{"product_id":"machine-learning-9781119642145","title":"Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 What is Machine Learning? 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHistory of Machine Learning 1\u003c\/p\u003e \u003cp\u003eAlan Turing 1\u003c\/p\u003e \u003cp\u003eArthur Samuel 2\u003c\/p\u003e \u003cp\u003eTom M. Mitchell 2\u003c\/p\u003e \u003cp\u003eSummary Definition 3\u003c\/p\u003e \u003cp\u003eAlgorithm Types for Machine Learning 3\u003c\/p\u003e \u003cp\u003eSupervised Learning 3\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 4\u003c\/p\u003e \u003cp\u003eThe Human Touch 4\u003c\/p\u003e \u003cp\u003eUses for Machine Learning 4\u003c\/p\u003e \u003cp\u003eSoftware 4\u003c\/p\u003e \u003cp\u003eStock Trading 5\u003c\/p\u003e \u003cp\u003eRobotics 6\u003c\/p\u003e \u003cp\u003eMedicine and Healthcare 6\u003c\/p\u003e \u003cp\u003eAdvertising 7\u003c\/p\u003e \u003cp\u003eRetail and E-commerce 7\u003c\/p\u003e \u003cp\u003eGaming Analytics 9\u003c\/p\u003e \u003cp\u003eThe Internet of Things 10\u003c\/p\u003e \u003cp\u003eLanguages for Machine Learning 10\u003c\/p\u003e \u003cp\u003ePython 10\u003c\/p\u003e \u003cp\u003eR 11\u003c\/p\u003e \u003cp\u003eMatlab 11\u003c\/p\u003e \u003cp\u003eScala 11\u003c\/p\u003e \u003cp\u003eRuby 11\u003c\/p\u003e \u003cp\u003eSoftware Used in This Book 11\u003c\/p\u003e \u003cp\u003eChecking the Java Version 12\u003c\/p\u003e \u003cp\u003eWeka Toolkit 12\u003c\/p\u003e \u003cp\u003eDeepLearning4J 13\u003c\/p\u003e \u003cp\u003eKafka 13\u003c\/p\u003e \u003cp\u003eSpark and Hadoop 13\u003c\/p\u003e \u003cp\u003eText Editors and IDEs 13\u003c\/p\u003e \u003cp\u003eData Repositories 14\u003c\/p\u003e \u003cp\u003eUC Irvine Machine Learning Repository 14\u003c\/p\u003e \u003cp\u003eKaggle 14\u003c\/p\u003e \u003cp\u003eSummary 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Planning for Machine Learning 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Machine Learning Cycle 15\u003c\/p\u003e \u003cp\u003eIt All Starts with a Question 16\u003c\/p\u003e \u003cp\u003eI Don’t Have Data! 16\u003c\/p\u003e \u003cp\u003eStarting Local 17\u003c\/p\u003e \u003cp\u003eTransfer Learning 17\u003c\/p\u003e \u003cp\u003eCompetitions 17\u003c\/p\u003e \u003cp\u003eOne Solution Fits All? 18\u003c\/p\u003e \u003cp\u003eDefining the Process 18\u003c\/p\u003e \u003cp\u003ePlanning 18\u003c\/p\u003e \u003cp\u003eDeveloping 19\u003c\/p\u003e \u003cp\u003eTesting 19\u003c\/p\u003e \u003cp\u003eReporting 19\u003c\/p\u003e \u003cp\u003eRefining 19\u003c\/p\u003e \u003cp\u003eProduction 20\u003c\/p\u003e \u003cp\u003eAvoiding Bias 20\u003c\/p\u003e \u003cp\u003eBuilding a Data Team 20\u003c\/p\u003e \u003cp\u003eMathematics and Statistics 20\u003c\/p\u003e \u003cp\u003eProgramming 21\u003c\/p\u003e \u003cp\u003eGraphic Design 21\u003c\/p\u003e \u003cp\u003eDomain Knowledge 21\u003c\/p\u003e \u003cp\u003eData Processing 22\u003c\/p\u003e \u003cp\u003eUsing Your Computer 22\u003c\/p\u003e \u003cp\u003eA Cluster of Machines 22\u003c\/p\u003e \u003cp\u003eCloud-Based Services 22\u003c\/p\u003e \u003cp\u003eData Storage 23\u003c\/p\u003e \u003cp\u003ePhysical Discs 23\u003c\/p\u003e \u003cp\u003eCloud-Based Storage 23\u003c\/p\u003e \u003cp\u003eData Privacy 23\u003c\/p\u003e \u003cp\u003eCultural Norms 24\u003c\/p\u003e \u003cp\u003eGenerational Expectations 24\u003c\/p\u003e \u003cp\u003eThe Anonymity of User Data 25\u003c\/p\u003e \u003cp\u003eDon’t Cross the “Creepy Line” 25\u003c\/p\u003e \u003cp\u003eData Quality and Cleaning 26\u003c\/p\u003e \u003cp\u003ePresence Checks 26\u003c\/p\u003e \u003cp\u003eType Checks 27\u003c\/p\u003e \u003cp\u003eLength Checks 27\u003c\/p\u003e \u003cp\u003eRange Checks 28\u003c\/p\u003e \u003cp\u003eFormat Checks 28\u003c\/p\u003e \u003cp\u003eThe Britney Dilemma 28\u003c\/p\u003e \u003cp\u003eWhat’s in a Country Name? 31\u003c\/p\u003e \u003cp\u003eDates and Times 33\u003c\/p\u003e \u003cp\u003eFinal Thoughts on Data Cleaning 33\u003c\/p\u003e \u003cp\u003eThinking About Input Data 34\u003c\/p\u003e \u003cp\u003eRaw Text 34\u003c\/p\u003e \u003cp\u003eComma-Separated Variables 34\u003c\/p\u003e \u003cp\u003eJSON 35\u003c\/p\u003e \u003cp\u003eYAML 37\u003c\/p\u003e \u003cp\u003eXML 37\u003c\/p\u003e \u003cp\u003eSpreadsheets 38\u003c\/p\u003e \u003cp\u003eDatabases 39\u003c\/p\u003e \u003cp\u003eThinking About Output Data 39\u003c\/p\u003e \u003cp\u003eDon’t Be Afraid to Experiment 40\u003c\/p\u003e \u003cp\u003eSummary 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Data Acquisition Techniques 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eScraping Data 43\u003c\/p\u003e \u003cp\u003eCopy and Paste 44\u003c\/p\u003e \u003cp\u003eGoogle Sheets 46\u003c\/p\u003e \u003cp\u003eUsing an API 47\u003c\/p\u003e \u003cp\u003eAcquiring Weather Data 48\u003c\/p\u003e \u003cp\u003eMigrating Data 50\u003c\/p\u003e \u003cp\u003eInstalling Embulk 51\u003c\/p\u003e \u003cp\u003eUsing the Quick Run 51\u003c\/p\u003e \u003cp\u003eInstalling Plugins 52\u003c\/p\u003e \u003cp\u003eMigrating Files to Database 53\u003c\/p\u003e \u003cp\u003eBulk Converting CSV to JSON 55\u003c\/p\u003e \u003cp\u003eSummary 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Statistics, Linear Regression, and Randomness 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWorking with a Basic Dataset 57\u003c\/p\u003e \u003cp\u003eLoading and Converting the Dataset 58\u003c\/p\u003e \u003cp\u003eIntroducing Basic Statistics 59\u003c\/p\u003e \u003cp\u003eMinimum and Maximum Values 60\u003c\/p\u003e \u003cp\u003eSum 61\u003c\/p\u003e \u003cp\u003eMean 62\u003c\/p\u003e \u003cp\u003eArithmetic Mean 62\u003c\/p\u003e \u003cp\u003eHarmonic Mean 62\u003c\/p\u003e \u003cp\u003eGeometric Mean 63\u003c\/p\u003e \u003cp\u003eThe Relationship Between the Three Averages 63\u003c\/p\u003e \u003cp\u003eMode 65\u003c\/p\u003e \u003cp\u003eMedian 66\u003c\/p\u003e \u003cp\u003eRange 67\u003c\/p\u003e \u003cp\u003eInterquartile Ranges 67\u003c\/p\u003e \u003cp\u003eVariance 68\u003c\/p\u003e \u003cp\u003eStandard Deviation 69\u003c\/p\u003e \u003cp\u003eUsing Simple Linear Regression 70\u003c\/p\u003e \u003cp\u003eUsing Your Spreadsheet 70\u003c\/p\u003e \u003cp\u003eWriting a Program 73\u003c\/p\u003e \u003cp\u003eEmbracing Randomness 75\u003c\/p\u003e \u003cp\u003eFinding Pi with Random Numbers 76\u003c\/p\u003e \u003cp\u003eUsing Monte Carlo Pi in Clojure 77\u003c\/p\u003e \u003cp\u003eSummary 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Working with Decision Trees 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Basics of Decision Trees 81\u003c\/p\u003e \u003cp\u003eUses for Decision Trees 81\u003c\/p\u003e \u003cp\u003eAdvantages of Decision Trees 82\u003c\/p\u003e \u003cp\u003eLimitations of Decision Trees 82\u003c\/p\u003e \u003cp\u003eDifferent Algorithm Types 82\u003c\/p\u003e \u003cp\u003eHow Decision Trees Work 84\u003c\/p\u003e \u003cp\u003eDecision Trees in Weka 88\u003c\/p\u003e \u003cp\u003eThe Requirement 88\u003c\/p\u003e \u003cp\u003eTraining Data 89\u003c\/p\u003e \u003cp\u003eUsing Weka to Create a Decision Tree 90\u003c\/p\u003e \u003cp\u003eCreating Java Code from the Classification 94\u003c\/p\u003e \u003cp\u003eTesting the Classifier Code 99\u003c\/p\u003e \u003cp\u003eThinking About Future Iterations 101\u003c\/p\u003e \u003cp\u003eSummary 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Clustering 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is Clustering? 103\u003c\/p\u003e \u003cp\u003eWhere is Clustering Used? 104\u003c\/p\u003e \u003cp\u003eThe Internet 104\u003c\/p\u003e \u003cp\u003eBusiness and Retail 104\u003c\/p\u003e \u003cp\u003eLaw Enforcement 105\u003c\/p\u003e \u003cp\u003eComputing 105\u003c\/p\u003e \u003cp\u003eClustering Models 105\u003c\/p\u003e \u003cp\u003eHow the K-Means Works 106\u003c\/p\u003e \u003cp\u003eCalculating the Number of Clusters in a Dataset 108\u003c\/p\u003e \u003cp\u003eK-Means Clustering with Weka 110\u003c\/p\u003e \u003cp\u003ePreparing the Data 110\u003c\/p\u003e \u003cp\u003eThe Workbench Method 111\u003c\/p\u003e \u003cp\u003eThe Command-Line Method 116\u003c\/p\u003e \u003cp\u003eConverting CSV File to ARFF 116\u003c\/p\u003e \u003cp\u003eThe Coded Method 120\u003c\/p\u003e \u003cp\u003eSummary 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Association Rules Learning 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhere is Association Rules Learning Used? 129\u003c\/p\u003e \u003cp\u003eWeb Usage Mining 130\u003c\/p\u003e \u003cp\u003eBeer and Diapers 130\u003c\/p\u003e \u003cp\u003eHow Association Rules Learning Works 131\u003c\/p\u003e \u003cp\u003eSupport 133\u003c\/p\u003e \u003cp\u003eConfidence 133\u003c\/p\u003e \u003cp\u003eLift 134\u003c\/p\u003e \u003cp\u003eConviction 134\u003c\/p\u003e \u003cp\u003eDefining the Process 134\u003c\/p\u003e \u003cp\u003eAlgorithms 135\u003c\/p\u003e \u003cp\u003eApriori 135\u003c\/p\u003e \u003cp\u003eFP-Growth 136\u003c\/p\u003e \u003cp\u003eMining the Baskets—A Walk-Through 136\u003c\/p\u003e \u003cp\u003eThe Raw Basket Data 136\u003c\/p\u003e \u003cp\u003eUsing the Weka Application 137\u003c\/p\u003e \u003cp\u003eInspecting the Results 141\u003c\/p\u003e \u003cp\u003eSummary 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Support Vector Machines 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is a Support Vector Machine? 143\u003c\/p\u003e \u003cp\u003eWhere are Support Vector Machines Used? 144\u003c\/p\u003e \u003cp\u003eThe Basic Classification Principles 144\u003c\/p\u003e \u003cp\u003eBinary and Multiclass Classification 144\u003c\/p\u003e \u003cp\u003eLinear Classifiers 146\u003c\/p\u003e \u003cp\u003eConfidence 147\u003c\/p\u003e \u003cp\u003eMaximizing and Minimizing to Find the Line 147\u003c\/p\u003e \u003cp\u003eHow Support Vector Machines Approach Classification 148\u003c\/p\u003e \u003cp\u003eUsing Linear Classification 148\u003c\/p\u003e \u003cp\u003eUsing Non-Linear Classification 150\u003c\/p\u003e \u003cp\u003eUsing Support Vector Machines in Weka 151\u003c\/p\u003e \u003cp\u003eInstalling LibSVM 151\u003c\/p\u003e \u003cp\u003eA Classification Walk-Through 152\u003c\/p\u003e \u003cp\u003eImplementing LibSVM with Java 158\u003c\/p\u003e \u003cp\u003eSummary 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Artificial Neural Networks 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is a Neural Network? 165\u003c\/p\u003e \u003cp\u003eArtificial Neural Network Uses 166\u003c\/p\u003e \u003cp\u003eHigh-Frequency Trading 166\u003c\/p\u003e \u003cp\u003eCredit Applications 167\u003c\/p\u003e \u003cp\u003eData Center Management 167\u003c\/p\u003e \u003cp\u003eRobotics 167\u003c\/p\u003e \u003cp\u003eMedical Monitoring 168\u003c\/p\u003e \u003cp\u003eTrusting the Black Box 168\u003c\/p\u003e \u003cp\u003eBreaking Down the Artificial Neural Network 169\u003c\/p\u003e \u003cp\u003ePerceptrons 169\u003c\/p\u003e \u003cp\u003eActivation Functions 170\u003c\/p\u003e \u003cp\u003eMultilayer Perceptrons 171\u003c\/p\u003e \u003cp\u003eBack Propagation 173\u003c\/p\u003e \u003cp\u003eData Preparation for Artificial Neural Networks 174\u003c\/p\u003e \u003cp\u003eArtificial Neural Networks with Weka 175\u003c\/p\u003e \u003cp\u003eGenerating a Dataset 175\u003c\/p\u003e \u003cp\u003eLoading the Data into Weka 177\u003c\/p\u003e \u003cp\u003eConfiguring the Multilayer Perceptron 178\u003c\/p\u003e \u003cp\u003eTraining the Network 180\u003c\/p\u003e \u003cp\u003eAltering the Network 182\u003c\/p\u003e \u003cp\u003eIncreasing the Test Data Size 183\u003c\/p\u003e \u003cp\u003eImplementing a Neural Network in Java 183\u003c\/p\u003e \u003cp\u003eCreating the Project 183\u003c\/p\u003e \u003cp\u003eWriting the Code 185\u003c\/p\u003e \u003cp\u003eConverting from CSV to Arff 188\u003c\/p\u003e \u003cp\u003eRunning the Neural Network 188\u003c\/p\u003e \u003cp\u003eDeveloping Neural Networks with DeepLearning4J 189\u003c\/p\u003e \u003cp\u003eModifying the Data 189\u003c\/p\u003e \u003cp\u003eViewing Maven Dependencies 190\u003c\/p\u003e \u003cp\u003eHandling the Training Data 191\u003c\/p\u003e \u003cp\u003eNormalizing Data 191\u003c\/p\u003e \u003cp\u003eBuilding the Model 192\u003c\/p\u003e \u003cp\u003eEvaluating the Model 193\u003c\/p\u003e \u003cp\u003eSaving the Model 193\u003c\/p\u003e \u003cp\u003eBuilding and Executing the Program 194\u003c\/p\u003e \u003cp\u003eSummary 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Machine Learning with Text Documents 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreparing Text for Analysis 198\u003c\/p\u003e \u003cp\u003eApache Tika 198\u003c\/p\u003e \u003cp\u003eCleaning the Text Data 203\u003c\/p\u003e \u003cp\u003eStopwords 205\u003c\/p\u003e \u003cp\u003eStemming 206\u003c\/p\u003e \u003cp\u003eN-grams 206\u003c\/p\u003e \u003cp\u003eTF\/IDF 207\u003c\/p\u003e \u003cp\u003eLoading the Documents 207\u003c\/p\u003e \u003cp\u003eCalculating the Term Frequency 208\u003c\/p\u003e \u003cp\u003eCalculating the Inverse Document Frequency 208\u003c\/p\u003e \u003cp\u003eComputing the TF\/IDF Score 209\u003c\/p\u003e \u003cp\u003eReviewing the Final Code Listing 209\u003c\/p\u003e \u003cp\u003eWord2Vec 211\u003c\/p\u003e \u003cp\u003eLoading the Raw Text Data 212\u003c\/p\u003e \u003cp\u003eTokenizing the Strings 212\u003c\/p\u003e \u003cp\u003eCreating the Model 212\u003c\/p\u003e \u003cp\u003eEvaluating the Model 213\u003c\/p\u003e \u003cp\u003eReviewing the Final Code 214\u003c\/p\u003e \u003cp\u003eBasic Sentiment Analysis 216\u003c\/p\u003e \u003cp\u003eLoading Positive and Negative Words 216\u003c\/p\u003e \u003cp\u003eLoading Sentences 217\u003c\/p\u003e \u003cp\u003eCalculating the Sentiment Score 217\u003c\/p\u003e \u003cp\u003eReviewing the Final Code 218\u003c\/p\u003e \u003cp\u003ePerforming a Test Run 220\u003c\/p\u003e \u003cp\u003eFurther Development 220\u003c\/p\u003e \u003cp\u003eSummary 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Machine Learning with Images 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is an Image? 223\u003c\/p\u003e \u003cp\u003eIntroducing Color Depth 224\u003c\/p\u003e \u003cp\u003eImages in Machine Learning 225\u003c\/p\u003e \u003cp\u003eBasic Classifi cation with Neural Networks 226\u003c\/p\u003e \u003cp\u003eBasic Settings 226\u003c\/p\u003e \u003cp\u003eLoading the MNIST Images 226\u003c\/p\u003e \u003cp\u003eModel Configuration 227\u003c\/p\u003e \u003cp\u003eModel Training 228\u003c\/p\u003e \u003cp\u003eModel Evaluation 228\u003c\/p\u003e \u003cp\u003eConvolutional Neural Networks 228\u003c\/p\u003e \u003cp\u003eHow CNNs Work 228\u003c\/p\u003e \u003cp\u003eCNN Demonstration 231\u003c\/p\u003e \u003cp\u003eDownloading the Image Data 231\u003c\/p\u003e \u003cp\u003eBasic Setup 232\u003c\/p\u003e \u003cp\u003eHandling the Training and Test Data 233\u003c\/p\u003e \u003cp\u003eImage Preparation 233\u003c\/p\u003e \u003cp\u003eCNN Model Configuration 234\u003c\/p\u003e \u003cp\u003eModel Training 236\u003c\/p\u003e \u003cp\u003eModel Evaluation 236\u003c\/p\u003e \u003cp\u003eSaving the Model 237\u003c\/p\u003e \u003cp\u003eTransfer Learning 237\u003c\/p\u003e \u003cp\u003eSummary 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Machine Learning Streaming with Kafka 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat You Will Learn in This Chapter 239\u003c\/p\u003e \u003cp\u003eFrom Machine Learning to Machine Learning Engineer 240\u003c\/p\u003e \u003cp\u003eFrom Batch Processing to Streaming Data Processing 241\u003c\/p\u003e \u003cp\u003eWhat is Kafka? 241\u003c\/p\u003e \u003cp\u003eHow Does It Work? 241\u003c\/p\u003e \u003cp\u003eFault Tolerance 243\u003c\/p\u003e \u003cp\u003eFurther Reading 243\u003c\/p\u003e \u003cp\u003eInstalling Kafka 243\u003c\/p\u003e \u003cp\u003eKafka as a Single-Node Cluster 244\u003c\/p\u003e \u003cp\u003eKafka as a Multinode Cluster 245\u003c\/p\u003e \u003cp\u003eTopics Management 247\u003c\/p\u003e \u003cp\u003eCreating Topics 248\u003c\/p\u003e \u003cp\u003eFinding Out Information About Existing Topics 248\u003c\/p\u003e \u003cp\u003eDeleting Topics 249\u003c\/p\u003e \u003cp\u003eSending Messages from the Command Line 249\u003c\/p\u003e \u003cp\u003eReceiving Messages from the Command Line 250\u003c\/p\u003e \u003cp\u003eKafka Tool UI 250\u003c\/p\u003e \u003cp\u003eWriting Your Own Producers and Consumers 251\u003c\/p\u003e \u003cp\u003eProducers in Java 251\u003c\/p\u003e \u003cp\u003eConsumers in Java 255\u003c\/p\u003e \u003cp\u003eBuilding and Running the Applications 258\u003c\/p\u003e \u003cp\u003eThe Streaming API 260\u003c\/p\u003e \u003cp\u003eBuilding a Streaming Machine Learning System 262\u003c\/p\u003e \u003cp\u003ePlanning the System 263\u003c\/p\u003e \u003cp\u003eContinuous Training 265\u003c\/p\u003e \u003cp\u003eDetermining Which Models to Use for Predictions 266\u003c\/p\u003e \u003cp\u003eDetermining Which Algorithms to Use 268\u003c\/p\u003e \u003cp\u003eSimple Linear Regression 271\u003c\/p\u003e \u003cp\u003eNeural Network 274\u003c\/p\u003e \u003cp\u003eKafka Topics 281\u003c\/p\u003e \u003cp\u003eCreating the Topics 281\u003c\/p\u003e \u003cp\u003eKafka Connect 283\u003c\/p\u003e \u003cp\u003eWhy Persist the Event Data? 283\u003c\/p\u003e \u003cp\u003eThe REST API Microservice 285\u003c\/p\u003e \u003cp\u003eProcessing Commands and Events 287\u003c\/p\u003e \u003cp\u003eFinding Kafka Brokers 288\u003c\/p\u003e \u003cp\u003eA Command or an Event? 289\u003c\/p\u003e \u003cp\u003eMaking Predictions 293\u003c\/p\u003e \u003cp\u003ePrediction Streaming API 293\u003c\/p\u003e \u003cp\u003ePrediction Functions 296\u003c\/p\u003e \u003cp\u003ePredicting Linear Regression 298\u003c\/p\u003e \u003cp\u003ePredicting the Neural Network Model 299\u003c\/p\u003e \u003cp\u003eRunning the Project 301\u003c\/p\u003e \u003cp\u003eRun MySQL 301\u003c\/p\u003e \u003cp\u003eRun Zookeeper 301\u003c\/p\u003e \u003cp\u003eRun Kafka 301\u003c\/p\u003e \u003cp\u003eCreate the Topics 301\u003c\/p\u003e \u003cp\u003eRun Kafka Connect 301\u003c\/p\u003e \u003cp\u003eModel Builds 302\u003c\/p\u003e \u003cp\u003eRun Events Streaming Application 302\u003c\/p\u003e \u003cp\u003eRun Prediction Streaming Application 302\u003c\/p\u003e \u003cp\u003eStart the API 302\u003c\/p\u003e \u003cp\u003eSend JSON Training Data 302\u003c\/p\u003e \u003cp\u003eTrain a Model 302\u003c\/p\u003e \u003cp\u003eMake a Prediction 303\u003c\/p\u003e \u003cp\u003eSummary 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Apache Spark 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSpark: A Hadoop Replacement? 305\u003c\/p\u003e \u003cp\u003eJava, Scala, or Python? 306\u003c\/p\u003e \u003cp\u003eDownloading and Installing Spark 306\u003c\/p\u003e \u003cp\u003eA Quick Intro to Spark 306\u003c\/p\u003e \u003cp\u003eStarting the Shell 307\u003c\/p\u003e \u003cp\u003eData Sources 307\u003c\/p\u003e \u003cp\u003eTesting Spark 308\u003c\/p\u003e \u003cp\u003eSpark Monitor 309\u003c\/p\u003e \u003cp\u003eComparing Hadoop MapReduce to Spark 310\u003c\/p\u003e \u003cp\u003eWriting Stand-Alone Programs with Spark 313\u003c\/p\u003e \u003cp\u003eSpark Programs in Java 313\u003c\/p\u003e \u003cp\u003eSpark Program Summary 318\u003c\/p\u003e \u003cp\u003eSpark SQL 318\u003c\/p\u003e \u003cp\u003eBasic Concepts 318\u003c\/p\u003e \u003cp\u003eWrapping Up SparkSQL 323\u003c\/p\u003e \u003cp\u003eSpark Streaming 323\u003c\/p\u003e \u003cp\u003eBasic Concepts 323\u003c\/p\u003e \u003cp\u003eCreating Your First Spark Stream 324\u003c\/p\u003e \u003cp\u003eSpark Streams from Kafka 326\u003c\/p\u003e \u003cp\u003eMLib: The Machine Learning Library 327\u003c\/p\u003e \u003cp\u003eDependencies 328\u003c\/p\u003e \u003cp\u003eDecision Trees 328\u003c\/p\u003e \u003cp\u003eClustering 330\u003c\/p\u003e \u003cp\u003eAssociation Rules with FP-Growth 332\u003c\/p\u003e \u003cp\u003eSummary 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Machine Learning with R 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInstalling R 337\u003c\/p\u003e \u003cp\u003emacOS 337\u003c\/p\u003e \u003cp\u003eWindows 338\u003c\/p\u003e \u003cp\u003eLinux 338\u003c\/p\u003e \u003cp\u003eYour First Run 338\u003c\/p\u003e \u003cp\u003eInstalling R-Studio 339\u003c\/p\u003e \u003cp\u003eThe R Basics 340\u003c\/p\u003e \u003cp\u003eVariables and Vectors 340\u003c\/p\u003e \u003cp\u003eMatrices 341\u003c\/p\u003e \u003cp\u003eLists 342\u003c\/p\u003e \u003cp\u003eData Frames 343\u003c\/p\u003e \u003cp\u003eInstalling Packages 344\u003c\/p\u003e \u003cp\u003eLoading in Data 345\u003c\/p\u003e \u003cp\u003ePlotting Data 347\u003c\/p\u003e \u003cp\u003eSimple Statistics 350\u003c\/p\u003e \u003cp\u003eSimple Linear Regression 350\u003c\/p\u003e \u003cp\u003eCreating the Data 351\u003c\/p\u003e \u003cp\u003eThe Initial Graph 351\u003c\/p\u003e \u003cp\u003eRegression with the Linear Model 351\u003c\/p\u003e \u003cp\u003eMaking a Prediction 352\u003c\/p\u003e \u003cp\u003eBasic Sentiment Analysis 353\u003c\/p\u003e \u003cp\u003eUsing Functions to Load in Word Lists 353\u003c\/p\u003e \u003cp\u003eWriting a Function to Score Sentiment 354\u003c\/p\u003e \u003cp\u003eTesting the Function 354\u003c\/p\u003e \u003cp\u003eApriori Association Rules 355\u003c\/p\u003e \u003cp\u003eInstalling the arules Package 355\u003c\/p\u003e \u003cp\u003eGathering the Training Data 356\u003c\/p\u003e \u003cp\u003eImporting the Transaction Data 356\u003c\/p\u003e \u003cp\u003eRunning the Apriori Algorithm 357\u003c\/p\u003e \u003cp\u003eInspecting the Results 358\u003c\/p\u003e \u003cp\u003eAccessing R from Java 358\u003c\/p\u003e \u003cp\u003eInstalling the rJava Package 358\u003c\/p\u003e \u003cp\u003eCreating Your First Java Code in R 359\u003c\/p\u003e \u003cp\u003eCalling R from Java Programs 359\u003c\/p\u003e \u003cp\u003eSetting Up an Eclipse Project 360\u003c\/p\u003e \u003cp\u003eCreating the Java\/R Class 361\u003c\/p\u003e \u003cp\u003eRunning the Example 361\u003c\/p\u003e \u003cp\u003eExtending Your R Implementations 363\u003c\/p\u003e \u003cp\u003eConnecting to Social Media with R 364\u003c\/p\u003e \u003cp\u003eSummary 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Kafka Quick Start 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInstalling Kafka 367\u003c\/p\u003e \u003cp\u003eStarting Zookeeper 367\u003c\/p\u003e \u003cp\u003eStarting Kafka 368\u003c\/p\u003e \u003cp\u003eCreating Topics 368\u003c\/p\u003e \u003cp\u003eListing Topics 369\u003c\/p\u003e \u003cp\u003eDescribing a Topic 369\u003c\/p\u003e \u003cp\u003eDeleting Topics 369\u003c\/p\u003e \u003cp\u003eRunning a Console Producer 370\u003c\/p\u003e \u003cp\u003eRunning a Console Consumer 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B The Twitter API Developer Application Configuration 371\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Useful Unix Commands 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Sample Data 375\u003c\/p\u003e \u003cp\u003eShowing the Contents: cat, more, and less 376\u003c\/p\u003e \u003cp\u003eExample Command 376\u003c\/p\u003e \u003cp\u003eExpected Output 376\u003c\/p\u003e \u003cp\u003eFiltering Content: grep 377\u003c\/p\u003e \u003cp\u003eExample Command for Finding Text 377\u003c\/p\u003e \u003cp\u003eExample Output 377\u003c\/p\u003e \u003cp\u003eSorting Data: sort 378\u003c\/p\u003e \u003cp\u003eExample Command for Basic Sorting 378\u003c\/p\u003e \u003cp\u003eExample Output 378\u003c\/p\u003e \u003cp\u003eFinding Unique Occurrences: uniq 380\u003c\/p\u003e \u003cp\u003eShowing the Top of a File: head 381\u003c\/p\u003e \u003cp\u003eCounting Words: wc 381\u003c\/p\u003e \u003cp\u003eLocating Anything: find 382\u003c\/p\u003e \u003cp\u003eCombining Commands and Redirecting Output 383\u003c\/p\u003e \u003cp\u003ePicking a Text Editor 383\u003c\/p\u003e \u003cp\u003eColon Frenzy: Vi and Vim 383\u003c\/p\u003e \u003cp\u003eNano 384\u003c\/p\u003e \u003cp\u003eEmacs 384\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Further Reading 385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachine Learning 385\u003c\/p\u003e \u003cp\u003eStatistics 386\u003c\/p\u003e \u003cp\u003eBig Data and Data Science 386\u003c\/p\u003e \u003cp\u003eVisualization 387\u003c\/p\u003e \u003cp\u003eMaking Decisions 387\u003c\/p\u003e \u003cp\u003eDatasets 388\u003c\/p\u003e \u003cp\u003eBlogs 388\u003c\/p\u003e \u003cp\u003eUseful Websites 389\u003c\/p\u003e \u003cp\u003eThe Tools of the Trade 389\u003c\/p\u003e \u003cp\u003eIndex 391\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407107203415,"sku":"9781119642145","price":34.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119642145.jpg?v=1730498200","url":"https:\/\/bookcurl.com\/products\/machine-learning-9781119642145","provider":"Book Curl","version":"1.0","type":"link"}