Apache Spark 2.x Machine Learning Cookbook
Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Offered by
Difficulty Level
Intermediate
Completion Time
22h12m
Language
English
About Book
Who Is This Book For?
This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem.
Apache Spark 2.x Machine Learning Cookbook
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 22h12m total length
Practical Machine Learning with Spark using Scala
Just enough Linear Algebra for Machine Learning with Spark
Spark’s three data musketeers for machine learning – Perfect Together
Common Recipes for Implementing a Robust Machine Learning System
Practical Machine Learning with Regression and Classification in Spark 2.0 – Part I
Practical Machine Learning with Regression and Classification in Spark 2.0 – Part II
Recommendation engine that scales with Spark
Unsupervised Clustering with Apache Spark 2.0
Optimization – Going Down the Hill with the Gradient Descent
Build Machine Learning Systems with Decision Tree and Ensemble Models
Curse of high-dimensionality in Big Data
Implementing Text Analytics with Spark 2.0 ML Library
Spark Streaming and Machine Learning Library
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