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Machine Learning with Spark
Machine Learning with Spark

Machine Learning with Spark: Develop intelligent, distributed machine learning systems , Second Edition

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Machine Learning with Spark

Math for Machine Learning

A machine learning user needs to have a fair understanding of machine learning concepts and algorithms. Familiarity with mathematics is an important aspect of machine learning. We learn to program by understanding the fundamental concepts and constructs of a language. Similarly, we learn machine learning by understanding concepts and algorithms using Mathematics, which is used to solve complex computational problems, and is a discipline for understanding and appreciating many computer science concepts. Mathematics plays a fundamental role in grasping theoretical concepts and in choosing the right algorithm. This chapter covers the basics of linear algebra and calculus for machine learning.

In this chapter, we will cover the following topics:

  • Linear algebra
  • Environment setup
    • Setting up the Scala environment in Intellij
    • Setting up the Scala environment on the command line
  • Fields
  • Vectors...

Linear algebra

Linear algebra is the study of solving a system of linear equations and transformations. Vectors, matrices, and determinants are the fundamental tools of linear algebra. We will learn each of these in detail using Breeze. Breeze is the underlying linear algebra library used for numerical processing. Respective Spark objects are wrappers around Breeze, and act as a public interface to ensure the consistency of the Spark ML library even if Breeze changes internally.

Setting up the Scala environment in Intellij

It is best to use an IDE like IntelliJ to edit Scala code, which provides faster development tools and coding assistance. Code completion and inspection makes coding and debugging faster and simpler, ensuring you focus on the end goal of learning...

Gradient descent

An SGD implementation of gradient descent uses a simple distributed sampling of the data examples. Loss is a part of the optimization problem, and therefore, is a true sub-gradient.

This requires access to the full dataset, which is not optimal.

The parameter miniBatchFraction specifies the fraction of the full data to use. The average of the gradients over this subset

is a stochastic gradient. S is a sampled subset of size |S|= miniBatchFraction.

In the following code, we show how to use stochastic gardient descent on a mini batch to calculate the weights and the loss. The output of this program is a vector of weights and loss.

object SparkSGD { 
def main(args: Array[String]): Unit = {
val m = 4
val n = 200000
val sc = new SparkContext("local[2]", "")
val points = sc.parallelize(0 until m,
2).mapPartitionsWithIndex { (idx, iter) =>
val random...

Prior, likelihood, and posterior

Bayes theorem states the following:

Posterior = Prior * Likelihood

This can also be stated as P (A | B) = (P (B | A) * P(A)) / P(B) , where P(A|B) is the probability of A given B, also called posterior.

Prior: Probability distribution representing knowledge or uncertainty of a data object prior or before observing it

Posterior: Conditional probability distribution representing what parameters are likely after observing the data object

Likelihood: The probability of falling under a specific category or class.

This is represented as follows:

Calculus

Calculus is a mathematical tool which helps the study of how things change. It provides a framework for modeling systems in which there is change, and a way to deduce the predictions of such models.

Differential calculus

At the core of calculus lie derivatives, where the derivative is defined as the instantaneous rate of change of a given function with respect to one of its variables. The study of finding a derivative is known as differentiation. Geometrically, the derivative at a known point is given by the slope of a tangent line to the graph of the function, provided that the derivative exists, and is defined at that point.

Differentiation is the reverse of Integration. Differentiation has several applications; like in physics, the derivative of displacement...

Plotting

In this segment, we will see how to use Breeze to create a simple line plot from the Breeze DenseVector.

Breeze uses most of the functionality of Scala's plotting facilities, although the API is different. In the following example, we create two vectors x1 and y with some values, and plot a line and save it to a PNG file:

package linalg.plot 
import breeze.linalg._
import breeze.plot._

object BreezePlotSampleOne {
def main(args: Array[String]): Unit = {

val f = Figure()
val p = f.subplot(0)
val x = DenseVector(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8)
val y = DenseVector(1.1, 2.1, 0.5, 1.0,3.0, 1.1, 0.0, 0.5,2.5)
p += plot(x, y)
p.xlabel = "x axis"
p.ylabel = "y axis"
f.saveas("lines-graph.png")
}
}

The preceding code generates the following Line Plot:

Breeze also supports histogram. This is drawn for various sample sizes 100,000, and...

Summary

In this chapter, you learnt the basics of linear algebra, which is useful for machine learning, and the basic constructs like vectors and matrix. You also learnt how to use Spark and Breeze to do basic operations on these constructs. We looked at techniques like SVD to transform data. We also looked at the importance of the function types in linear algebra. In the end, you learnt how to plot basic charts using Breeze. In the next chapter, we will cover an overview of Machine Learning systems, components and architecture.

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Key benefits

  • Get to the grips with the latest version of Apache Spark
  • Utilize Spark's machine learning library to implement predictive analytics
  • Leverage Spark’s powerful tools to load, analyze, clean, and transform your data

Description

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.

Who is this book for?

If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.

What you will learn

  • Get hands-on with the latest version of Spark ML
  • Create your first Spark program with Scala and Python
  • Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2
  • Access public machine learning datasets and use Spark to load, process, clean, and transform data
  • Use Spark s machine learning library to implement programs by utilizing well-known machine learning models
  • Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models
  • Write Spark functions to evaluate the performance of your machine learning models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 28, 2017
Length: 532 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785889936
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Product Details

Publication date : Apr 28, 2017
Length: 532 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785889936
Category :
Languages :

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Table of Contents

12 Chapters
Getting Up and Running with Spark Chevron down icon Chevron up icon
Math for Machine Learning Chevron down icon Chevron up icon
Designing a Machine Learning System Chevron down icon Chevron up icon
Obtaining, Processing, and Preparing Data with Spark Chevron down icon Chevron up icon
Building a Recommendation Engine with Spark Chevron down icon Chevron up icon
Building a Classification Model with Spark Chevron down icon Chevron up icon
Building a Regression Model with Spark Chevron down icon Chevron up icon
Building a Clustering Model with Spark Chevron down icon Chevron up icon
Dimensionality Reduction with Spark Chevron down icon Chevron up icon
Advanced Text Processing with Spark Chevron down icon Chevron up icon
Real-Time Machine Learning with Spark Streaming Chevron down icon Chevron up icon
Pipeline APIs for Spark ML Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(9 Ratings)
5 star 22.2%
4 star 22.2%
3 star 22.2%
2 star 0%
1 star 33.3%
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UJJAWAL SINHA Aug 15, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great Book! I recommend to buy it if you want to know practical guide on Machine Learning using spark 2.0 and scala .
Amazon Verified review Amazon
Amazon Customer Aug 07, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Perfect book for someone who is just starting his adventure with Spark and Machine Learning. The author has very didactic approach that guides the reader step by step from understanding first the basics, installing the environment, implementing and training models, evaluating, etc. I strongly recommend this book.
Amazon Verified review Amazon
PJG Mar 24, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is a nice introduction to using the Apache Spark framework. It assumes no prior knowledge of either Hadoop, Spark or machine learning itself (although the latter is covered at quite a rapid pace in places so some background would likely be helpful!). The code examples are presented in Python and (mainly) Scala, with examples that are reasonably well-described.The overall tone of the book is clear and the chapters progress in a logical order, with a fairly rapid journey through the main machine learning techniques from a Spark perspective. Later chapters were particularly interesting, covering text mining and more complex methods (e.g. feature hashing).Some of the example data sets feel a little 'tired' (movie ratings data yet again - or perhaps I've just read too many machine learning books), but otherwise this is a good book and comes recommended.
Amazon Verified review Amazon
Ebot Tabi Aug 24, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book gives a great introduction to using the Apache Spark framework, a goto for anyone who wishes to learn how to use Apache Spark framework. It assumes no prior knowledge of either Hadoop or Spark. The code examples are presented in Python and Scala are well-described.The overall the book is clearly structured and the chapters progress in a logical order, with a fairly rapid introduction to machine learning techniques from a Spark perspective.I definitely do recommend this book for beginners.
Amazon Verified review Amazon
Mukesh Rao Apr 22, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Did not find as useful as ML in R by Bret Lantz
Amazon Verified review Amazon
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