Applied Data Science Using PySpark: Learn the End-to-End Predictive Model-Buildi

Description: Applied Data Science Using PySpark by Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla 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 LearnBuild an end-to-end predictive modelImplement multiple variable selection techniquesOperationalize modelsMaster multiple algorithms and implementations Who This Book is ForData scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streamingdata. FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover 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 Author Biography Ramcharan Kakarla is currently lead data scientist at Comcast residing in Philadelphia. He is a passionate data science and artificial intelligence advocate with five+ years of experience. He holds a masters degree from Oklahoma State University with specialization in data mining. Prior to OSU, he received his bachelors in electrical and electronics engineering from Sastra University in India. He was born and raised in the coastal town of Kakinada, India. He started his career working as a performance engineer with several Fortune 500 clients including State Farm and British Airways. In his current role he is focused on building data science solutions and frameworks leveraging big data. He has published several papers and posters in the field of predictive analytics. He served as SAS Global Ambassador for the year 2015.Sundar Krishnan is passionate about artificial intelligence and data science with more than five years of industrial experience. He has tremendous experience in building and deploying customer analytics models and designing machine learning workflow automation. Currently, he is associated with Comcast as a lead data scientist. Sundar was born and raised in Tamil Nadu, India and has a bachelors degree from Government College of Technology, Coimbatore. He completed his masters at Oklahoma State University, Stillwater. In his spare time, he blogs about his data science works on Medium. Table of Contents 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. Feature 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 Details ISBN1484264991 Author Sridhar Alla Short Title Applied Data Science Using Pyspark Language English ISBN-10 1484264991 ISBN-13 9781484264997 Format Paperback DOI 10.1007/978-1-4842-6500-0 Pages 410 Year 2020 Publication Date 2020-12-18 Publisher APress Edition 1st Imprint APress Place of Publication Berkley Country of Publication United States AU Release Date 2020-12-18 NZ Release Date 2020-12-18 US Release Date 2020-12-18 UK Release Date 2020-12-18 Illustrations 190 Illustrations, black and white; XXVI, 410 p. 190 illus. Subtitle Learn the End-to-End Predictive Model-Building Cycle Edition Description 1st ed. Alternative 9781484283738 DEWEY 005.7 Audience Professional & Vocational Replaced by 9798868808197 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:134245757;

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