Book description
Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning
About 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 For
This 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 Detail
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.
Style and approach
A 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.
Table of contents
-
Mastering Java Machine Learning
- Table of Contents
- Mastering Java Machine Learning
- Credits
- Foreword
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Preface
-
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
-
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
-
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
-
4. Semi-Supervised and Active Learning
- Semi-supervised learning
- Active learning
- Case study in active learning
- Summary
- References
-
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
-
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
-
7. Deep Learning
- Multi-layer feed-forward neural network
- Limitations of neural networks
-
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
-
Building blocks for deep learning
- Case study
- Summary
- References
-
8. Text Mining and Natural Language Processing
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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
- Topics in text mining
- Tools and usage
- Summary
- References
-
NLP, subfields, and tasks
-
9. Big Data Machine Learning – The Final Frontier
- What are the characteristics of Big Data?
- Big Data Machine Learning
- Batch Big Data Machine Learning
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Case study
- Business problem
- Machine Learning mapping
- Data collection
- Data sampling and transformation
- Spark MLlib as Big Data Machine Learning platform
- A. Linear Algebra
- B. Probability
- Index
Product information
- Title: Mastering Java Machine Learning
- Author(s):
- Release date: July 2017
- Publisher(s): Packt Publishing
- ISBN: 9781785880513
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