
Learn PySpark
Build Python-based Machine Learning and Deep Learning Models
Pramod Singh(Author)
APress
Published on 7. September 2019
Book
Paperback/Softback
XVIII, 210 pages
978-1-4842-4960-4 (ISBN)
Description
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
Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.
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
Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.
More details
Edition
First Edition
Language
English
Place of publication
Berkeley
United States
Target group
Professional and scholarly
Illustrations
32 farbige Abbildungen, 155 s/w Abbildungen
XVIII, 210 p. 187 illus., 32 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 13 mm
Weight
353 gr
ISBN-13
978-1-4842-4960-4 (9781484249604)
DOI
10.1007/978-1-4842-4961-1
Schweitzer Classification
Other editions
Additional editions

E-Book
09/2019
APress
€56.99
Available for download
Person
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.
Content
Chapter 1: Introduction to PySpark.- Chapter 2: Data Processing.- Chapter 3: Spark Structured Streaming.- Chapter 4: Airflow.- Chapter 5: Machine Learning Library (MLlib).- Chapter 6: Supervised Machine Learning.- Chapter 7: Unsupervised Machine Learning.- Chapter 8: Deep Learning Using PySpark.