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.

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Machine Learning with Scala
Introduction to Machine Learning with Scala
Technical requirements
Overview of ML
ML tasks
Overview of Scala
ML libraries in Scala
Getting started learning
Summary
2
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
Summary
3
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
Summary
4
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
What's next?
Summary
5
Scala for Dimensionality Reduction and Clustering
Scala for Dimensionality Reduction and Clustering
Technical requirements
Overview of unsupervised learning
Clustering analysis through examples
Dimensionality reduction
Summary
6
Scala for Recommender System
Scala for Recommender System
Technical requirements
Overview of recommendation systems
Model-based book recommendation system
Summary
7
Introduction to Deep Learning with Scala
Introduction to Deep Learning with Scala
Technical requirements
DL versus ML
DL and ANNs
Neural network architectures
DL frameworks
Getting started with learning
Summary
8
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DL frameworks

There are several popular DL frameworks. Each of them comes with some pros and cons. Some of them are desktop-based and some of them are cloud-based platforms where you can deploy/run your DL applications. However, most of the libraries that are released under an open license help when people are using graphics processors, which can ultimately help in speeding up the learning process.

Such frameworks and libraries include TensorFlow, PyTorch, Keras, Deeplearning4j, H2O, and the Microsoft Cognitive Toolkit (CNTK). Even a few years back, other implementations including Theano, Caffee, and Neon were used widely. However, these are now obsolete. Since we will focus on learning in Scala, JVM-based DL libraries such as Deeplearning4j can be a reasonable choice. Deeplearning4j (DL4J) is one of the first commercial-grade, open source, distributed DL libraries that was built...

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