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Machine Learning with Scala Quick Start Guide

Machine Learning with Scala Quick Start Guide

By : Karim, Kumar N
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Machine Learning with Scala Quick Start Guide

Machine Learning with Scala Quick Start Guide

By: Karim, 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.
Table of Contents (9 chapters)
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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
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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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Summary

In this chapter, we saw how to classify cancer patients on the basis of tumor types from a very high-dimensional gene expression dataset curated from TCGA. Our LSTM architecture managed to achieve 99% accuracy, which is outstanding. Nevertheless, we discussed many aspects of DL4J, which will be helpful in upcoming chapters. Finally, we saw answers to some frequent questions related to this project, LSTM networks, and DL4J hyperparameters/network tuning.

This is, more or less, the end of our little journey in developing ML projects using Scala and different open source frameworks. Throughout these chapters, I have tried to provide you with several examples of how to use these wonderful technologies efficiently for developing ML projects. While writing this book, I had to keep many constraints in my mind; for example, the page count, API availability, and of course, my expertise...

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