T�tulo: Apache Spark 2.x Machine Learning Cookbook: Over 100 recipes to simplify machine learning model implementations with Spark
Editor: Packt Publishing
Fecha de publicaci�n: 2017
Idioma: Ingl�s
ISBN 10: 1783551607
ISBN 13: 9781783551606
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

Simplify machine learning model implementations with Spark

About This Book

Solve the day-to-day problems of data science with Spark
This unique cookbook consists of exciting and intuitive numerical recipes
Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data

Who This Book Is For

This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem.

What You Will Learn

Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark
Build a recommendation engine that scales with Spark
Find out how to build unsupervised clustering systems to classify data in Spark
Build machine learning systems with the Decision Tree and Ensemble models in Spark
Deal with the curse of high-dimensionality in big data using Spark
Implement Text analytics for Search Engines in Spark
Streaming Machine Learning System implementation using Spark

In Detail

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.

Style and approach

This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.

Rese�a del editor

Simplify machine learning model implementations with Spark About This Book * Solve the day-to-day problems of data science with Spark * This unique cookbook consists of exciting and intuitive numerical recipes * Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn * Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark * Build a recommendation engine that scales with Spark * Find out how to build unsupervised clustering systems to classify data in Spark * Build machine learning systems with the Decision Tree and Ensemble models in Spark * Deal with the curse of high-dimensionality in big data using Spark * Implement Text analytics for Search Engines in Spark * Streaming Machine Learning System implementation using Spark In Detail 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. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.

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