build python-based machine learning and deep learning models Singh, Pramod. creator SpringerLink (Online service) O'Reilly (Firm) Serials Solutions text Electronic books. xxu New York Apress 2019 1st ed. monographic eng xviii,210p. ; 23 cm. Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark's core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github. Chapter 1: Introduction to PySpark -- Chapter 2: Data Processing -- Chapter 3: Spark Structured Streaming -- Chapter 4: Airflow -- Chapter 5: Machine Learning Library (MLlib) -- Chapter 6: Supervised Machine Learning -- Chapter 7: Unsupervised Machine Learning -- Chapter 8: Deep Learning Using PySpark. By Pramod Singh. Requires an SPL library card. Mode of access: World Wide Web. Python (Computer program language) Big data Machine learning Open source software Computer programming QA76.73.P98 005.76821 SINP 9781484249604 https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484249611/?ar https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484249611/?ar Requires an SPL library card. 190906 20250110114257.0 ssj0002239821