Learn PySpark: Build Python-based Machine Learning and Deep Learning Models
(eBook)

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Published
Apress|Apress, 2019.
Format
eBook
Language
English
ISBN
9781484249611

Citations

APA Citation, 7th Edition (style guide)

Pramod Singh., & Pramod Singh|AUTHOR. (2019). Learn PySpark: Build Python-based Machine Learning and Deep Learning Models . Apress|Apress.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Pramod Singh and Pramod Singh|AUTHOR. 2019. Learn PySpark: Build Python-based Machine Learning and Deep Learning Models. Apress|Apress.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Pramod Singh and Pramod Singh|AUTHOR. Learn PySpark: Build Python-based Machine Learning and Deep Learning Models Apress|Apress, 2019.

MLA Citation, 9th Edition (style guide)

Pramod Singh, and Pramod Singh|AUTHOR. Learn PySpark: Build Python-based Machine Learning and Deep Learning Models Apress|Apress, 2019.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID6e7b75bc-1bf2-2538-2fe5-7fe4ee3d46d2-eng
Full titlelearn pyspark build python based machine learning and deep learning models
Authorsingh pramod
Grouping Categorybook
Last Update2023-01-06 13:45:25PM
Last Indexed2024-05-16 20:56:20PM

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Image Sourcehoopla
First LoadedJun 20, 2023
Last UsedApr 12, 2024

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    [synopsis] => 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.
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