Practical Machine Learning with Spark: Uncover Apache Spark's Scalable Performance with High-Quality Algorithms Across NLP, Computer Vision and ML(Eng
暫譯: 實用機器學習與 Spark：揭示 Apache Spark 在自然語言處理、計算機視覺和機器學習中的高品質算法與可擴展性能

Explore the cosmic secrets of Distributed Processing for Deep Learning applications

KEY FEATURES

● In-depth practical demonstration of ML/DL concepts using Distributed Framework.

● Covers graphical illustrations and visual explanations for ML/DL pipelines.

● Includes live codebase for each of NLP, computer vision and machine learning applications.

DESCRIPTION

This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark.

The book walks readers by setting up Hadoop and Spark installations on-premises, Docker, and AWS. Readers will learn about Spark MLib and how to utilize it in supervised and unsupervised machine learning scenarios. With the help of Spark, some of the most prominent technologies, such as natural language processing and computer vision, are evaluated and demonstrated in a realistic setting. Using the capabilities of Apache Spark, this book discusses the fundamental components that underlie each of these natural language processing, computer vision, and machine learning technologies, as well as how you can incorporate these technologies into your business processes.

WHAT YOU WILL LEARN

●Learn how to get started with machine learning projects using Spark.

●Witness how to use Spark MLib's design for machine learning and deep learning operations.

●Use Spark in tasks involving NLP, unsupervised learning, and computer vision.

●Experiment with Spark in a cloud environment and with AI pipeline workflows.

WHO THIS BOOK IS FOR

This book is valuable for data engineers, machine learning engineers, data scientists, data architects, business analysts, and technical consultants worldwide. It would be beneficial to have some familiarity with the fundamentals of Hadoop and Python.