Learn PySpark: build python-based machine learning and deep learning models / By Pramod Singh.

By:

Singh, Pramod

Contributor(s):

Material type: Text

TextPublication details: New York : Apress , 2019.Edition: 1st edDescription: xviii,210p. ; 23 cmISBN:

9781484249604

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

005.76821 1 SINP

LOC classification:

QA76.73.P98

Online resources:

View this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. An e-book available through full-text database.

Contents:

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.

In: Springer eBooksSummary: 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.

List(s) this item appears in: New Arrivals - March 2025

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Item type	Current library	Collection	Call number	Status	Barcode
Book	St Aloysius Institute of Management & Information Technology	MCA	005.76821 SINP (Browse shelf(Opens below))	Available	MCA17276
Book	St Aloysius Institute of Management & Information Technology	MCA	005.76821 SINP (Browse shelf(Opens below))	Available	MCA17056

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Previous	005.76821 RYZS Advanced analytics with spark : patterns for learning from data at scale /	005.76821 SANK Fast data processing with spark 2: learn how to use spark to process big data at speed and scale for sharper analytics put the principles into practice for faster slicker big data projects. Learn how to use spark to process big data at speed and scale for sharper analytics put the principles into practice for faster slicker big data projects.	005.76821 SINP Learn PySpark: build python-based machine learning and deep learning models /	005.76821 SINP Learn PySpark: build python-based machine learning and deep learning models /	005.76821 THOR Apache spark 2 for beginners: develop large scale distributed data processing application using spark 2 in scala and python. Develop large scale distributed data processing application using spark 2 in scala and python.	005.76821 ZECP Spark in action /	005.76821 ZECP Spark in action /	Next

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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.

Mode of access: World Wide Web.

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