Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

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.

Related Content you might be interested in

Current Title:

Small book image
Apache Spark 2.x Machine Learning Cookbook
Mohammed Guller | Siamak Amirghodsi | Shuen Mei | Meenakshi Rajendran | Broderick Hall
Sep, 2017 | 666 pages
Small book image
Machine Learning with Spark
Manpreet Singh Ghotra | Rajdeep Dua
Apr, 2017 | 532 pages
Small book image
Scala and Spark for Big Data Analytics
Md Rezaul Karim | Sridhar Alla
Jul, 2017 | 796 pages
Small book image
Hands-On Data Analysis with Scala
Rajesh Gupta
May, 2019 | 298 pages
Table of Contents (20 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Practical Machine Learning with Spark Using Scala
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
2
Just Enough Linear Algebra for Machine Learning with Spark
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
3
Spark's Three Data Musketeers for Machine Learning - Perfect Together
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
4
Common Recipes for Implementing a Robust Machine Learning System
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
5
Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I
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
6
Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II
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
7
Recommendation Engine that Scales with Spark
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
8
Unsupervised Clustering with Apache 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
9
Optimization - Going Down the Hill with Gradient Descent
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
10
Building Machine Learning Systems with Decision Tree and Ensemble Models
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
11
Curse of High-Dimensionality in Big Data
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
12
Implementing Text Analytics with Spark 2.0 ML Library
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
13
Spark Streaming and Machine Learning Library
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

About the Reviewer

Sumit Pal, who has authored SQL on Big Data - Technology, Architecture, and Innovations by Apress, has more than 22 years of experience in the software industry in various roles, spanning companies from startups to enterprises. 

Sumit is an independent consultant working with big data, data visualization, and data science, and he is a software architect building end-to-end data-driven analytic systems.

Sumit has worked for Microsoft (SQL server development team), Oracle (OLAP development team), and Verizon (big data analytics team) in a career spanning 22 years. Currently, he works for multiple clients advising them on their data architectures and big data solutions, and does hands-on coding with Spark, Scala, Java, and Python.

Sumit has spoken at the following Big Data Conferences:

  • Data Summit NY, May 2017
  • Big Data Symposium Boston, May 2017
  • Apache Linux Foundation, May 2016, Vancouver, Canada,
  • Data Center World, March 2016, Las Vegas
  • Chicago, Nov 2015
  • Big Data Conferences in Global Big Data Conference in Boston, Aug 2015

Sumit has also developed a Big Data Analyst Training course for Experfy, more details of which can be found at https://www.experfy.com/training/courses/big-data-analyst.

Sumit has an extensive experience in building scalable systems across the stack from middle tier and data tier to visualization for analytics applications, using big data and NoSQL DB. He has deep expertise in database internals, data warehouses, dimensional modeling, data science with Java and Python, and SQL. 

Sumit started his career as a part of the SQL Server Development Team at Microsoft in 1996-97 and then as a core server engineer for Oracle Corporation at their OLAP Development team in Burlington, MA.

Sumit has also worked at Verizon as an Associate Director for big data architecture, where he strategized, managed, architected, and developed platforms and solutions for analytics and machine learning applications. He has also served as a chief architect at ModelN/LeapfrogRX (2006-2013), where he architected the middle-tier core analytics platform with open source OLAP engine (Mondrian) on J2EE and solved some complex Dimensional ETL, Modeling, and performance optimization problems.

Sumit has MS and BS in computer science. He hiked to the Mt. Everest Base camp in Oct, 2016.

 

 

Previous Section
Next Chapter