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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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Decision trees for supervised learning

In this section, we'll see how to use DTs to solve both regression and classification problems. In the previous two chapters, Chapter 2, Scala for Regression Analysis, and Chapter 3, Scala for Learning Classification, we solved customer churn and insurance-severity claim problems. Those were classification and regression problems, respectively. In both approaches, we used other classic models. However, we'll see how we can solve them with tree-based and ensemble techniques. We'll use the DT implementation from the Apache Spark ML package in Scala.

Decision trees for classification

First of all, we know the customer churn prediction problem in Chapter 3, Scala for Learning...

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