Machine Learning with Scala Quick Start Guide

By : Karim, Kumar N

Buy this Book

Machine Learning with Scala Quick Start Guide

By: Karim, Kumar N

Buy this Book

Overview of this book

Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Machine Learning with Scala

Technical requirements

Overview of ML

ML tasks

Overview of Scala

ML libraries in Scala

Getting started learning

Summary

Scala for Regression Analysis

Technical requirements

An overview of regression analysis

Regression analysis algorithms

Learning regression analysis through examples

Linear regression

Generalized linear regression (GLR)

Hyperparameter tuning and cross-validation

Summary

Scala for Learning Classification

Technical requirements

Overview of classification

Developing predictive models for churn

LR for churn prediction

NB for churn prediction

SVM for churn prediction

Summary

Scala for Tree-Based Ensemble Techniques

Technical requirements

Decision trees and tree ensembles

Decision trees for supervised learning

Gradient boosted trees for supervised learning

Random forest for supervised learning

What's next?

Summary

Scala for Dimensionality Reduction and Clustering

Technical requirements

Overview of unsupervised learning

Clustering analysis through examples

Dimensionality reduction

Summary

Scala for Recommender System

Technical requirements

Overview of recommendation systems

Model-based book recommendation system

Summary

Introduction to Deep Learning with Scala

Technical requirements

DL versus ML

DL and ANNs

Neural network architectures

DL frameworks

Getting started with learning

Summary

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Generalized linear regression (GLR)

In an LR, the output is assumed to follow a Gaussian distribution. In contrast, in generalized linear models (GLMs), the response variable Y_i follows some random distribution from a parametric set of probability distributions of a certain form. As we have seen in the previous example, following and creating a GLR estimator will not be difficult:

val glr = new GeneralizedLinearRegression()
      .setFamily("gaussian")//continuous value prediction (or gamma)
      .setLink("identity")//continuous value prediction (or inverse)
      .setFeaturesCol("features")
      .setLabelCol("label")

For the GLR-based prediction, the following response and identity link functions are supported based on data types (source: https://spark.apache.org/docs/latest/ml-classification-regression.html#generalized-linear-regression...