Apress | Applied Data Science Using PySpark: Learn The End-To-End Predictive Model-Building Cycle (2021 EN)

Author: Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla
Full Title: Applied Data Science Using PySpark: Learn The End-To-End Predictive Model-Building Cycle
Publisher: Apress; 1st ed. edition (January 3, 2021)
Year: 2021
ISBN-13: 9781484265000 (978-1-4842-6500-0), 9781484264997 (978-1-4842-6499-7)
ISBN-10: 1484265009, 1484264991
Pages: 410
Language: English
Genre: Educational: Machine Learning
File type: EPUB (True), PDF (True)
Quality: 10/10
Price: 40.65 €


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.


Learn:
✓ Build an end-to-end predictive model
✓ Implement multiple variable selection techniques
✓ Operationalize models
✓ Master multiple algorithms and implementations

Features:
✓ Covers industry-standard methods and procedures all implemented with examples
✓ Includes how to transition data science solutions from traditional languages to PySpark
✓ Includes handpicked tips and tricks that can help in your day-to-day work

Who This Book Is For:
Data scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data.

-------------
Our members see more. Join us!