithms -- Apriori -- FP-Growth -- Mining the Baskets -- A Walk-Through -- The Raw Basket Data -- Using the Weka Application -- Inspecting the Results -- Summary -- ch. 8 Support Vector Machines -- What Is a Support Vector Machine? -- Where Are Support Vector Machines Used? -- The Basic Classification Principles -- Binary and Multiclass Classification -- Linear Classifiers -- Confidence -- Maximizing and Minimizing to Find the Line -- How Support Vector Machines Approach Classification -- Using Linear Classification -- Using Non-Linear Classification -- Using Support Vector Machines in Weka -- Installing LibSVM -- Weka LibSVM Installation -- A Classification Walk-Through -- Setting the Options -- Running the Classifier -- Dealing with Errors from LibSVM -- Saving the Model -- Implementing LibSVM with Java -- Converting csv Data to .arff Format -- Setting Up the Project and Libraries -- Training and Predicting with the Existing Data -- Summary -- ch. 9 Artificial Neural Networks -- What Is a Neural Network? -- Artificial Neural Network Uses -- High-Frequency Trading -- Credit Applications -- Data Center Management -- Robotics -- Medical Monitoring -- Trusting the Black Box -- Breaking Down the Artificial Neural Network -- Perceptrons -- Activation Functions -- Multilayer Perceptrons -- Back Propagation -- Data Preparation for Artificial Neural Networks -- Artificial Neural Networks with Weka -- Generating a Dataset -- Loading the Data into Weka -- Configuring the Multilayer Perceptron -- Learning Rate -- Hidden Layers -- Training Time -- Training the Network -- Altering the Network -- Which Bit Is Which? -- Adding Nodes -- Connecting Nodes -- Removing Connections -- Removing Nodes -- Increasing the Test Data Size -- Implementing a Neural Network in Java -- Creating the Project -- Writing the Code -- Converting from CSV to Arff -- Running the Neural Network -- Developing Neural Networks with DeepLearning4J -- Modifying the Data -- Viewing Maven Dependencies -- Handling the Training Data -- Normalizing Data -- Building the Model -- Evaluating the Model -- Saving the Model -- Building and Executing the Program -- Summary -- ch. 10 Machine Learning with Text Documents -- Preparing Text for Analysis -- Apache Tika -- Downloading Tika -- Tika from the Command Line -- Tika Within an Application -- Cleaning the Text Data -- Convert Words to Lowercase -- Remove Punctuation -- Stopwords -- Stemming -- N-grams -- TF/IDF -- Loading the Documents -- Calculating the Term Frequency -- Calculating the Inverse Document Frequency -- Computing the TF/IDF Score -- Reviewing the Final Code Listing -- Word2Vec -- Loading the Raw Text Data -- Tokenizing the Strings -- Creating the Model -- Evaluating the Model -- Reviewing the Final Code -- Basic Sentiment Analysis -- Loading Positive and Negative Words -- Loading Sentences -- Calculating the Sentiment Score -- Reviewing the Final Code -- Performing a Test Run -- Further Development -- Summary -- ch.

11 Machine Learning with Images -- What Is an Image? -- Introducing Color Depth -- Images in Machine Learning -- Basic Classification with Neural Networks -- Basic Settings -- Loading the MNIST Images -- Model Configuration -- Model Training -- Model Evaluation -- Convolutional Neural Networks -- How CNNs Work -- Feature Extraction -- Activation Functions -- Pooling -- Classification -- CNN Demonstration -- Downloading the Image Data -- Basic Setup -- Handling the Training and Test Data -- Image Preparation -- CNN Model Configuration -- Model Training -- Model Evaluation -- Saving the Model -- Transfer Learning -- Summary -- ch. 12 Machine Learning Streaming with Kafka -- What You Will Learn in This Chapter -- From Machine Learning to Machine Learning Engineer -- From Batch Processing to Streaming Data Processing -- What Is Kafka? -- How Does It Work? -- Fault Tolerance -- Further Reading -- Installing Kafka -- Kafka as a Single-Node Cluster -- Starting Zookeeper -- Starting Kafka -- Kafka as a Multinode Cluster -- Starting the Multibroker Cluster -- Topics Management -- Creating Topics -- Finding Out Information About Existing Topics -- Deleting Topics -- Sending Messages from the Command Line -- Receiving Messages from the Command Line -- Kafka Tool UI -- Writing Your Own Producers and Consumers -- Producers in Java -- Properties -- The Producer -- Messages -- The Final Code -- Message Acknowledgments -- Consumers in Java -- Properties -- Windows -- Linux -- Your First Run -- Installing R-Studio -- The R Basics -- Variables and Vectors -- Matrices -- Lists -- Data Frames -- Installing Packages -- Loading in Data -- CSV Files -- MySQL Queries -- Creating Random Sample Data -- Plotting Data -- Bar Charts -- Pie Charts -- Dot Plots -- Line Charts -- Simple Statistics -- Simple Linear Regression -- Creating the Data -- The Initial Graph -- Regression with the Linear Model -- Making a Prediction

Note continued: Basic Sentiment Analysis -- Using Functions to Load in Word Lists -- Writing a Function to Score Sentiment -- Testing the Function -- Apriori Association Rules -- Installing the arules Package -- Gathering the Training Data -- Importing the Transaction Data -- Running the Apriori Algorithm -- Inspecting the Results -- Accessing R from Java -- Installing the rJava Package -- Creating Your First Java Code in R -- Calling R from Java Programs -- Setting Up an Eclipse Project -- Creating the Java/R Class -- Running the Example -- Extending Your R Implementations -- Connecting to Social Media with R -- Summary -- Appendix A Kafka Quick Start -- Installing Kafka -- Starting Zookeeper -- Starting Kafka -- Creating Topics -- Listing Topics -- Describing a Topic -- Deleting Topics -- Running a Console Producer -- Running a Console Consumer -- Appendix B The Twitter API Developer Application Configuration -- Appendix C Useful Unix Commands -- Using Sample Data -- Showing the Contents: cat, more, and less -- Example Command -- Expected Output -- Filtering Content: grep -- Example Command for Finding Text -- Example Output -- Sorting Data: sort -- Example Command for Basic Sorting -- Example Output -- Finding Unique Occurrences: uniq -- Showing the Top of a File: head -- Counting Words: wc -- Locating Anything: find -- Combining Commands and Redirecting Output -- Picking a Text Editor -- Colon Frenzy: Vi and Vim -- Nano -- Emacs -- Appendix D Further Reading -- Machine Learning -- Statistics -- Big Data and Data Science -- Visualization -- Making Decisions -- Datasets -- Blogs -- Useful Websites -- The Tools of the Trade.