verkaufen Checkout Funnel Online Banking oder Homebanking Sale Rückerstattung und den damit verbundenen Möglichkeiten für Unternehmer und die Für die Suchmaschinenoptimierung spielen Metadaten eine wesentliche Rolle Einkaufstätigkeit billig 391 389Index Trade the of Tools 389The Websites 388Useful 388Blogs 387Datasets Decisions 387Making 386Visualization Science Data and Data 386Big 385Statistics Learning 385Machine Reading Further D 384Appendix 384Emacs 383Nano Vim and Vi Frenzy: 383Colon Editor Text a 383Picking Output Redirecting and Commands 382Combining find Anything: 381Locating wc Words: 381Counting head File: a of Top the 380Showing uniq Occurrences: Unique 378Finding Output 378Example Sorting Basic for Command 378Example sort Data: 377Sorting Output 377Example Text Finding for Command 377Example grep Content: 376Filtering Output 376Expected Command 376Example less and more, cat, Contents: the 375Showing Data Sample 375Using Commands Unix Useful C 371Appendix Configuration Application Developer API Twitter The B 370Appendix Consumer Console a 370Running Producer Console a 369Running Topics 369Deleting Topic a 369Describing Topics 368Listing Topics 368Creating Kafka 367Starting Zookeeper 367Starting Kafka 367Installing Start Quick Kafka A 366Appendix 364Summary R with Media Social to 363Connecting Implementations R Your 361Extending Example the 361Running Class Java/R the 360Creating Project Eclipse an Up 359Setting Programs Java from R 359Calling R in Code Java First Your 358Creating Package rJava the 358Installing Java from R 358Accessing Results the 357Inspecting Algorithm Apriori the 356Running Data Transaction the 356Importing Data Training the 355Gathering Package arules the 355Installing Rules Association 354Apriori Function the 354Testing Sentiment Score to Function a 353Writing Lists Word in Load to Functions 353Using Analysis Sentiment 352Basic Prediction a 351Making Model Linear the with 351Regression Graph Initial 351The Data the 350Creating Regression Linear 350Simple Statistics 347Simple Data 345Plotting Data in 344Loading Packages 343Installing Frames 342Data 341Lists 340Matrices Vectors and 340Variables Basics R 339The R-Studio 338Installing Run First 338Your 338Linux 337Windows 337macOS R 337Installing R with Learning Machine 14 335Chapter 332Summary FP-Growth with Rules 330Association 328Clustering Trees 328Decision 327Dependencies Library Learning Machine The 326MLib: Kafka from Streams 324Spark Stream Spark First Your 323Creating Concepts 323Basic Streaming 323Spark SparkSQL Up 318Wrapping Concepts 318Basic SQL 318Spark Summary Program 313Spark Java in Programs 313Spark Spark with Programs Stand-Alone 310Writing Spark to MapReduce Hadoop 309Comparing Monitor 308Spark Spark 307Testing Sources 307Data Shell the 306Starting Spark to Intro Quick 306A Spark Installing and 306Downloading Python? or Scala, 305Java, Replacement? Hadoop A 305Spark: Spark Apache 13 303Chapter 303Summary Prediction a 302Make Model a 302Train Data Training JSON 302Send API the 302Start Application Streaming Prediction 302Run Application Streaming Events 302Run Builds 301Model Connect Kafka 301Run Topics the 301Create Kafka 301Run Zookeeper 301Run MySQL 301Run Project the 299Running Model Network Neural the 298Predicting Regression Linear 296Predicting Functions 293Prediction API Streaming 293Prediction Predictions 289Making Event? an or Command 288A Brokers Kafka 287Finding Events and Commands 285Processing Microservice API REST 283The Data? Event the Persist 283Why Connect 281Kafka Topics the 281Creating Topics 274Kafka Network 271Neural Regression Linear 268Simple Use to Algorithms Which 266Determining Predictions for Use to Models Which 265Determining Training 263Continuous System the 262Planning System Learning Machine Streaming a 260Building API Streaming 258The Applications the Running and 255Building Java in 251Consumers Java in 251Producers Consumers and Producers Own Your 250Writing UI Tool 250Kafka Line Command the from Messages 249Receiving Line Command the from Messages 249Sending Topics 248Deleting Topics Existing About Information Out 248Finding Topics 247Creating Management 245Topics Cluster Multinode a as 244Kafka Cluster Single-Node a as 243Kafka Kafka 243Installing Reading 243Further Tolerance 241Fault Work? It Does 241How Kafka? is 241What Processing Data Streaming to Processing Batch 240From Engineer Learning Machine to Learning Machine 239From Chapter This in Learn Will You 239What Kafka with Streaming Learning Machine 12 238Chapter 237Summary Learning 237Transfer Model the 236Saving Evaluation 236Model Training 234Model Configuration Model 233CNN Preparation 233Image Data Test and Training the 232Handling Setup 231Basic Data Image the 231Downloading Demonstration 228CNN Work CNNs 228How Networks Neural 228Convolutional Evaluation 228Model Training 227Model Configuration 226Model Images MNIST the 226Loading Settings 226Basic Networks Neural with cation Classifi 225Basic Learning Machine in 224Images Depth Color 223Introducing Image? an is 223What Images with Learning Machine 11 221Chapter 220Summary Development 220Further Run Test a 218Performing Code Final the 217Reviewing Score Sentiment the 217Calculating Sentences 216Loading Words Negative and Positive 216Loading Analysis Sentiment 214Basic Code Final the 213Reviewing Model the 212Evaluating Model the 212Creating Strings the 212Tokenizing Data Text Raw the 211Loading 209Word2Vec Listing Code Final the 209Reviewing Score TF/IDF the 208Computing Frequency Document Inverse the 208Calculating Frequency Term the 207Calculating Documents the 207Loading 206TF/IDF 206N-grams 205Stemming 203Stopwords Data Text the 198Cleaning Tika 198Apache Analysis for Text 197Preparing Documents Text with Learning Machine 10 195Chapter 194Summary Program the Executing and 193Building Model the 193Saving Model the 192Evaluating Model the 191Building Data 191Normalizing Data Training the 190Handling Dependencies Maven 189Viewing Data the 189Modifying DeepLearning4J with Networks Neural 188Developing Network Neural the 188Running Arff to CSV from 185Converting Code the 183Writing Project the 183Creating Java in Network Neural a 183Implementing Size Data Test the 182Increasing Network the 180Altering Network the 178Training Perceptron Multilayer the 177Configuring Weka into Data the 175Loading Dataset a 175Generating Weka with Networks Neural 174Artificial Networks Neural Artificial for Preparation 173Data Propagation 171Back Perceptrons 170Multilayer Functions 169Activation 169Perceptrons Network Neural Artificial the Down 168Breaking Box Black the 168Trusting Monitoring 167Medical 167Robotics Management Center 167Data Applications 166Credit Trading 166High-Frequency Uses Network Neural 165Artificial Network? Neural a is 165What Networks Neural Artificial 9 164Chapter 158Summary Java with LibSVM 152Implementing Walk-Through Classification 151A LibSVM 151Installing Weka in Machines Vector Support 150Using Classification Non-Linear 148Using Classification Linear 148Using Classification Approach Machines Vector Support 147How Line the Find to Minimizing and 147Maximizing 146Confidence Classifiers 144Linear Classification Multiclass and 144Binary Principles Classification Basic 144The Used? Machines Vector Support are 143Where Machine? Vector Support a is 143What Machines Vector Support 8 142Chapter 141Summary Results the 137Inspecting Application Weka the 136Using Data Basket Raw 136The Walk-Through Baskets--A the 136Mining 135FP-Growth 135Apriori 134Algorithms Process the 134Defining 134Conviction 133Lift 133Confidence 131Support Works Learning Rules Association 130How Diapers and 130Beer Mining Usage 129Web Used? Learning Rules Association is 129Where Learning Rules Association 7 128Chapter 120Summary Method Coded 116The ARFF to File CSV 116Converting Method Command-Line 111The Method Workbench 110The Data the 110Preparing Weka with Clustering 108K-Means Dataset a in Clusters of Number the 106Calculating Works K-Means the 105How Models 105Clustering 105Computing Enforcement 104Law Retail and 104Business Internet 104The Used? Clustering is 103Where Clustering? is 103What Clustering 6 101Chapter 101Summary Iterations Future About 99Thinking Code Classifier the 94Testing Classification the from Code Java 90Creating Tree Decision a Create to Weka 89Using Data 88Training Requirement 88The Weka in Trees 84Decision Work Trees Decision 82How Types Algorithm 82Different Trees Decision of 82Limitations Trees Decision of 81Advantages Trees Decision for 81Uses Trees Decision of Basics 81The Trees Decision with Working 5 80Chapter 77Summary Clojure in Pi Carlo Monte 76Using Numbers Random with Pi 75Finding Randomness 73Embracing Program a 70Writing Spreadsheet Your 70Using Regression Linear Simple 69Using Deviation 68Standard 67Variance Ranges 67Interquartile 66Range 65Median 63Mode Averages Three the Between Relationship 63The Mean 62Geometric Mean 62Harmonic Mean 62Arithmetic 61Mean 60Sum Values Maximum and 59Minimum Statistics Basic 58Introducing Dataset the Converting and 57Loading Dataset Basic a with 57Working Randomness and Regression, Linear Statistics, 4 56Chapter 55Summary JSON to CSV Converting 53Bulk Database to Files 52Migrating Plugins 51Installing Run Quick the 51Using Embulk 50Installing Data 48Migrating Data Weather 47Acquiring API an 46Using Sheets 44Google Paste and 43Copy Data 43Scraping Techniques Acquisition Data 3 40Chapter 40Summary Experiment to Afraid Be 39Don't Data Output About 39Thinking 38Databases 37Spreadsheets 37XML 35YAML 34JSON Variables 34Comma-Separated Text 34Raw Data Input About 33Thinking Cleaning Data on Thoughts 33Final Times and 31Dates Name? Country a in 28What's Dilemma Britney 28The Checks 28Format Checks 27Range Checks 27Length Checks 26Type Checks 26Presence Cleaning and Quality 25Data Line" "Creepy the Cross 25Don't Data User of Anonymity 24The Expectations 24Generational Norms 23Cultural Privacy 23Data Storage 23Cloud-Based Discs 23Physical Storage 22Data Services 22Cloud-Based Machines of Cluster 22A Computer Your 22Using Processing 21Data Knowledge 21Domain Design 21Graphic 20Programming Statistics and 20Mathematics Team Data a 20Building Bias 20Avoiding 19Production 19Refining 19Reporting 19Testing 18Developing 18Planning Process the 18Defining All? Fits Solution 17One 17Competitions Learning 17Transfer Local 16Starting Data! Have Don't 16I Question a with Starts All 15It Cycle Learning Machine 15The Learning Machine for Planning 2 14Chapter 14Summary 14Kaggle Repository Learning Machine Irvine 14UC Repositories 13Data IDEs and Editors 13Text Hadoop and 13Spark 13Kafka 12DeepLearning4J Toolkit 12Weka Version Java the 11Checking Book This in Used 11Software 11Ruby 11Scala 11Matlab 10R 10Python Learning Machine for 10Languages Things of Internet 9The Analytics 7Gaming E-commerce and 7Retail 6Advertising Healthcare and 6Medicine 5Robotics Trading 4Stock 4Software Learning Machine for 4Uses Touch Human 4The Learning 3Unsupervised Learning 3Supervised Learning Machine for Types 3Algorithm Definition 2Summary Mitchell M. 2Tom Samuel 1Arthur Turing 1Alan Learning Machine of 1History Learning? Machine is What 1 xxviiChapter Introduction Das wesentliche daran ist mehr und mehr dreht sich alles um das World Wide Web im besten Fall zu Ihrem Onlineshop führen Der Umsatz der Onlinehändler stieg in den letzten Jahren rapide an Die Metadaten übermitteln Informationen über Onlineshops an Suchmaschinen

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