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Machine Learning: End-to-End guide for Java developers

You're reading from   Machine Learning: End-to-End guide for Java developers Data Analysis, Machine Learning, and Neural Networks simplified

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Product type Course
Published in Oct 2017
Publisher Packt
ISBN-13 9781788622219
Length 1159 pages
Edition 1st Edition
Languages
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Authors (2):
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Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
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Toc

What this learning path covers

Module 1, Java for Data Science, investigates the support provided for low-level math operations and how they can be supported in a multiple processor environment. Data analysis, at its heart, necessitates the ability to manipulate and analyze large quantities of numeric data.

Module 2, Machine Learning in Java, reviews the various Java libraries and platforms dedicated to machine learning, what each library brings to the table, and what kind of problems it is able to solve. The review includes Weka, Java-ML, Apache Mahout, Apache Spark, deeplearning4j, and Mallet.

Module 3, Mastering Java Machine Learning, presents many advanced methods in clustering and outlier techniques, with applications. Topics covered are feature selection and reduction in unsupervised data, clustering algorithms, evaluation methods in clustering, and anomaly detection using statistical, distance, and distribution techniques. At the end of the chapter, we perform a case study for both clustering and outlier detection using a real-world image dataset, MNIST. We use the Smile API to do feature reduction and ELKI for learning.

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