Mastering Machine Learning with Spark 2.x

✍ Scribed by Alex Tellez, Max Pumperla, Michal Malohlava

• Publisher: Packt Publishing
• Year: 2017
• Tongue: English
• Leaves: 419
• Category: Library

✦ Synopsis

Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial

About This Book

• Process and analyze big data in a distributed and scalable way
• Write sophisticated Spark pipelines that incorporate elaborate extraction
• Build and use regression models to predict flight delays

Who This Book Is For

Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.

What You Will Learn

• Use Spark streams to cluster tweets online
• Run the PageRank algorithm to compute user influence
• Perform complex manipulation of DataFrames using Spark
• Define Spark pipelines to compose individual data transformations
• Utilize generated models for off-line/on-line prediction
• Transfer the learning from an ensemble to a simpler Neural Network
• Understand basic graph properties and important graph operations
• Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language
• Use K-means algorithm to cluster movie reviews dataset

In Detail

The purpose of machine

✦ Subjects

Data Mining;Databases & Big Data;Computers & Technology;Data Processing;Databases & Big Data;Computers & Technology