Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering Java for Data Science
  • Table Of Contents Toc
  • Feedback & Rating feedback
Mastering Java for Data Science

Mastering Java for Data Science

By : Alexey Grigorev
5 (1)
close
close
Mastering Java for Data Science

Mastering Java for Data Science

5 (1)
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 (11 chapters)
close
close

Classification


In machine learning, the classification problems deal with discrete targets with a finite set of possible values. What this means is that there is a set of possible outcomes, and given some features we want to predict the outcome. 

The binary classification is the most common type of classification problem, as the target variable can have only two possible values, such as True/False, Relevant/Not Relevant, Duplicate/Not Duplicate, Cat/Dog, and so on.

Sometimes the target variable can have more than two outcomes, for example, colors, category of an item, model of a car, and so on, and we call this multi-class classification. Typically, each observation can only have one label, but in some settings an observation can be assigned several values. Multi-class classification can be converted to a set of binary classification problems, which is why we will mostly concentrate on binary classification.

Binary classification models

As we have already discussed, the binary classification...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY