Author Description

Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorialAbout This Book• Process and analyze big data in a distributed and scalable way• Write sophisticated Spark pipelines that incorporate elaborate extraction• Build and use regression models to predict flight delays Who This Book Is ForAre you a developer with a background in machine learning and statistics who is feeling limited by the current slow and "small data" machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.What You Will Learn• Use Spark streams to cluster tweets online• Run the PageRank algorithm to compute user influence• Perform complex manipulation of DataFrames using Spark• Define Spark pipelines to compose individual data transformations• Utilize generated models for off-line/on-line prediction• Transfer the learning from an ensemble to a simpler Neural Network• Understand basic graph properties and important graph operations• Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language• Use K-means algorithm to cluster movie reviews datasetIn DetailThe 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 learni