Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday 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 (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Basic stream processing and computational techniques


We will now describe some basic computations that can be performed on the stream of data. If we must run summary operations such as aggregations or histograms with limits on memory and speed, we can be sure that some kind of trade-off will be needed. Two well-known types of approximations in these situations are:

  • ϵ Approximation: The computation is close to the exact value within the fraction ϵ of error.

  • (ϵ , δ) Approximation: The computation is close to the exact value within 1 ± ϵ with probability within 1 – δ.

Stream computations

We will illustrate some basic computations and aggregations to highlight the difference between batch and stream-based calculations when we must compute basic operations with constraints on memory and yet consider the entire data:

  • Frequency count or point queries: The generic technique of Count-Min Sketch has been successfully applied to perform various summarizations on the data streams. The primary technique...