Machine Learning with Spark Second Edition
Rajdeep Dua, Manpreet Singh Ghotra, Nick Pentreath
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- Get to the grips with the latest version of Apache Spark
- Utilize Spark's machine learning library to implement predictive analytics
- Leverage Spark's powerful tools to load, analyze, clean, and transform your data
Spark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression.
This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.
Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.
What you will learn
- Get hands-on with the latest version of Spark ML
- Create your first Spark program with Scala and Python
- Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2
- Access public machine learning datasets and use Spark to load, process, clean, and transform data
- Use Spark's machine learning library to implement programs by utilizing well-known machine learning models
- Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models
- Write Spark functions to evaluate the performance of your machine learning models