| Description |
1 online resource (556 pages) |
| Contents |
Cover -- Copyright -- Credits -- Foreword -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 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 -- Roles -- Process -- Machine learning -- tools and datasets -- Datasets -- Summary -- Chapter 2: Practical Approach to Real-World Supervised Learning -- Formal description and notation -- Data quality analysis -- Descriptive data analysis -- Basic label analysis -- Basic feature analysis -- Visualization analysis -- Univariate feature analysis -- Multivariate feature analysis -- Data transformation and preprocessing -- Feature construction -- Handling missing values -- Outliers -- Discretization -- Data sampling -- Is sampling needed? -- Undersampling and oversampling -- Training, validation, and test set -- Feature relevance analysis and dimensionality reduction -- Feature search techniques -- Feature evaluation techniques -- Filter approach -- Wrapper approach -- Embedded approach -- Model building -- Linear models -- Linear Regression -- Naïve Bayes -- Logistic Regression -- Non-linear models -- Decision Trees -- K-Nearest Neighbors (KNN) -- Support vector machines (SVM) -- Ensemble learning and meta learners -- Bootstrap aggregating or bagging -- Boosting -- Model assessment, evaluation, and comparisons -- Model assessment -- Model evaluation metrics -- Confusion matrix and related metrics -- ROC and PRC curves -- Gain charts and lift curves -- Model comparisons -- Comparing two algorithms -- Comparing multiple algorithms |
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Case Study -- Horse Colic Classification -- Business problem -- Machine learning mapping -- Data analysis -- Label analysis -- Features analysis -- Supervised learning experiments -- Weka experiments -- RapidMiner experiments -- Results, observations, and analysis -- Summary -- References -- Chapter 3: Unsupervised Machine Learning Techniques -- Issues in common with supervised learning -- Issues specific to unsupervised learning -- Feature analysis and dimensionality reduction -- Notation -- Linear methods -- Principal component analysis (PCA) -- Random projections (RP) -- Multidimensional Scaling (MDS) -- Nonlinear methods -- Kernel Principal Component Analysis (KPCA) -- Manifold learning -- Clustering -- Clustering algorithms -- k-Means -- DBSCAN -- Mean shift -- Expectation maximization (EM) or Gaussian mixture modeling (GMM) -- Hierarchical clustering -- Self-organizing maps (SOM) -- Spectral clustering -- Affinity propagation -- Clustering validation and evaluation -- Internal evaluation measures -- External evaluation measures -- Outlier or anomaly detection -- Outlier algorithms -- Statistical-based -- Distance-based methods -- Density-based methods -- Clustering-based methods -- High-dimensional-based methods -- One-class SVM -- Outlier evaluation techniques -- Supervised evaluation -- Unsupervised evaluation -- Real-world case study -- Tools and software -- Business problem -- Machine learning mapping -- Data collection -- Data quality analysis -- Data sampling and transformation -- Feature analysis and dimensionality reduction -- Observations on feature analysis and dimensionality reduction -- Clustering models, results, and evaluation -- Observations and clustering analysis -- Outlier models, results, and evaluation -- Summary -- References -- Chapter 4: Semi-Supervised and Active Learning -- Semi-supervised learning |
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Representation, notation, and assumptions -- Semi-supervised learning techniques -- Self-training SSL -- Co-training SSL or multi-view SSL -- Cluster and label SSL -- Transductive graph label propagation -- Transductive SVM (TSVM) -- Case study in semi-supervised learning -- Tools and software -- Business problem -- Machine learning mapping -- Data collection -- Data quality analysis -- Data sampling and transformation -- Datasets and analysis -- Experiments and results -- Active learning -- Representation and notation -- Active learning scenarios -- Active learning approaches -- Uncertainty sampling -- Version space sampling -- Query by disagreement (QBD) -- Advantages and limitations -- Data distribution sampling -- How does it work? -- Advantages and limitations -- Case study in active learning -- Tools and software -- Business problem -- Machine learning mapping -- Data Collection -- Data sampling and transformation -- Feature analysis and dimensionality reduction -- Models, results, and evaluation -- Pool-based scenarios -- Stream-based scenarios -- Analysis of active learning results -- Summary -- References -- Chapter 5: Real-Time Stream Machine Learning -- Assumptions and mathematical notations -- Basic stream processing and computational techniques -- Stream computations -- Sliding windows -- Sampling -- Concept drift and drift detection -- Data management -- Partial memory -- Full memory -- Detection methods -- Adaptation methods -- Incremental supervised learning -- Modeling techniques -- Linear algorithms -- Non-linear algorithms -- Ensemble algorithms -- Validation, evaluation, and comparisons in online setting -- Model validation techniques -- Incremental unsupervised learning using clustering -- Modeling techniques -- Partition based -- Hierarchical based and micro clustering -- Density based -- Grid based |
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Validation and evaluation techniques -- Unsupervised learning using outlier detection -- Partition-based clustering for outlier detection -- Inputs and outputs -- How does it work? -- Advantages and limitations -- Distance-based clustering for outlier detection -- Inputs and outputs -- How does it work? -- Validation and evaluation techniques -- Case study in stream learning -- Tools and software -- Business problem -- Machine learning mapping -- Data collection -- Data sampling and transformation -- Feature analysis and dimensionality reduction -- Models, results, and evaluation -- Supervised learning experiments -- Clustering experiments -- Outlier detection experiments -- Analysis of stream learning results -- Summary -- References -- Chapter 6: Probabilistic Graph Modeling -- Probability revisited -- Concepts in probability -- Conditional probability -- Chain rule and Bayes' theorem -- Random variables, joint, and marginal distributions -- Marginal independence and conditional independence -- Factors -- Distribution queries -- Graph concepts -- Graph structure and properties -- Subgraphs and cliques -- Path, trail, and cycles -- Bayesian networks -- Representation -- Definition -- Reasoning patterns -- Independencies, flow of influence, D-Separation, I-Map -- Inference -- Elimination-based inference -- Propagation-based techniques -- Sampling-based techniques -- Learning -- Learning parameters -- Learning structures -- Markov networks and conditional random fields -- Representation -- Parameterization -- Independencies -- Inference -- Learning -- Conditional random fields -- Specialized networks -- Tree augmented network -- Input and output -- How does it work? -- Advantages and limitations -- Markov chains -- Hidden Markov models -- Most probable path in HMM -- Posterior decoding in HMM -- Tools and usage -- OpenMarkov |
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Weka Bayesian Network GUI -- Case study -- Business problem -- Machine learning mapping -- Data sampling and transformation -- Feature analysis -- Models, results, and evaluation -- Analysis of results -- Summary -- References -- Chapter 7: Deep Learning -- Multi-layer feed-forward neural network -- Inputs, neurons, activation function, and mathematical notation -- Multi-layered neural network -- Structure and mathematical notations -- Activation functions in NN -- Training neural network -- Limitations of neural networks -- Vanishing gradients, local optimum, and slow training -- Deep learning -- Building blocks for deep learning -- Rectified linear activation function -- Restricted Boltzmann Machines -- Autoencoders -- Unsupervised pre-training and supervised fine-tuning -- Deep feed-forward NN -- Deep Autoencoders -- Deep Belief Networks -- Deep learning with dropouts -- Sparse coding -- Convolutional Neural Network -- CNN Layers -- Recurrent Neural Networks -- Case study -- Tools and software -- Business problem -- Machine learning mapping -- Data sampling and transformation -- Feature analysis -- Models, results, and evaluation -- Basic data handling -- Multi-layer perceptron -- Convolutional Network -- Variational Autoencoder -- DBN -- Parameter search using Arbiter -- Results and analysis -- Summary -- References -- Chapter 8: Text Mining and Natural Language Processing -- NLP, subfields, and tasks -- Text categorization -- Part-of-speech tagging (POS tagging) -- Text clustering -- Information extraction and named entity recognition -- Sentiment analysis and opinion mining -- Coreference resolution -- Word sense disambiguation -- Machine translation -- Semantic reasoning and inferencing -- Text summarization -- Automating question and answers -- Issues with mining unstructured data -- Text processing components and transformations |
| Summary |
Annotation Become an advanced practitioner with this progressive set of master classes on application-oriented machine learningAbout This Book* Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects* More than 15 open source Java tools in a wide range of techniques, with code and practical usage.* More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis. Who This Book Is ForThis book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practical machine learning. What You Will Learn* Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance.* Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining.* Apply machine learning to real-world data with methodologies, processes, applications, and analysis.* Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning.* Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.* Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on. In DetailJava is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. Style and approachA practical guide to help you explore machine learning-and an array of Java-based tools and frameworks-with the help of practical examples and real-world use cases |
| Notes |
Print version record |
| Subject |
Java (Computer program language)
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Object-oriented programming languages.
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Java (Computer program language)
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Object-oriented programming languages
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| Form |
Electronic book
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| Author |
Choppella, Krishna
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| ISBN |
9781785888557 |
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1785888552 |
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