Learn PySpark: Build Python-based Machine Learning and Deep Learning Models - Tapa blanda

Singh, Pramod

 
9781484249604: Learn PySpark: Build Python-based Machine Learning and Deep Learning Models
Tapa blanda
ISBN 10: 1484249607 ISBN 13: 9781484249604
Editorial: Apress, 2019

Sinopsis

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges.


You’ll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. 

You’ll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.

What You’ll Learn
  • Develop pipelines for streaming data processing using PySpark 
  • Build Machine Learning & Deep Learning models using PySpark latest offerings
  • Use graph analytics using PySpark 
  • Create Sequence Embeddings from Text data 
Who This Book is For 

Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Pramod Singh is currently a Manager (Data Science) at Publicis Sapient and working as data science lead for a project with Mercedes Benz. He has spent the last nine years working on multiple Data projects at SapientRazorfish, Infosys & Tally and has used traditional to advanced machine learning and deep learning techniques in multiple projects using R, Python, Spark and Tensorflow. Pramod has also been a regular speaker at major conferences in India and abroad and is currently authoring a couple of books on Deep Learning and AI techniques. He regularly conducts Data Science meetups at SapientRazorfish and presents webinars on Machine Learning and Artificial Intelligence. He lives in Bangalore with his wife and 2-year-old son. In his spare time, he enjoys coding, reading and watching football.

De la contraportada

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges.


You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. 

You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Apress
Año de publicación
2019
Idioma
Inglés
ISBN 10 
1484249607
ISBN 13 
9781484249604
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
228
Contacto del fabricante
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3,69115 Heidelberg, Germany
Heidelberg
69115
Alemania

Otras ediciones populares con el mismo título

9781484249628: Learn PySpark: Build Python-based Machine Learning and Deep Learning Models

Edición Destacada

ISBN 10:  1484249623 ISBN 13:  9781484249628
Editorial: Apress, 2019
Tapa blanda