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.