Book Image

Machine Learning with Scala Quick Start Guide

By : Md. Rezaul Karim, Ajay Kumar N

Book Image

Machine Learning with Scala Quick Start Guide

By: Md. Rezaul Karim, Ajay Kumar N

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

Free Chapter

Introduction to Machine Learning with Scala

Introduction to Machine Learning with Scala

Technical requirements

Overview of Scala

ML libraries in Scala

Getting started learning

Scala for Regression Analysis

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

Scala for Learning Classification

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

Scala for Tree-Based Ensemble Techniques

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

Scala for Dimensionality Reduction and Clustering

Scala for Dimensionality Reduction and Clustering

Technical requirements

Overview of unsupervised learning

Clustering analysis through examples

Dimensionality reduction

Scala for Recommender System

Scala for Recommender System

Technical requirements

Overview of recommendation systems

Model-based book recommendation system

Introduction to Deep Learning with Scala

Introduction to Deep Learning with Scala

Technical requirements

Neural network architectures

Getting started with learning

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Introduction to Deep Learning with Scala

Throughout Chapter 2, Scala for Regression Analysis, to Chapter 6, Scala for Recommender System, we have learned about linear and classic machine learning (ML) algorithms through real-life examples. In this chapter, we will explain some basic concepts of deep learning (DL). We will start with DL, which is one of the emerging branches of ML. We will briefly discuss some of the most well-known and widely used neural network architectures and DL frameworks and libraries.

Finally, we will use the Long Short-Term Memory (LSTM) architecture for cancer type classification from a very high-dimensional dataset curated from The Cancer Genome Atlas (TCGA). The following topics will be covered in this chapter:

DL versus ML
DL and neural networks
Deep neural network architectures
DL frameworks
Getting started with learning

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