Mastering Java for Data Science

Use Java to create a diverse range of Data Science applications and bring Data Science into production
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Mastering Java for Data Science

Alexey Grigorev

Use Java to create a diverse range of Data Science applications and bring Data Science into production
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Book Details

ISBN 139781782174271
Paperback364 pages

Book Description

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises.

Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort.

This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data.

Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.

Table of Contents

Chapter 1: Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
Chapter 2: Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
Chapter 3: Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
Chapter 4: Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
Chapter 5: Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
Chapter 6: Working with Text - Natural Language Processing and Information Retrieval
Natural Language Processing and information retrieval
Machine learning for texts
Summary
Chapter 7: Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
Chapter 8: Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
Chapter 9: Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
Chapter 10: Deploying Data Science Models
Microservices
Online evaluation
Summary

What You Will Learn

  • Get a solid understanding of the data processing toolbox available in Java
  • Explore the Data Science ecosystem available in Java
  • Find out how to approach different Machine Learning problems with Java
  • Process unstructured information such as natural language text or images
  • Create your own search engine
  • Get state-of-the-art performance with XGBoost
  • Learn how to build deep neural networks with DeepLearning4j
  • Build applications that scale and process large amounts of data
  • Deploy data science models to production and evaluate their performance

Authors

Table of Contents

Chapter 1: Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
Chapter 2: Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
Chapter 3: Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
Chapter 4: Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
Chapter 5: Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
Chapter 6: Working with Text - Natural Language Processing and Information Retrieval
Natural Language Processing and information retrieval
Machine learning for texts
Summary
Chapter 7: Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
Chapter 8: Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
Chapter 9: Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
Chapter 10: Deploying Data Science Models
Microservices
Online evaluation
Summary

Book Details

ISBN 139781782174271
Paperback364 pages
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