Book Image

Mastering Machine Learning with Spark 2.x

By : Michal Malohlava, Alex Tellez, Max Pumperla
Book Image

Mastering Machine Learning with Spark 2.x

By: Michal Malohlava, Alex Tellez, Max Pumperla

Overview of this book

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Table of Contents (9 chapters)
3
Ensemble Methods for Multi-Class Classification

Predicting Movie Reviews Using NLP and Spark Streaming

In this chapter, we will take an in-depth look at the field of Natural Language Processing (NLP), not to be confused with Neuro-Linguistic Programming! NLP helps analyze raw textual data and extract useful information such as sentence structure, sentiment of text, or even translation of text between languages. Since many sources of data contain raw text, (for example, reviews, news articles, and medical records). NLP is getting more and more popular, thanks to providing an insight into the text and helps make automatized decisions easier.

Under the hood, NLP is often using machine-learning algorithms to extract and model the structure of text. The power of NLP is much more visible if it is applied in the context of another machine method, where, for example, text can represent one of the input features.

In this chapter, we...