book

Mastering Java Machine Learning

Name: Mastering Java Machine Learning
ISBN: 9781785880513

by Uday Kamath, Krishna Choppella

July 2017

Beginner to intermediate

556 pages

13h 8m

English

Packt Publishing

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Mastering Java Machine Learning
Table of Contents
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and moreWhy subscribe?
Customer Feedback
Preface
What this book covers

What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
ErrataPiracyQuestions
1. Machine Learning Review
Machine learning – history and definition
What is not machine learning?
Machine learning – concepts and terminology
Machine learning – types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning – roles and process
RolesProcess
Machine learning – tools and datasets
Datasets
Summary
2. Practical Approach to Real-World Supervised Learning
Formal description and notationData quality analysisDescriptive data analysisBasic label analysisBasic feature analysisVisualization analysisUnivariate feature analysisCategorical featuresContinuous featuresMultivariate feature analysis
Data transformation and preprocessing
Feature constructionHandling missing valuesOutliersDiscretizationData samplingIs sampling needed?Undersampling and oversamplingStratified samplingTraining, validation, and test set
Feature relevance analysis and dimensionality reduction
Feature search techniquesFeature evaluation techniquesFilter approachUnivariate feature selectionInformation theoretic approachStatistical approachMultivariate feature selectionMinimal redundancy maximal relevance (mRMR)Correlation-based feature selection (CFS)Wrapper approachEmbedded approach
Model building
Linear modelsLinear RegressionAlgorithm input and outputHow does it work?Advantages and limitationsNaïve BayesAlgorithm input and outputHow does it work?Advantages and limitationsLogistic RegressionAlgorithm input and outputHow does it work?Advantages and limitationsNon-linear modelsDecision TreesAlgorithm inputs and outputsHow does it work?Advantages and limitationsK-Nearest Neighbors (KNN)Algorithm inputs and outputsHow does it work?Advantages and limitationsSupport vector machines (SVM)Algorithm inputs and outputsHow does it work?Advantages and limitationsEnsemble learning and meta learnersBootstrap aggregating or baggingAlgorithm inputs and outputsHow does it work?Random ForestAdvantages and limitationsBoostingAlgorithm inputs and outputsHow does it work?Advantages and limitations
Model assessment, evaluation, and comparisons
Model assessmentModel evaluation metricsConfusion matrix and related metricsROC and PRC curvesGain charts and lift curvesModel comparisonsComparing two algorithmsMcNemar's TestPaired-t testWilcoxon signed-rank testComparing multiple algorithmsANOVA testFriedman's test
Case Study – Horse Colic Classification
Business problemMachine learning mappingData analysisLabel analysisFeatures analysisSupervised learning experimentsWeka experimentsSample end-to-end process in JavaWeka experimenter and model selectionRapidMiner experimentsVisualization analysisFeature selectionModel process flowModel evaluation metricsEvaluation on Confusion MetricsROC Curves, Lift Curves, and Gain ChartsResults, observations, and analysis
Summary
References
3. Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
NotationLinear methodsPrincipal component analysis (PCA)Inputs and outputsHow does it work?Advantages and limitationsRandom projections (RP)Inputs and outputsHow does it work?Advantages and limitationsMultidimensional Scaling (MDS)Inputs and outputsHow does it work?Advantages and limitationsNonlinear methodsKernel Principal Component Analysis (KPCA)Inputs and outputsHow does it work?Advantages and limitationsManifold learningInputs and outputsHow does it work?Advantages and limitations
Clustering
Clustering algorithmsk-MeansInputs and outputsHow does it work?Advantages and limitationsDBSCANInputs and outputsHow does it work?Advantages and limitationsMean shiftInputs and outputsHow does it work?Advantages and limitationsExpectation maximization (EM) or Gaussian mixture modeling (GMM)Input and outputHow does it work?Advantages and limitationsHierarchical clusteringInput and outputHow does it work?Advantages and limitationsSelf-organizing maps (SOM)Inputs and outputsHow does it work?Advantages and limitationsSpectral clusteringInputs and outputsHow does it work?Advantages and limitationsAffinity propagationInputs and outputsHow does it work?Advantages and limitationsClustering validation and evaluationInternal evaluation measuresNotationR-SquaredDunn's IndicesDavies-Bouldin indexSilhouette's indexExternal evaluation measuresRand indexF-MeasureNormalized mutual information index
Outlier or anomaly detection
Outlier algorithmsStatistical-basedInputs and outputsHow does it work?Advantages and limitationsDistance-based methodsInputs and outputsHow does it work?Advantages and limitationsDensity-based methodsInputs and outputsHow does it work?Advantages and limitationsClustering-based methodsInputs and outputsHow does it work?Advantages and limitationsHigh-dimensional-based methodsInputs and outputsHow does it work?Advantages and limitationsOne-class SVMInputs and outputsHow does it work?Advantages and limitationsOutlier evaluation techniquesSupervised evaluationUnsupervised evaluation
Real-world case study
Tools and softwareBusiness problemMachine learning mappingData collectionData quality analysisData sampling and transformationFeature analysis and dimensionality reductionPCARandom projectionsISOMAPObservations on feature analysis and dimensionality reductionClustering models, results, and evaluationObservations and clustering analysisOutlier models, results, and evaluationObservations and analysis
Summary
References
4. Semi-Supervised and Active Learning
Semi-supervised learningRepresentation, notation, and assumptionsSemi-supervised learning techniquesSelf-training SSLInputs and outputsHow does it work?Advantages and limitationsCo-training SSL or multi-view SSLInputs and outputsHow does it work?Advantages and limitationsCluster and label SSLInputs and outputsHow does it work?Advantages and limitationsTransductive graph label propagationInputs and outputsHow does it work?Advantages and limitationsTransductive SVM (TSVM)Inputs and outputsHow does it work?Advantages and limitationsCase study in semi-supervised learningTools and softwareBusiness problemMachine learning mappingData collectionData quality analysisData sampling and transformationDatasets and analysisFeature analysis resultsExperiments and resultsAnalysis of semi-supervised learning
Active learning
Representation and notationActive learning scenariosActive learning approachesUncertainty samplingHow does it work?Least confident samplingSmallest margin samplingLabel entropy samplingAdvantages and limitationsVersion space samplingQuery by disagreement (QBD)How does it work?Query by Committee (QBC)How does it work?Advantages and limitationsData distribution samplingHow does it work?Expected model changeExpected error reductionVariance reductionDensity weighted methodsAdvantages and limitations
Case study in active learning
Tools and softwareBusiness problemMachine learning mappingData CollectionData sampling and transformationFeature analysis and dimensionality reductionModels, results, and evaluationPool-based scenariosStream-based scenariosAnalysis of active learning results
Summary
References
5. Real-Time Stream Machine Learning
Assumptions and mathematical notations
Basic stream processing and computational techniques
Stream computationsSliding windowsSampling
Concept drift and drift detection
Data managementPartial memoryFull memoryDetection methodsMonitoring model evolutionWidmer and KubatDrift Detection Method or DDMEarly Drift Detection Method or EDDMMonitoring distribution changesWelch's t testKolmogorov-Smirnov's testCUSUM and Page-Hinckley testAdaptation methodsExplicit adaptationImplicit adaptation
Incremental supervised learning
Modeling techniquesLinear algorithmsOnline linear models with loss functionsInputs and outputsHow does it work?Advantages and limitationsOnline Naïve BayesInputs and outputsHow does it work?Advantages and limitationsNon-linear algorithmsHoeffding trees or very fast decision trees (VFDT)Inputs and outputsHow does it work?Advantages and limitationsEnsemble algorithmsWeighted majority algorithmInputs and outputsHow does it work?Advantages and limitationsOnline Bagging algorithmInputs and outputsHow does it work?Advantages and limitationsOnline Boosting algorithmInputs and outputsHow does it work?Advantages and limitationsValidation, evaluation, and comparisons in online settingModel validation techniquesPrequential evaluationHoldout evaluationControlled permutationsEvaluation criteriaComparing algorithms and metrics
Incremental unsupervised learning using clustering
Modeling techniquesPartition basedOnline k-MeansInputs and outputsHow does it work?Advantages and limitationsHierarchical based and micro clusteringInputs and outputsHow does it work?Advantages and limitationsInputs and outputsHow does it work?Advantages and limitationsDensity basedInputs and outputsHow does it work?Advantages and limitationsGrid basedInputs and outputsHow does it work?Advantages and limitationsValidation and evaluation techniquesKey issues in stream cluster evaluationEvaluation measuresCluster Mapping Measures (CMM)V-MeasureOther external measures
Unsupervised learning using outlier detection
Partition-based clustering for outlier detectionInputs and outputsHow does it work?Advantages and limitationsDistance-based clustering for outlier detectionInputs and outputsHow does it work?Exact StormAbstract-CDirect Update of Events (DUE)Micro Clustering based Algorithm (MCOD)Approx StormAdvantages and limitationsValidation and evaluation techniques
Case study in stream learning
Tools and softwareBusiness problemMachine learning mappingData collectionData sampling and transformationFeature analysis and dimensionality reductionModels, results, and evaluationSupervised learning experimentsConcept drift experimentsClustering experimentsOutlier detection experimentsAnalysis of stream learning results
Summary
References
6. Probabilistic Graph Modeling
Probability revisitedConcepts in probabilityConditional probabilityChain rule and Bayes' theoremRandom variables, joint, and marginal distributionsMarginal independence and conditional independenceFactorsFactor typesDistribution queriesProbabilistic queriesMAP queries and marginal MAP queries
Graph concepts
Graph structure and propertiesSubgraphs and cliquesPath, trail, and cycles
Bayesian networks
RepresentationDefinitionReasoning patternsCausal or predictive reasoningEvidential or diagnostic reasoningIntercausal reasoningCombined reasoningIndependencies, flow of influence, D-Separation, I-MapFlow of influenceD-SeparationI-MapInferenceElimination-based inferenceVariable elimination algorithmInput and outputHow does it work?Advantages and limitationsClique tree or junction tree algorithmInput and outputHow does it work?Advantages and limitationsPropagation-based techniquesBelief propagationFactor graphMessaging in factor graphInput and outputHow does it work?Advantages and limitationsSampling-based techniquesForward sampling with rejectionInput and outputHow does it work?Advantages and limitationsLearningLearning parametersMaximum likelihood estimation for Bayesian networksBayesian parameter estimation for Bayesian networkPrior and posterior using the Dirichlet distributionLearning structuresMeasures to evaluate structuresMethods for learning structuresConstraint-based techniquesInputs and outputsHow does it work?Advantages and limitationsSearch and score-based techniquesInputs and outputsHow does it work?Advantages and limitations
Markov networks and conditional random fields
RepresentationParameterizationGibbs parameterizationFactor graphsLog-linear modelsIndependenciesGlobalPairwise MarkovMarkov blanketInferenceLearningConditional random fields
Specialized networks
Tree augmented networkInput and outputHow does it work?Advantages and limitationsMarkov chainsHidden Markov modelsMost probable path in HMMPosterior decoding in HMM
Tools and usage
OpenMarkovWeka Bayesian Network GUI
Case study
Business problemMachine learning mappingData sampling and transformationFeature analysisModels, results, and evaluationAnalysis of results
Summary
References
7. Deep Learning
Multi-layer feed-forward neural networkInputs, neurons, activation function, and mathematical notationMulti-layered neural networkStructure and mathematical notationsActivation functions in NNSigmoid functionHyperbolic tangent ("tanh") functionTraining neural networkEmpirical risk minimizationParameter initializationLoss functionGradientsGradient at the output layerGradient at the Hidden LayerParameter gradientFeed forward and backpropagationHow does it work?RegularizationL2 regularizationL1 regularization
Limitations of neural networks
Vanishing gradients, local optimum, and slow training
Deep learning
Building blocks for deep learningRectified linear activation functionRestricted Boltzmann MachinesDefinition and mathematical notationConditional distributionFree energy in RBMTraining the RBMSampling in RBMContrastive divergenceInputs and outputsHow does it work?Persistent contrastive divergenceAutoencodersDefinition and mathematical notationsLoss functionLimitations of AutoencodersDenoising AutoencoderUnsupervised pre-training and supervised fine-tuningDeep feed-forward NNInput and outputsHow does it work?Deep AutoencodersDeep Belief NetworksInputs and outputsHow does it work?Deep learning with dropoutsDefinition and mathematical notationInputs and outputsHow does it work?Learning Training and testing with dropoutsSparse codingConvolutional Neural NetworkLocal connectivityParameter sharingDiscrete convolutionPooling or subsamplingNormalization using ReLUCNN LayersRecurrent Neural NetworksStructure of Recurrent Neural NetworksLearning and associated problems in RNNsLong Short Term MemoryGated Recurrent Units
Case study
Tools and softwareBusiness problemMachine learning mappingData sampling and transforFeature analysisModels, results, and evaluationBasic data handlingMulti-layer perceptronParameters used for MLPCode for MLPConvolutional NetworkParameters used for ConvNetCode for CNNVariational AutoencoderParameters used for the Variational AutoencoderCode for Variational AutoencoderDBNParameter search using ArbiterResults and analysis
Summary
References
8. Text Mining and Natural Language Processing
NLP, subfields, and tasksText categorizationPart-of-speech tagging (POS tagging)Text clusteringInformation extraction and named entity recognitionSentiment analysis and opinion miningCoreference resolutionWord sense disambiguationMachine translationSemantic reasoning and inferencingText summarizationAutomating question and answers
Issues with mining unstructured data
Text processing components and transformations
Document collection and standardizationInputs and outputsHow does it work?TokenizationInputs and outputsHow does it work?Stop words removalInputs and outputsHow does it work?Stemming or lemmatizationInputs and outputsHow does it work?Local/global dictionary or vocabulary?Feature extraction/generationLexical featuresCharacter-based featuresWord-based featuresPart-of-speech tagging featuresTaxonomy featuresSyntactic featuresSemantic featuresFeature representation and similarityVector space modelBinaryTerm frequency (TF)Inverse document frequency (IDF)Term frequency-inverse document frequency (TF-IDF)Similarity measuresEuclidean distanceCosine distancePairwise-adaptive similarityExtended Jaccard coefficientDice coefficientFeature selection and dimensionality reductionFeature selectionInformation theoretic techniquesStatistical-based techniquesFrequency-based techniquesDimensionality reduction
Topics in text mining
Text categorization/classificationTopic modelingProbabilistic latent semantic analysis (PLSA)Input and outputHow does it work?Advantages and limitationsText clusteringFeature transformation, selection, and reductionClustering techniquesGenerative probabilistic modelsInput and outputHow does it work?Advantages and limitationsDistance-based text clusteringNon-negative matrix factorization (NMF)Input and outputHow does it work?Advantages and limitationsEvaluation of text clusteringNamed entity recognitionHidden Markov models for NERInput and outputHow does it work?Advantages and limitationsMaximum entropy Markov models for NERInput and outputHow does it work?Advantages and limitationsDeep learning and NLP
Tools and usage
MalletKNIMETopic modeling with malletBusiness problemMachine Learning mappingData collectionData sampling and transformationFeature analysis and dimensionality reductionModels, results, and evaluationAnalysis of text processing results
Summary
References
9. Big Data Machine Learning – The Final Frontier
What are the characteristics of Big Data?
Big Data Machine Learning
General Big Data frameworkBig Data cluster deployment frameworksHortonworks Data PlatformCloudera CDHAmazon Elastic MapReduceMicrosoft Azure HDInsightData acquisitionPublish-subscribe frameworksSource-sink frameworksSQL frameworksMessage queueing frameworksCustom frameworksData storageHDFSNoSQLKey-value databasesDocument databasesColumnar databasesGraph databasesData processing and preparationHive and HQLSpark SQLAmazon RedshiftReal-time stream processingMachine LearningVisualization and analysis
Batch Big Data Machine Learning
H2O as Big Data Machine Learning platformH2O architectureMachine learning in H2OTools and usage
Case study
Business problemMachine Learning mappingData collectionData sampling and transformationExperiments, results, and analysisFeature relevance and analysisEvaluation on test dataAnalysis of resultsSpark MLlib as Big Data Machine Learning platformSpark architectureMachine Learning in MLlibTools and usageExperiments, results, and analysisk-Meansk-Means with PCABisecting k-Means (with PCA)Gaussian Mixture ModelRandom ForestAnalysis of resultsReal-time Big Data Machine LearningSAMOA as a real-time Big Data Machine Learning frameworkSAMOA architectureMachine Learning algorithmsTools and usageExperiments, results, and analysisAnalysis of resultsThe future of Machine LearningSummaryReferences
A. Linear Algebra
VectorScalar product of vectors
Matrix
Transpose of a matrixMatrix additionScalar multiplicationMatrix multiplicationProperties of matrix productLinear transformationMatrix inverseEigendecompositionPositive definite matrixSingular value decomposition (SVD)
B. Probability
Axioms of probability
Bayes' theorem
Density estimationMeanVarianceStandard deviationGaussian standard deviationCovarianceCorrelation coefficientBinomial distributionPoisson distributionGaussian distributionCentral limit theoremError propagation
Index

Overview

Mastering Java Machine Learning is your essential guide to incorporating machine learning techniques into your Java programming expertise. Through a mix of theory and practical examples, this book introduces you to advanced machine learning concepts, such as text mining, deep learning, and big data processing, while showcasing how to implement them using Java-based tools.

What this Book will help me do

Understand and utilize key Java libraries for machine learning, enhancing your data processing capabilities.
Implement advanced machine learning techniques such as clustering, anomaly detection, and probabilistic modeling.
Analyze real-world scenarios using case studies to apply machine learning methods effectively.
Develop and optimize machine learning models for both batch and real-time data processing applications.
Gain insights into specialized domains like deep learning and active learning to stay ahead in the field.

Author(s)

Krishna Choppella and None Kamath are seasoned experts in machine learning and Java development with years of experience in both academic research and industry applications. Their hands-on approach emphasizes clarity and applicability, aiming to make complex topics accessible to readers.

Who is it for?

This book is tailored for Java programmers who are looking to delve into the world of machine learning. It is ideal for intermediate-level developers who have foundational knowledge of Java and basic machine learning concepts and are eager to further their expertise by mastering advanced topics and practical applications.

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