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Applied Data Science Using PySpark - Learn the End-to-End Predictive Model-Building Cycle (Paperback, 1st ed.)
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Applied Data Science Using PySpark - Learn the End-to-End Predictive Model-Building Cycle (Paperback, 1st ed.)
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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 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.
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