Mastering Java machine learning : mastering and implementing advanced techniques in machine learning / Dr. Uday Kamath, Krishna Choppella.

Format:

Book

Author/Creator:

Kamath, Uday, author.

Choppella, Krishna, author.

Language:

English

Subjects (All):

Machine learning.

Java (Computer program language).

Physical Description:

1 online resource (556 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Birmingham, [England] ; Mumbai, [India] : Packt, 2017.

Biography/History:

Kamath Uday: Dr. Uday Kamath is the chief data scientist at BAE Systems Applied Intelligence. He specializes in scalable machine learning and has spent 20 years in the domain of AML, fraud detection in financial crime, cyber security, and bioinformatics, to name a few. Dr. Kamath is responsible for key products in areas focusing on the behavioral, social networking and big data machine learning aspects of analytics at BAE AI. He received his PhD at George Mason University, under the able guidance of Dr. Kenneth De Jong, where his dissertation research focused on machine learning for big data and automated sequence mining. Choppella Krishna: Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.

Summary:

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learningKey Features[*] 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.Book DescriptionJava 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. 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.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.

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.

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

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

Data collection

Data sampling and transformation

Observations on feature analysis and dimensionality reduction

Clustering models, results, and evaluation

Observations and clustering analysis

Outlier models, results, and evaluation

Chapter 4: Semi-Supervised and Active Learning

Semi-supervised learning.

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

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?

Case study in active learning

Data Collection

Models, results, and evaluation

Pool-based scenarios

Stream-based scenarios

Analysis of active learning results

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

Partition based

Hierarchical based and micro clustering

Density based

Grid based.

Validation and evaluation techniques

Unsupervised learning using outlier detection

Partition-based clustering for outlier detection

Inputs and outputs

Distance-based clustering for outlier detection

Validation and evaluation techniques

Case study in stream learning

Clustering experiments

Outlier detection experiments

Analysis of stream learning results

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

Parameterization

Independencies

Conditional random fields

Specialized networks

Tree augmented network

Input and output

Markov chains

Hidden Markov models

Most probable path in HMM

Posterior decoding in HMM

Tools and usage

OpenMarkov.

Weka Bayesian Network GUI

Case study

Feature analysis

Analysis of results

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

Basic data handling

Multi-layer perceptron

Convolutional Network

Variational Autoencoder

DBN

Parameter search using Arbiter

Results and analysis

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.

Document collection and standardization.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed August 11, 2017).

ISBN:

1-78588-855-2

OCLC:

994715866