Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Chapter 3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

In this chapter, we will cover the following recipes:

  • Creating RDDs with Spark 2.0 using internal data sources
  • Creating RDDs with Spark 2.0 using external data sources
  • Transforming RDDs with Spark 2.0 using the filter() API
  • Transforming RDDs with the super useful flatMap() API
  • Transforming RDDs with set operation APIs
  • RDD transformation/aggregation with groupBy() and reduceByKey()
  • Transforming RDDs with the zip() API
  • Join transformation with paired key-value RDDs
  • Reduce and grouping transformation with paired key-value RDDs
  • Creating DataFrames from Scala data structures
  • Operating on DataFrames programmatically without SQL
  • Loading DataFrames and setup from an external source
  • Using DataFrames with standard SQL language - SparkSQL
  • Working with the Dataset API using a Scala sequence
  • Creating and using Datasets from RDDs and back again
  • Working with JSON using the Dataset API and SQL together
  • Functional programming with the Dataset...