Machine.Learning.with.Spark

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文件名称：Machine.Learning.with.Spark
文件大小：4.74MB
文件格式：PDF
更新时间：2018-03-29 04:49:57
Machine Learning Spark Title: Machine Learning with Spark Author: Nick Pentreath Length: 329 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2014-12-08 ISBN-10: 1783288515 ISBN-13: 9781783288519 Create scalable machine learning applications to power a modern data-driven business using Spark About This Book 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 Use Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required. In Detail 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. Table of Contents Chapter 1. Getting Up and Running with Spark Chapter 2. Designing a Machine Learning System Chapter 3. Obtaining, Processing, and Preparing Data with Spark Chapter 4. Building a Recommendation Engine with Spark Chapter 5. Building a Classification Model with Spark Chapter 6. Building a Regression Model with Spark Chapter 7. Building a Clustering Model with Spark Chapter 8. Dimensionality Reduction with Spark Chapter 9. Advanced Text Processing with Spark Chapter 10. Real-time Machine Learning with Spark Streaming