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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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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 (13 chapters)
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10
A. Linear Algebra
12
Index

Incremental supervised learning

This section introduces several techniques used to learn from stream data when the true label for each instance is available. In particular, we present linear, non-linear, and ensemble-based algorithms adapted to incremental learning, as well as methods required in the evaluation and validation of these models, keeping in mind that learning is constrained by limits on memory and CPU time.

Modeling techniques

The modeling techniques are divided into linear algorithms, non-linear algorithms, and ensemble methods.

Linear algorithms

The linear methods described here require little to no adaptation to handle stream data.

Online linear models with loss functions

Different loss functions such as hinge, logistic, and squared error can be used in this algorithm.

Inputs and outputs

Only numeric features are used in these methods. The choice of loss function l and learning rate λ at which to apply the weight updates are taken as input parameters. The output is typically...

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