Machine Learning with Spark - Tackle Big Data with Powerful Spark Machine Learning Algorithms

by Nick Pentreath

Key FeaturesFollow real-world examples to learn how to develop your own machine learning systems with SparkA 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 systemExplore and use Spark's powerful range of features to load, analyze, clean, and your dataBook DescriptionApache 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.

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 evaluation About the AuthorNick Pentreath is a member of the Apache Spark Project Management Committee. He has has a background in financial markets, machine learning, and software development, including experience as a research scientist at the online ad targeting start-up Cognitive Match Limited in London and leading the Data Science and Analytics team at Mxit, Africa's largest social network. He is also one of the cofounders of Graphflow, a big data and machine learning company focused on user-centric recommendations and customer intelligence.

Table of Contents Getting Up and Running with SparkDesigning a Machine Learning SystemObtaining, Processing and Preparing Data with SparkBuilding a Recommendation Engine with SparkBuilding a Classification Model with SparkBuilding a Regression Model with SparkBuilding a Clustering Model with SparkDimensionality Reduction with SparkAdvanced Text Processing with SparkReal-Time Machine Learning with Spark Streaming

Genres: Programming

340 pages, Kindle Edition

First published February 20, 2015

Reviews

[Alex Ott · Rating 4 out of 5]

Good content on the use of Spark for ML, but take into account that this book is slightly outdated as it covers Spark 1.2

[Jared Ramsey · Rating 5 out of 5]

This is a great book. It provides a great introduction to machine learning with Spark and is very easy to follow.

[TN · Rating 3 out of 5]

I found a bunch of useful ideas on machine learning and NLP. However, the mixture between Python, Scala and Java in the examples wasn't the best experience.