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Book Overview & Buying
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Table Of Contents
Apache Spark 2.x Machine Learning Cookbook
By :
Apache Spark 2.x Machine Learning Cookbook
By:
Overview of this book
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.
Table of Contents (14 chapters)
Preface
Free Chapter
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 Gradient Descent
Building 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
