In our interconnected world, graphs are omnipresent. The World Wide Web (WWW) is just one example of a complex structure that we can consider a graph, in which web pages represent entities that are connected by incoming and outgoing links between them. In Facebook’s social graph, many millions of users form a network, connecting friends around the globe. Many other important structures that we see and can collect data for today come equipped with a natural graph structure; that is, they can, at a very basic level, be understood as a collection of vertices that are connected to each other in a certain way by what we call edges. Stated in this generality, this observation reflects how ubiquitous graphs are. What makes it valuable is that the graphs are well-studied structures and that there are many algorithms available that allow us to gain important...
Mastering Machine Learning with Spark 2.x
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Mastering Machine Learning with Spark 2.x
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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.
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Current Title:
Mastering Machine Learning with Spark 2.x
Table of Contents (9 chapters)
Preface
Free Chapter
Introduction to Large-Scale Machine Learning and Spark
Detecting Dark Matter - The Higgs-Boson Particle
Ensemble Methods for Multi-Class Classification
Predicting Movie Reviews Using NLP and Spark Streaming
Word2vec for Prediction and Clustering
Extracting Patterns from Clickstream Data
Graph Analytics with GraphX
Lending Club Loan Prediction
Customer Reviews