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

Search engine - preparing data

In the first chapter, we introduced the running example, building a search engine. A search engine is a program that, given a query from the user, returns results ordered by relevance with respect to the query. In this chapter, we will perform the first steps--obtaining and processing data.

Suppose we are working on a web portal where users generate a lot of content, but they have trouble finding what other people have created. To overcome this problem, we propose to build a search engine, and product management has identified the typical queries that the users will put in.

For example, "Chinese food", "homemade pizza", and "how to learn programming" are typical queries from this list.

Now we need to collect the data. Luckily for us, there are already search engines on the Internet that can take in a query and return a list of URLs they consider relevant. We can use them for obtaining the data. You probably already know such engines--Google or Bing, to name just...