Learn PySpark

Build Python-based Machine Learning and Deep Learning Models

By: Pramod Singh

Write A Review

Paperback | 7 September 2019

At a Glance

Paperback
230 Pages

Dimensions(cm)
24.4 x 23.6 x 1.0

Paperback

$74.99

or 4 interest-free payments of $18.75 with

Ships in 5 to 7 business days

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.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.