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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

Machine learning in the cloud

Setting up a complete machine learning stack that is able to scale with the increasing amount of data could be challenging. A recent wave of the Software as a Service (SaaS) and Infrastructure as a Service (IaaS) paradigm has spilled over to the machine learning domain as well. The trend today is to move the actual data preprocessing, modeling, and prediction to cloud environments and focus on modeling tasks only.

In this section, we'll review some of the promising services offering algorithms, predictive models already train in specific domain, and environments empowering collaborative workflows in data science teams.

Machine learning as a service

The first category is algorithms as a service...

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