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Apache Spark 2.x Machine Learning Cookbook

By : Amirghodsi, Mohammed Guller, Shuen Mei, Rajendran, Hall

3 (2)

Buy this Book

Apache Spark 2.x Machine Learning Cookbook

3 (2)

By: Amirghodsi, Mohammed Guller, Shuen Mei, Rajendran, Hall

Buy this Book

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

Practical Machine Learning with Spark Using Scala

Introduction

Downloading and installing the JDK

Downloading and installing IntelliJ

Downloading and installing Spark

Configuring IntelliJ to work with Spark and run Spark ML sample codes

Running a sample ML code from Spark

Identifying data sources for practical machine learning

Running your first program using Apache Spark 2.0 with the IntelliJ IDE

How to add graphics to your Spark program

Just Enough Linear Algebra for Machine Learning with Spark

Introduction

Package imports and initial setup for vectors and matrices

Creating DenseVector and setup with Spark 2.0

Creating SparseVector and setup with Spark

Creating dense matrix and setup with Spark 2.0

Using sparse local matrices with Spark 2.0

Performing vector arithmetic using Spark 2.0

Performing matrix arithmetic using Spark 2.0

Exploring RowMatrix in Spark 2.0

Exploring Distributed IndexedRowMatrix in Spark 2.0

Exploring distributed CoordinateMatrix in Spark 2.0

Exploring distributed BlockMatrix in Spark 2.0

Spark's Three Data Musketeers for Machine Learning - Perfect Together

Introduction

Creating RDDs with Spark 2.0 using internal data sources

Creating RDDs with Spark 2.0 using external data sources

Transforming RDDs with Spark 2.0 using the filter() API

Transforming RDDs with the super useful flatMap() API

Transforming RDDs with set operation APIs

RDD transformation/aggregation with groupBy() and reduceByKey()

Transforming RDDs with the zip() API

Join transformation with paired key-value RDDs

Reduce and grouping transformation with paired key-value RDDs

Creating DataFrames from Scala data structures

Operating on DataFrames programmatically without SQL

Loading DataFrames and setup from an external source

Using DataFrames with standard SQL language - SparkSQL

Working with the Dataset API using a Scala Sequence

Creating and using Datasets from RDDs and back again

Working with JSON using the Dataset API and SQL together

Functional programming with the Dataset API using domain objects

Common Recipes for Implementing a Robust Machine Learning System

Introduction

Spark's basic statistical API to help you build your own algorithms

ML pipelines for real-life machine learning applications

Normalizing data with Spark

Splitting data for training and testing

Common operations with the new Dataset API

Creating and using RDD versus DataFrame versus Dataset from a text file in Spark 2.0

LabeledPoint data structure for Spark ML

Getting access to Spark cluster in Spark 2.0

Getting access to Spark cluster pre-Spark 2.0

Getting access to SparkContext vis-a-vis SparkSession object in Spark 2.0

New model export and PMML markup in Spark 2.0

Regression model evaluation using Spark 2.0

Binary classification model evaluation using Spark 2.0

Multiclass classification model evaluation using Spark 2.0

Multilabel classification model evaluation using Spark 2.0

Using the Scala Breeze library to do graphics in Spark 2.0

Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

Introduction

Fitting a linear regression line to data the old fashioned way

Generalized linear regression in Spark 2.0

Linear regression API with Lasso and L-BFGS in Spark 2.0

Linear regression API with Lasso and 'auto' optimization selection in Spark 2.0

Linear regression API with ridge regression and 'auto' optimization selection in Spark 2.0

Isotonic regression in Apache Spark 2.0

Multilayer perceptron classifier in Apache Spark 2.0

One-vs-Rest classifier (One-vs-All) in Apache Spark 2.0

Survival regression – parametric AFT model in Apache Spark 2.0

Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

Introduction

Linear regression with SGD optimization in Spark 2.0

Logistic regression with SGD optimization in Spark 2.0

Ridge regression with SGD optimization in Spark 2.0

Lasso regression with SGD optimization in Spark 2.0

Logistic regression with L-BFGS optimization in Spark 2.0

Support Vector Machine (SVM) with Spark 2.0

Naive Bayes machine learning with Spark 2.0 MLlib

Exploring ML pipelines and DataFrames using logistic regression in Spark 2.0

Recommendation Engine that Scales with Spark

Introduction

Setting up the required data for a scalable recommendation engine in Spark 2.0

Exploring the movies data details for the recommendation system in Spark 2.0

Exploring the ratings data details for the recommendation system in Spark 2.0

Building a scalable recommendation engine using collaborative filtering in Spark 2.0

Unsupervised Clustering with Apache Spark 2.0

Introduction

Building a KMeans classifying system in Spark 2.0

Bisecting KMeans, the new kid on the block in Spark 2.0

Using Gaussian Mixture and Expectation Maximization (EM) in Spark to classify data

Classifying the vertices of a graph using Power Iteration Clustering (PIC) in Spark 2.0

Latent Dirichlet Allocation (LDA) to classify documents and text into topics

Streaming KMeans to classify data in near real-time

Optimization - Going Down the Hill with Gradient Descent

Introduction

Optimizing a quadratic cost function and finding the minima using just math to gain insight

Coding a quadratic cost function optimization using Gradient Descent (GD) from scratch

Coding Gradient Descent optimization to solve Linear Regression from scratch

Normal equations as an alternative for solving Linear Regression in Spark 2.0

Building Machine Learning Systems with Decision Tree and Ensemble Models

Introduction

Getting and preparing real-world medical data for exploring Decision Trees and Ensemble models in Spark 2.0

Building a classification system with Decision Trees in Spark 2.0

Solving Regression problems with Decision Trees in Spark 2.0

Building a classification system with Random Forest Trees in Spark 2.0

Solving regression problems with Random Forest Trees in Spark 2.0

Building a classification system with Gradient Boosted Trees (GBT) in Spark 2.0

Solving regression problems with Gradient Boosted Trees (GBT) in Spark 2.0

Curse of High-Dimensionality in Big Data

Introduction

Two methods of ingesting and preparing a CSV file for processing in Spark

Singular Value Decomposition (SVD) to reduce high-dimensionality in Spark

Principal Component Analysis (PCA) to pick the most effective latent factor for machine learning in Spark

Implementing Text Analytics with Spark 2.0 ML Library

Introduction

Doing term frequency with Spark - everything that counts

Displaying similar words with Spark using Word2Vec

Downloading a complete dump of Wikipedia for a real-life Spark ML project

Using Latent Semantic Analysis for text analytics with Spark 2.0

Topic modeling with Latent Dirichlet allocation in Spark 2.0

Spark Streaming and Machine Learning Library

Introduction

Structured streaming for near real-time machine learning

Streaming DataFrames for real-time machine learning

Streaming Datasets for real-time machine learning

Streaming data and debugging with queueStream

Downloading and understanding the famous Iris data for unsupervised classification

Streaming KMeans for a real-time on-line classifier

Downloading wine quality data for streaming regression

Streaming linear regression for a real-time regression

Downloading Pima Diabetes data for supervised classification

Streaming logistic regression for an on-line classifier

Spark's Three Data Musketeers for Machine Learning - Perfect Together

In this chapter, we will cover the following recipes:

Creating RDDs with Spark 2.0 using internal data sources
Creating RDDs with Spark 2.0 using external data sources
Transforming RDDs with Spark 2.0 using the filter() API
Transforming RDDs with the super useful flatMap() API
Transforming RDDs with set operation APIs
RDD transformation/aggregation with groupBy() and reduceByKey()
Transforming RDDs with the zip() API
Join transformation with paired key-value RDDs
Reduce and grouping transformation with paired key-value RDDs
Creating DataFrames from Scala data structures
Operating on DataFrames programmatically without SQL
Loading DataFrames and setup from an external source
Using DataFrames with standard SQL language - SparkSQL
Working with the Dataset API using a Scala sequence
Creating and using...

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Apache Spark 2.x Machine Learning Cookbook

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Apache Spark 2.x Machine Learning Cookbook

By : Amirghodsi, Mohammed Guller, Shuen Mei, Rajendran, Hall

Apache Spark 2.x Machine Learning Cookbook

By: Amirghodsi, Mohammed Guller, Shuen Mei, Rajendran, Hall

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