{"product_id":"machine-learning-isbn-9781119642145","title":"Machine Learning","description":"\u003cb\u003eDig deep into the data with a hands-on guide to machine learning with updated examples and more!\u003c\/b\u003e \u003cp\u003e\u003ci\u003eMachine Learning: Hands-On for Developers and Technical Professionals\u003c\/i\u003e provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.\u003c\/p\u003e \u003cp\u003eAt its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. \u003ci\u003eMachine Learning\u003c\/i\u003e is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLearn the languages of machine learning including Hadoop, Mahout, and Weka\u003c\/li\u003e \u003cli\u003eUnderstand decision trees, Bayesian networks, and artificial neural networks\u003c\/li\u003e \u003cli\u003eImplement Association Rule, Real Time, and Batch learning\u003c\/li\u003e \u003cli\u003eDevelop a strategic plan for safe, effective, and efficient machine learning\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eBy learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, \u003ci\u003eMachine Learning: Hands-On for Developers and Technical Professionals\u003c\/i\u003e provides the skills and techniques required to dig deeper.\u003c\/p\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  \u003cp\u003e\u003cb\u003eJASON BELL\u003c\/b\u003e has worked in software development for over thirty years, now he focuses on large volume data solutions and helping retail and finance customers gain insight from that data with machine learning. He is also an active committee member for several international technology conferences.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLearn more from your data with this hands-on guide to machine learning\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIf you want to get into machine learning but fear the math, this book is your ultimate guide. Specifically designed for non-mathematicians, this useful guide presents a breakdown of each variant of machine learning, with examples and working code. You'll learn the various algorithms, data preparation techniques, trees, and networks, and get acquainted with the tools that help you get more from your data. You'll understand how it works, where it's used, and how to make it great. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eLearn the languages of machine learning: Weka, DeepLearning4J, Spark\u003csup\u003e™\u003c\/sup\u003e, and R\u003c\/li\u003e \u003cli\u003eMake the right data storage and cleaning decisions, tailored to your desired output\u003c\/li\u003e \u003cli\u003eUnderstand decision trees, K-means clustering, artificial neural networks, and association rule learning\u003c\/li\u003e \u003cli\u003eImplement support vector machines knowing the relevant advantages and limitations\u003c\/li\u003e \u003cli\u003eIncorporate Big Data processing techniques with Spark and MLLib\u003c\/li\u003e \u003cli\u003eUse Apache Kafka to capture streaming data and learn in real time\u003c\/li\u003e \u003cli\u003eAccess the tools you need to plan your project and acquire and process data\u003c\/li\u003e \u003cli\u003eStudy examples and use provided working code for hands-on learning\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989549138149,"sku":"NP9781119642145","price":53.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119642145.jpg?v=1761784555","url":"https:\/\/k12savings.com\/es\/products\/machine-learning-isbn-9781119642145","provider":"K12savings","version":"1.0","type":"link"}