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1. Getting Up and Running with Spark
2. Designing a Machine Learning System
3. Obtaining, Processing and Preparing Data with Spark
4. Building a Recommendation Engine with Spark
5. Building a Classification Model with Spark
6. Building a Regression Model with Spark
7. Building a Clustering Model with Spark
8. Dimensionality Reduction with Spark
9. Advanced Text Processing with Spark
10. Real-Time Machine Learning with Spark Streaming

Key Features
*Follow real-world examples to learn how to develop your own machine learning systems with Spark
*A practical tutorial with real-world use cases allowing you to develop your own machine learning systems with Spark
*Combine various techniques and models into an intelligent machine learning system
*Explore and use Spark s powerful range of features to load, analyze, clean, and your data


Book Description
Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design.

This book guides you through the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction. You will cover advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming.

What you will learn
-Create your first Spark program in Scala, Java, and Python
-Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2
-Access public machine learning datasets and use Spark to load, process, clean, and transform data
-Use Spark's machine learning library to implement programs utilizing well-known machine learning models including collaborative filtering, classification, regression, clustering, and dimensionality reduction
-Write Spark functions to evaluate the performance of your machine learning models
-Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models
-Explore online learning methods and use Spark Streaming for online learning and model evaluation