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Table Of Contents

Mastering Machine Learning with Spark 2.x

By : Malohlava, Tellez, Max Pumperla

5 (1)

Buy this Book

Mastering Machine Learning with Spark 2.x

5 (1)

By: Malohlava, Tellez, Max Pumperla

Buy this Book

Overview of this book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

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

Detecting Dark Matter - The Higgs-Boson Particle

Type I versus type II error

Spark start and data load

Summary

Ensemble Methods for Multi-Class Classification

Data

Modeling goal

Summary

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

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

Extracting Patterns from Clickstream Data

Frequent pattern mining

Pattern mining with Spark MLlib

Deploying a pattern mining application

Summary

Graph Analytics with GraphX

Basic graph theory

GraphX distributed graph processing engine

Graph algorithms and applications

Summary

Lending Club Loan Prediction

Motivation

Preparation of the environment

Data load

Exploration – data analysis

Summary

Super learner

In the preceding sections, we trained several models. Now, we will compose them into an ensemble called a super learner using a deep learning model. The process to build a super learner is straightforward (see the preceding figure):

Select base algorithms (for example, GLM, random forest, GBM, and so on).
Select a meta-learning algorithm (for example, deep learning).
Train each of the base algorithms on the training set.
Perform K-fold cross-validation on each of these learners and collect the cross-validated predicted values from each of the base algorithms.
The N cross-validated predicted values from each of the L-base algorithms can be combined to form a new NxL matrix. This matrix, along with the original response vector, is called the "level-one" data.
Train the meta-learning algorithm on the level-one data.
The super learner (or so-called "ensemble...