December 2016
Beginner to intermediate
392 pages
8h 13m
English
Content preview from Apache Spark for Data Science CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 5. Working with Spark MLlib
In this chapter, you will learn about the MLlib component of Spark. We will cover the following recipes:
- Implementing Naive Bayes classification
- Implementing decision trees
- Building a recommendation system
- Implementing logistic regression using Spark ML pipelines
Introduction
MLlib is the machine learning (ML) library that is provided with Apache Spark, the in-memory, cluster-based, open source data processing system. In this chapter, I will examine the functionality of algorithms provided within the MLlib library in terms of areas of machine learning tasks such as classification, recommendation, and neural processing. For each algorithm, we'll provide working examples that tackle real problems. We will take a step-by-step ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.