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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Apache Spark 2.x Machine Learning Cookbook (Paperback)
Loot Price: R1,424
Discovery Miles 14 240
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Apache Spark 2.x Machine Learning Cookbook (Paperback)
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Simplify machine learning model implementations with Spark About
This Book * Solve the day-to-day problems of data science with
Spark * This unique cookbook consists of exciting and intuitive
numerical recipes * Optimize your work by acquiring, cleaning,
analyzing, predicting, and visualizing your data Who This Book Is
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. What You
Will Learn * Get to know how Scala and Spark go hand-in-hand for
developers when developing ML systems with Spark * Build a
recommendation engine that scales with Spark * Find out how to
build unsupervised clustering systems to classify data in Spark *
Build machine learning systems with the Decision Tree and Ensemble
models in Spark * Deal with the curse of high-dimensionality in big
data using Spark * Implement Text analytics for Search Engines in
Spark * Streaming Machine Learning System implementation using
Spark In Detail Machine learning aims to extract knowledge from
data, relying on fundamental concepts in computer science,
statistics, probability, and optimization. Learning about
algorithms enables a wide range of applications, from everyday
tasks such as product recommendations and spam filtering to cutting
edge applications such as self-driving cars and personalized
medicine. You will gain hands-on experience of applying these
principles using Apache Spark, a resilient cluster computing system
well suited for large-scale machine learning tasks. 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. Style and approach This book is packed with
intuitive recipes supported with line-by-line explanations to help
you understand how to optimize your work flow and resolve problems
when working with complex data modeling tasks and predictive
algorithms. This is a valuable resource for data scientists and
those working on large scale data projects.
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