Machine Learning with Spark  (English, Paperback, Nick Pentreath)

About This BookA practical tutorial with real-world use cases allowing you to develop your own machine learning systems with SparkCombine various techniques and models into an intelligent machine learning systemUse Spark’s powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.What You Will LearnCreate your first Spark program in Scala, Java, and PythonSet up and configure a development environment for Spark on your own computer, as well as on Amazon EC2Access public machine learning datasets and use Spark to load, process, clean, and transform dataUse Spark's machine learning library to implement programs utilizing well-known machine learning models including collaborative filtering, classification, regression, clustering, and dimensionality reductionWrite Spark functions to evaluate the performance of your machine learning modelsDeal with large-scale text data, including feature extraction and using text data as input to your machine learning modelsExplore online learning methods and use Spark Streaming for online learning and model evaluationIn DetailApache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design.This book guides you through the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction. You will cover advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming.About the AuthorsNick Pentreath has a background in financial markets, machine learning, and software development. He has worked at Goldman Sachs Group, Inc.; as a research scientist at the online ad targeting start-up Cognitive Match Limited, London; and led the Data Science and Analytics team at Mxit, Africa's largest social network.He is a cofounder of Graphflow, a big data and machine learning company focused on user-centric recommendations and customer intelligence. He is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add value to the bottom line.Nick is a member of the Apache Spark Project Management Committee.