Apache Spark 2.x Machine Learning Cookbook

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

Table of Contents

Chapter 1. Practical Machine Learning With Spark Using Scala
Chapter 2. Just Enough Linear Algebra For Machine Learning With Spark
Chapter 3. Spark’S Three Data Musketeers For Machine Learning – Perfect Together
Chapter 4. Common Recipes For Implementing A Robust Machine Learning System
Chapter 5. Practical Machine Learning With Regression And Classification In Spark 2.0 – Part I
Chapter 6. Practical Machine Learning With Regression And Classification In Spark 2.0 – Part Ii
Chapter 7. Recommendation Engine That Scales With Spark
Chapter 8. Unsupervised Clustering With Apache Spark 2.0
Chapter 9. Optimization – Going Down The Hill With Gradient Descent
Chapter 10. Building Machine Learning Systems With Decision Tree And Ensemble Models
Chapter 11. Curse Of High-Dimensionality In Big Data
Chapter 12. Implementing Text Analytics With Spark 2.0 Ml Library
Chapter 13. Spark Streaming And Machine Learning Library