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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

To get the most out of this book

Get in touch

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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Regression analysis algorithms

There are numerous algorithms proposed and available, which can be used for the regression analysis. For example, LR tries to find relationships and dependencies between variables. It models the relationship between a continuous dependent variable y (that is, a label or target) and one or more independent variables, x, using a linear function. Examples of regression algorithms include the following:

Linear regression (LR)
Generalized linear regression (GLR)
Survival regression (SR)
Isotonic regression (IR)
Decision tree regressor (DTR)
Random forest regression (RFR)
Gradient boosted trees regression (GBTR)

We start by explaining regression with the simplest LR algorithm, which models the relationship between a dependent variable, y, which involves a linear combination of interdependent variables, x: