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Machine Learning in Java

You're reading from   Machine Learning in Java Helpful techniques to design, build, and deploy powerful machine learning applications in Java

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788474399
Length 300 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Bostjan Kaluza Bostjan Kaluza
Author Profile Icon Bostjan Kaluza
Bostjan Kaluza
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Toc

Table of Contents (13) Chapters Close

Preface 1. Applied Machine Learning Quick Start FREE CHAPTER 2. Java Libraries and Platforms for Machine Learning 3. Basic Algorithms - Classification, Regression, and Clustering 4. Customer Relationship Prediction with Ensembles 5. Affinity Analysis 6. Recommendation Engines with Apache Mahout 7. Fraud and Anomaly Detection 8. Image Recognition with Deeplearning4j 9. Activity Recognition with Mobile Phone Sensors 10. Text Mining with Mallet - Topic Modeling and Spam Detection 11. What Is Next? 12. Other Books You May Enjoy

Basic Algorithms - Classification, Regression, and Clustering

In the previous chapter, we reviewed the key Java libraries that are used for machine learning and what they bring to the table. In this chapter, we will finally get our hands dirty. We will take a closer look at the basic machine learning tasks, such as classification, regression, and clustering. Each of the topics will introduce basic algorithms for classification, regression, and clustering. The example datasets will be small, simple, and easy to understand.

The following topics will be covered in this chapter:

  • Loading data
  • Filtering attributes
  • Building classification, regression, and clustering models
  • Evaluating models
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