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Applied data science using Pyspark =...

Krishnan, Sundar.

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Applied data science using Pyspark = learn the end-to-end predictive model-building cycle /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied data science using Pyspark/ by Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla.
Reminder of title:	learn the end-to-end predictive model-building cycle /
Author:	Kakarla, Ramcharan.
other author:	Krishnan, Sundar.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xxvi, 410 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Setting up the Pyspark Environment -- Chapter 2: Basic Statistics and Visualizations -- Chapter 3: :Variable Selection -- Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques -- Chapter 5: Model Validation and selecting the best model -- Chapter 6: Unsupervised and recommendation algorithms -- Chapter 7:End to end modeling pipelines -- Chapter 8: Productionalizing a machine learning model -- Chapter 9: Experimentations -- Chapter 10:Other Tips: Optional.
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-1-4842-6500-0
ISBN:	9781484265000

Applied data science using Pyspark = learn the end-to-end predictive model-building cycle /
Kakarla, Ramcharan.

Applied data science using Pysparklearn the end-to-end predictive model-building cycle /[electronic resource] :by Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla. - Berkeley, CA :Apress :2021. - xxvi, 410 p. :ill., digital ;24 cm.

Chapter 1: Setting up the Pyspark Environment -- Chapter 2: Basic Statistics and Visualizations -- Chapter 3: :Variable Selection -- Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques -- Chapter 5: Model Validation and selecting the best model -- Chapter 6: Unsupervised and recommendation algorithms -- Chapter 7:End to end modeling pipelines -- Chapter 8: Productionalizing a machine learning model -- Chapter 9: Experimentations -- Chapter 10:Other Tips: Optional.

Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. You will: Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations.

ISBN: 9781484265000

Standard No.: 10.1007/978-1-4842-6500-0doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Applied data science using Pyspark = learn the end-to-end predictive model-building cycle /
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505 0 $a Chapter 1: Setting up the Pyspark Environment -- Chapter 2: Basic Statistics and Visualizations -- Chapter 3: :Variable Selection -- Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques -- Chapter 5: Model Validation and selecting the best model -- Chapter 6: Unsupervised and recommendation algorithms -- Chapter 7:End to end modeling pipelines -- Chapter 8: Productionalizing a machine learning model -- Chapter 9: Experimentations -- Chapter 10:Other Tips: Optional.
520 $a Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. You will: Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations.
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