Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Other uses of unstructured data


The personalization and device diagnostic obviously are not the only uses of unstructured data. The preceding case is a good example as we started from structured record and quickly converged on the need to construct an unstructured data structure to simplify the analysis.

In fact, there are many more unstructured data than there are structured; it is just the convenience of having the flat structure for the traditional statistical analysis that makes us to present the data as a set of records. Text, images, and music are the examples of semi-structured data.

One example of non-structured data is denormalized data. Traditionally the record data are normalized mostly for performance reasons as the RDBMSs have been optimized to work with structured data. This leads to foreign key and lookup tables, but these are very hard to maintain if the dimensions change. Denormalized data does not have this problem as the lookup table can be stored with each record—it is...