Applied Data Science Using PySpark: Learn the End-to-End Predictive Model-Building Cycle Paperback – December 18,2020
by: Ramcharan Kakarla ,Sundar Krishnan (Contributor),Sridhar Alla (Contributor)
Publisher finelybook 出版社: Apress; 1st ed. edition (December 18,2020)
Language 语言: English
Print Length 页数: 436 pages
ISBN-10: 1484264991
ISBN-13: 9781484264997
Book Description
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.
What You Will Learn
Build an end-to-end predictive model
Implement multiple variable selection techniques
Operationalize models
Master multiple algorithms and implementations
