Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

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.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Data Science Using Java
Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
2
Data Processing Toolbox
Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
3
Exploratory Data Analysis
Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
4
Supervised Learning - Classification and Regression
Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
5
Unsupervised Learning - Clustering and Dimensionality Reduction
Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
6
Working with Text - Natural Language Processing and Information Retrieval
Working with Text - Natural Language Processing and Information Retrieval
Natural Language Processing and information retrieval
Machine learning for texts
Summary
7
Extreme Gradient Boosting
Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
8
Deep Learning with DeepLearning4J
Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
9
Scaling Data Science
Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
10
Deploying Data Science Models
Deploying Data Science Models
Microservices
Online evaluation
Summary

About the Reviewers

Stanislav Bashkyrtsev has been working with Java for the last 9 years. Last years were focused on automation and optimization of development processes.Luca Massaron is a data scientist and a marketing research director specialized in multivariate statistical analysis, machine learning, and customer insight with over a decade of experience in solving real-world problems and in generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten Kaggler, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and nonexperts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential. He is the coauthor of five recently published books and he is just working on the sixth. For Packt Publishing he contributed as an author to Python Data Science Essentials (both 1st and 2nd editions), Regression Analysis with Python, and Large Scale Machine Learning with Python.

You can find him on LinkedIn at https://it.linkedin.com/in/lmassaron.

Prashant Verma started his IT carrier in 2011 as a Java developer in Ericsson working in telecom domain. After a couple of years of JAVA EE experience, he moved into big data domain, and has worked on almost all the popular big data technologies such as Hadoop, Spark, Flume, Mongo, Cassandra, and so on. He has also played with Scala. Currently, he works with QA Infotech as lead data engineer, working on solving e-learning domain problems using  analytics and machine learning.

Prashant has worked for many companies such as Ericsson and QA Infotech, with domain knowledge of telecom and e-learning. Prashant has also been working as a freelance consultant in his free time.

I want to thank Packt Publishing for giving me the chance to review the book as well as my employer and my family for their patience while I was busy working on this book.

 

 

 

 

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