Mastering Machine Learning with Spark 2.x

Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial
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Mastering Machine Learning with Spark 2.x

Alex Tellez, Max Pumperla, Michal Malohlava

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

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Book Details

ISBN 139781785283451
Paperback340 pages

Book Description

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter.

This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification.

Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.

Table of Contents

Chapter 1: Introduction to Large-Scale Machine Learning and Spark
Data science
The sexiest role of the 21st century – data scientist?
Introducing H2O.ai
What's the difference between H2O and Spark's MLlib?
Data munging
Data science - an iterative process
Summary
Chapter 2: Detecting Dark Matter - The Higgs-Boson Particle
Type I versus type II error
Spark start and data load
Summary
Chapter 3: Ensemble Methods for Multi-Class Classification
Data
Modeling goal
Summary
Chapter 4: Predicting Movie Reviews Using NLP and Spark Streaming
NLP - a brief primer
The dataset
Feature extraction
Featurization - feature hashing
Let's do some (model) training!
Super learner
Summary
Chapter 5: Word2vec for Prediction and Clustering
Motivation of word vectors
Word2vec explained
Doc2vec explained
Applying word2vec and exploring our data with vectors
Creating document vectors
Supervised learning task
Summary
Chapter 6: Extracting Patterns from Clickstream Data
Frequent pattern mining
Pattern mining with Spark MLlib
Deploying a pattern mining application
Summary
Chapter 7: Graph Analytics with GraphX
Basic graph theory
GraphX distributed graph processing engine
Graph algorithms and applications
Summary
Chapter 8: Lending Club Loan Prediction
Motivation
Preparation of the environment
Data load
Exploration – data analysis
Summary

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

Authors

Table of Contents

Chapter 1: Introduction to Large-Scale Machine Learning and Spark
Data science
The sexiest role of the 21st century – data scientist?
Introducing H2O.ai
What's the difference between H2O and Spark's MLlib?
Data munging
Data science - an iterative process
Summary
Chapter 2: Detecting Dark Matter - The Higgs-Boson Particle
Type I versus type II error
Spark start and data load
Summary
Chapter 3: Ensemble Methods for Multi-Class Classification
Data
Modeling goal
Summary
Chapter 4: Predicting Movie Reviews Using NLP and Spark Streaming
NLP - a brief primer
The dataset
Feature extraction
Featurization - feature hashing
Let's do some (model) training!
Super learner
Summary
Chapter 5: Word2vec for Prediction and Clustering
Motivation of word vectors
Word2vec explained
Doc2vec explained
Applying word2vec and exploring our data with vectors
Creating document vectors
Supervised learning task
Summary
Chapter 6: Extracting Patterns from Clickstream Data
Frequent pattern mining
Pattern mining with Spark MLlib
Deploying a pattern mining application
Summary
Chapter 7: Graph Analytics with GraphX
Basic graph theory
GraphX distributed graph processing engine
Graph algorithms and applications
Summary
Chapter 8: Lending Club Loan Prediction
Motivation
Preparation of the environment
Data load
Exploration – data analysis
Summary

Book Details

ISBN 139781785283451
Paperback340 pages
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