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.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback


In this chapter, we learned about Extreme Gradient Boosting --an implementation of Gradient Boosting Machines. We learned how to install the library and then we applied to solve a variety of supervised learning problems: classification, regression, and ranking. 

XGBoost shines when the data is structured: when it is possible to extract good features from our data and put these features into a tabular format. However, in some cases, the data is quite hard to structure. For example, when dealing with images or sounds, a lot of effort is needed to extract useful features. But we do not necessarily have to do the feature extraction ourselves, instead, we can use Neural Network models which can learn the best features themselves. 

In the next chapter, we will look at deeplearning4j--a deep learning library for Java.