Mastering Java Machine Learning

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning
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Mastering Java Machine Learning

Dr. Uday Kamath, Krishna Choppella

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning
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Book Details

ISBN 139781785880513
Paperback556 pages

Book Description

Java 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.

Table of Contents

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
Machine learning – tools and datasets
Summary
Chapter 2: Practical Approach to Real-World Supervised Learning
Formal description and notation
Data transformation and preprocessing
Feature relevance analysis and dimensionality reduction
Model building
Model assessment, evaluation, and comparisons
Case Study – Horse Colic Classification
Summary
References
Chapter 3: Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
Clustering
Outlier or anomaly detection
Real-world case study
Summary
References
Chapter 4: Semi-Supervised and Active Learning
Semi-supervised learning
Active learning
Case study in active learning
Summary
References
Chapter 5: Real-Time Stream Machine Learning
Assumptions and mathematical notations
Basic stream processing and computational techniques
Concept drift and drift detection
Incremental supervised learning
Incremental unsupervised learning using clustering
Unsupervised learning using outlier detection
Case study in stream learning
Summary
References
Chapter 6: Probabilistic Graph Modeling
Probability revisited
Graph concepts
Bayesian networks
Markov networks and conditional random fields
Specialized networks
Tools and usage
Case study
Summary
References
Chapter 7: Deep Learning
Multi-layer feed-forward neural network
Limitations of neural networks
Deep learning
Case study
Summary
References
Chapter 8: Text Mining and Natural Language Processing
NLP, subfields, and tasks
Issues with mining unstructured data
Text processing components and transformations
Topics in text mining
Tools and usage
Summary
References
Chapter 9: Big Data Machine Learning – The Final Frontier
What are the characteristics of Big Data?
Big Data Machine Learning
Batch Big Data Machine Learning
Case study

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.

Authors

Table of Contents

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
Machine learning – tools and datasets
Summary
Chapter 2: Practical Approach to Real-World Supervised Learning
Formal description and notation
Data transformation and preprocessing
Feature relevance analysis and dimensionality reduction
Model building
Model assessment, evaluation, and comparisons
Case Study – Horse Colic Classification
Summary
References
Chapter 3: Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
Clustering
Outlier or anomaly detection
Real-world case study
Summary
References
Chapter 4: Semi-Supervised and Active Learning
Semi-supervised learning
Active learning
Case study in active learning
Summary
References
Chapter 5: Real-Time Stream Machine Learning
Assumptions and mathematical notations
Basic stream processing and computational techniques
Concept drift and drift detection
Incremental supervised learning
Incremental unsupervised learning using clustering
Unsupervised learning using outlier detection
Case study in stream learning
Summary
References
Chapter 6: Probabilistic Graph Modeling
Probability revisited
Graph concepts
Bayesian networks
Markov networks and conditional random fields
Specialized networks
Tools and usage
Case study
Summary
References
Chapter 7: Deep Learning
Multi-layer feed-forward neural network
Limitations of neural networks
Deep learning
Case study
Summary
References
Chapter 8: Text Mining and Natural Language Processing
NLP, subfields, and tasks
Issues with mining unstructured data
Text processing components and transformations
Topics in text mining
Tools and usage
Summary
References
Chapter 9: Big Data Machine Learning – The Final Frontier
What are the characteristics of Big Data?
Big Data Machine Learning
Batch Big Data Machine Learning
Case study

Book Details

ISBN 139781785880513
Paperback556 pages
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