Index. Mark Wickham 2018 M. Wickham, Practical Java Machine Learning,

Transcription

1 A Access points (AP), 80 Amazon AWS advantages, 123 cloud-based services, 123 data schema, 128 data validation, EC2 AMI, 131 free tier pricing details, 147 Java developers, 143 ML model, 126 evaluation, 130 services, 124 settings, 129 process and experiment, 131 regression algorithm, 130 RMSE, 131 SageMaker, 141 S3 input data, 127 Synergy Research Group, 123 uploading ML data, 126 Weka ML classification, deep learning packages, initial connection, 135 Java, 136 OpenJDK, Oracle JDK package, SSH client, 135 weka directory, 139 Amazon Linux, 132 AMR, 161 Android data visualization Android Studio, app screenshot, 102 FrameLayout, 101 mobile devices, 97 project file summary, 97 WebView class, , 102 Android SDK, 30 Android Studio, 36, 120 download, features, 36, 37 install, SDK Manager, 38 version 3.1.2, 38 Apache MXNet, 21 Apache OpenOffice Calc advantgaes, CSV file, 59, 61 installation, 58 ML data, API, ML API providers, 151 cloud providers, 148 high-level ML API comparison, 149 REST APIs, 150 Artificial intelligence (AI), 3 definition, 2 with DL, 21 Mark Wickham 2018 M. Wickham, Practical Java Machine Learning, 383

2 Artificial intelligence (AI) (cont.) domains, 3 representation of past and present, 5 subfield relationships, 4 winter periods, 5 7 Attribute-relation file format (ARFF) CSV file, differences, 63 Weka machine learning, 62 AWS Toolkit, 42 B Big data, 51 BlueJ IDE, 36 Bottom-up approach, 315 bq tool, 117 Business case challenges and concerns, 8 9 data science platform (see Data science platform) monetization, 13 C Caffe, 21 Cascading style sheet (CSS), 91 Cassandra Query Language (CQL), 174 Classic machine learning (CML) classifiers and clustering algorithms, 195 DBSCAN, 206 definition, 2 vs. DL, EM algorithm, 208 K-means algorithm, 204 KNN, 199 mobile devices, NB, 195 performance and data set size, 19 Random Forest Algorithm (RF), 197 relationship diagram, 14 SVM, 202 see also Machine learning gate (MLG) Classification and regression trees (CART), 197 Cloud data, ML Apache Cassandra Java Interface, 172 AWS S3 buckets object store, 167 data storage services, 166 NoSQL databases, 168 NoSQL data store methods, 170 unstructured data, 167 virtual machine, 167 Cloud platforms big four service providers, 106 cloud provider considerations, 108 competitive positioning, 109 IaaS, 106 ML-related services, 107 ML solutions, pricing, 110 Cloud Tools for Eclipse (CT4E), 120 Competitive advantage bridging domains, 45 creative thinking, 44 Computational Network Toolkit (CNTK), 21 Confusion matrix, 215 Convolutional neural networks (CNN), 186 D Data categories, Data definition, Data dictionary, 74 Data-driven documents (D3), 86 Data formats 384

3 ARFF files, CSV files, 57.dat files, file types, 55 JSON (see JavaScript Object Notation (JSON)) OpenOffice (see Apache OpenOffice Calc) PAMAP2_Dataset, 55, 57 plain text files, 55 Dataku, 12 Data mining (DM), 2 3 Data nomenclature qualitative data, 53 quantitative data, 53 Data preprocessing Activity Id, 73 attributes, 73 data type identification, 74 duplicates, 75 erroneous values and outliers, features, 73 instances, 73 JSON validation, labels, 73 mathematical/statistical principles, 72 missing values, ML-Gate 5, 72 OpenOffice Calc, macro processing, Data science platform access prebuilt model, 11 build vs. buy decision, 10 list of popular, 12 recommendations for site visitors, 11 Data scientist, data defining, DataStax, 172 Data types Nominal data, 53 Ordinal data, 53 Discrete data, 53 Continuous data, 53 Data visualization, 84 DBSCAN algorithm, 246, 257 attributes, 261 color-coded, 261 data cluster, eruption time, 257 noise, 263 Old Faithful geyser dataset, 258 parameter adjustments, visualization, 260 waiting time, 257 Weka Explorer, 258 Deep learning (DL) AI definitions and domain, 3 AI engines, 21 algorithms, 186 deep networks, 20 vs. CML, definition, 2 neural network algorithms, 52 performance and data set size, tuning methods, 20 Dendogram, Density-based spatial clustering of applications with noise (DBSCAN), 206 see also DBSCAN algorithm DL4J, 21 DNF (Did Not Finish) entry, 287 D3 Plus, 86 D3 visualization cluster dendogram, cluster-dendo-json.html file, 93 collapsible tree,

4 D3 visualization (cont.) CSS, 91 CSV file, 88, 90 d3.nest()function, 94 dendo-csv.html, dendogram, 90, 96 flare.csv and flare.json, 87 graphical style, 87 JavaScript, 88, 96 JSON file, 93 project file summary, 87 radial dendogram, 92 structure of, 88 tree, 91 tree-dendo-csv.html file, 91 underscore.nest()function, 94 web server, E Eclipse IDE download, 39 features, 36 for Java developers, installing, 40 ML plugin, 42 ELKI, 236 Exception handling, 357, 359 Exercises in Programming Style (book), 27 Explorer, Attributes tab, 250 Classify tab, 250 Cluster tab, 250 data preprocessing, 250 key options, Visualize tab, 250 Expectation-Maximization (EM) algorithm, 208 F Fake data, 7 G gcloud compute command-line tool, 118 gcloud init command, 118 gcloud tool, 116 GCP Cloud Speech API App Android Audio audio recording implementation, 163 AudioRecord and AudioTrack classes, 161, 162 formats, 161 MainActivity.java, ProcessVoice class, 162, 163 recognizeinputstream method, 163 SpeechService.java, types, 162 VoiceRecorder.java, 162 audio input methods, 154 credential.json file, 160 displaying active credentials, 160 file summary, 153 GCP monitoring API, JSON configuration file, JSON private key file, JSON service account key type, 158 ML, 156 service account key, 157 service-based architecture, 165 Google Cloud Platform (GCP) client libraries, 120 Cloud Machine Learning Engine, 121 CT4E, 120 dashboard, 113 free tier pricing details, 122 GCE VM,

5 Google Cloud SDK, 116 hardware and software resources, 112 higher-level categories, Google Cloud SDK, 116 Google Cloud Speech API, 151 Google Cloud Tools, 42 Google Compute Engine (GCE) Virtual Machines (VM), 114 Government data, 50 gsutil tool, 117 H H2O.ai, 12 Holdout method, 218 I IBM, 12 IDE, 36 Android Studio (see Android Studio) BlueJ, 36 Eclipse (see Eclipse IDE) IntelliJ IDEA, 36 NetBeans, 36 Inference process, 25 Infrastructure as a Service (IaaS), 106 Integrating models managing models approaches, 306 best practices, 307 device constraints, model version control, optimal model size, updating, Raspberry Pi (see Raspberry Pi integration) sensor data (see Sensor data) Weka (see Weka) IntelliJ IDEA, 36 Internet data, 49 Iris flower dataset, 62 J Java cards, 29 devices, IDE (see IDE) installing, lambda expressions, 30 market share, mathematical algorithm, programming language, 35 programming language platforms, Java 8, 30, 35 Java API, Weka applying filters, classifier, clusterer, 312 label attribute, setting, 310 loading data, loading models, making predictions, training and testing, working with options, 309 Java FX, 30 Java ML environments, , 236 advantages, 228 ELKI, 236 factors, 231 Java-ML, 236 KNIME, links,

6 Java ML environments (cont.) RapidMiner, 232, 234 Weka, 232 Java Performance (book), 31 Java Platform, Enterprise Edition (Java EE), 29 Java Platform, Micro Edition (Java ME), 30 Java Runtime Environment (JRE), 29 JavaScript Object Notation (JSON) Android SDK, arrays and objects, 64 data interchange, 68 definition, 66 iris.arff file, Java JDK, 70 Eclipse Java build path, JSON libraries, 70 Maven repository, 71 NoSQL databases, 69 objects and arrays, properties, 64 structure, validation, JavaScript visualization libraries, Java SE Developer Kit (JDK), 29 Java, The Complete Reference (book), 31 JSONArray, 65 JSONObject, 65 K Keras, 21 K-fold cross-validation, 218 K-means algorithm, 204 K-nearest neighbor (KNN) algorithm, 2, 199, 278, 305 accuracy, issue, 280 options, 279 KNIME, 12, 234 PMML, 235 KnowledgeFlow, Weka layouts, result list, 268 textviewer, three-clusterer comparison, templates, 264 Kotlin, 35 KStar (K*), see K-nearest neighbor (KNN) algorithm kubectl tool, 117 L Labeled vs. unlabeled data, 179 Lambda expressions, 30 Least squares method, 77 Linear regression algorithm, 185 M Machine learning gate (MLG) acquiring data, 24 deployment, 26 development projects, generate model, 25 integrate model, 26 process/clean/visualize data, 25 test and refine model, 25 well-defined problem, Machine Learning (ML) algorithm analysis confusion matrix, 215 K-fold cross-validation, 218 ROC,

7 algorithm performance deep learning, 209 MNIST database, 209 algorithm selection, 178 algorithm styles, 179 supervised learning, 180 API (see API, ML) data format (see Data formats) data preprocessing (see Data preprocessing) definition, 2 DL algorithms, 186, 192 domain, 3 flowchart, 192 functional algorithm decision process, 193 Google Cloud Speech API, 178 Java AbstractClassifier class, classification algorithms, 222 clustering algorithms, 223 concurrency, 225 GNU General Public License, 224 lambda expressions, 225 random forest algorithm, 220 RandomTree.java class, 221 stream API, 225 Subversion repository, 220 Java ML environments, 178 linear regression algorithm, 185 megatrends (see Megatrends) ML-Gate 3 (MLG3), 178 red pill/blue pill metaphor, 18 reinforcement learning, 188 semi-supervised learning algorithms, 184, 191 supervised ML Algorithms, 189 unsupervised learning algorithms, 182, 190 MathWorks, 12 Matrix Toolkit for Java (mtj) library, 317 Megatrends advancement, ML algorithms, 52 cloud service providers, 51 computing resources, 51 data categories, relative data sizes, 51 Microsoft Azure, 12 Microsoft Azure Toolkit, 42 Missing Completely At Random (MCAR), 74 Mixed National Institute of Standards and Technology (MNIST) characteristics, 210 classification algorithm performance, 212 classifiers, 212 CML classification algorithm, 213 data, 214 non-dl algorithms, 214 visualization of, ML-Gates, ML-Gates 1, 298 ML-Gates methodology, Mobile device data, 49 MP3, 161 N Naive Bayesian (NB) networks, 181 Naive Bayes (NB) algorithm, 2, 195 accuracy, 283 ActivityID, 281 classification, kernel setting option, 281 NaN,

8 Net Beans IDE, 36, 43 Neural networks (NN), 2 NR (Not Reported) entries, 287 O Open source computer vision (OpenCV), 21 P PAMAP2_Dataset, 186 Predictive Model Markup Language (PMML), 235 Private data, 50 Public data, 50 Python, 35 Q Qualitative data, 53 Quant Components, 42 Quantitative data, 53 R Random forest (RF) algorithm, 2, 181, accuracy, 278 classification, data classification, 274 options, 274 RapidMiner, 12 jar file libraries, 234 main interface, 233 pricing, 234 Raspberry Pi integration, 337 features, 339 GUI considerations, 341 Old Faithful project auto starting ML apps, classifier model, exception handling, 357, 359 exporting runnable jar file, GUI implementation, , 353 Java layout managers, ML code, overview, project setup, 347 requirements, 343 single instance data file, setup for ML, Weka API library, 342 Raspberry Pi 3 model, 338 Receiver operator characteristic (ROC), 216 Recurrent neural networks (RNN), 186 Regression, 77 Reinforcement learning (RL), 2, 188 Relational database management systems (RDBMS), 168 Remote machine interface (RMI), 382 R for data science, 42 Root Mean Square Error (RMSE), 131 S SAP, 12 Semi-supervised learning algorithms, 184 Sensor data, 49 Android Activity Tracker project, 370 architecture, implementation, model integration, results, improving, timer, implementing, Android sensors,

9 availability, 365 SensorEvent, 364 SensorEventListener, 364 SensorManager, 364 support, 364 Raspberry Pi, units of measure, 369 Sequential minimal optimization (SMO), 283 accuracy, 286 classifier output, default options, Service Level Agreements (SLAs), 122 The Signal and the Noise (book), 52 Smartphone, 80 Social media data, 49 Spark MLlib, 21 Statistics, 3 Support vector machine (SVM) algorithm, 2, 181, 202, Synthetic data, 50 T TensorFlow, 21, 133 Theano, 21 Top-down approach, 315 Torch package, 21 U University of Waikato, 220 Unsupervised learning algorithms, 182 V Visualization Android data (see Android data visualization) D3 Plus, 86 D3 visualization (see D3 visualization) JavaScript visualization libraries, W, X, Y Waikato Environment (Weka), see Weka Weka, 232 for Android AWT, 314 libraries in Android Studio, libraries in Eclipse, Net Beans, 314 performance, Swing, 314 Weka Model Create project, Weka Model Load project, 321, clustering algorithms DBSCAN algorithm (see DBSCAN algorithm) KnowledgeFlow (see KnowledgeFlow, Weka) CML algorithms, 232 definition, 12 documentation PDF, 249 Weka book, 249 Weka manual, 249 YouTube, 249 Explorer (see Explorer) filters, importing and exporting, installation, 236 configuration, 238, GUI Chooser, 238 Java parameters, 241 logo, 237 package manager,

10 Weka (cont.) platforms, 237.prop files, settings, 244 versions, 236 Java API (see Java API, Weka) KnowledgeFlow, license notes, model evaluation classifier s performance, DNF entry, 287 multiple ROC curves, NR entries, 287 observations, 287 modules, 248 preprocessing (data cleaning) ActivityIDs, cleaned dataset, 269 reasons, 269 structure, 270 simple CLI Shell, 255 commands, 256 KnowledgeFlow interface, 257 MultiFilter operation, 256 serialized option, 256 SVM algorithm, 283, 285 options, 283 SMO algorithm, 284, 286 Wifi gathering Android WifiManager, 81 AP, 80 data acquisition, normalized value, 81 key code, 83 signal strength, 81 WifiCollect.java, WifiManager.calculateSignalLevel method, 81 Windows, Apache web server, MySQL, PHP (WAMP), 88 Z 7-Zip unzipping tool,