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 Machine Learning
  • Table Of Contents Toc
  • Feedback & Rating feedback
Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
close
close
Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
close
close
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Case study


In this section, we will perform a case study with real-world machine learning datasets to illustrate some of the concepts from Bayesian networks.

We will use the UCI Adult dataset, also known as the Census Income dataset (http://archive.ics.uci.edu/ml/datasets/Census+Income). This dataset was extracted from the United States Census Bureau's 1994 census data. The donors of the data is Ronny Kohavi and Barry Becker, who were with Silicon Graphics at the time. The dataset consists of 48,842 instances with 14 attributes, with a mix of categorical and continuous types. The target class is binary.

Business problem

The problem consists of predicting the income of members of a population based on census data, specifically, whether their income is greater than $50,000.

Machine learning mapping

This is a problem of classification and this time around we will be training Bayesian graph networks to develop predictive models. We will be using linear, non-linear, and ensemble algorithms, as we...

Limited Time Offer

$10p/m for 3 months

Get online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech and supported with AI assistants
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

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

Submit Your Feedback

Modal Close icon

Create a Note

Modal Close icon
You need to login to use this feature.

Delete Bookmark

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

Delete Note

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

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note