Machine Learning with Scala Quick Start Guide: Leverage popular machine learning algorithms and techniques and implement them in Scala

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Scala for Regression Analysis

In this chapter, we will learn regression analysis in detail. We will start learning from the regression analysis workflow followed by the linear regression (LR) and generalized linear regression (GLR) algorithms. Then we will develop a regression model for predicting slowness in traffic using LR and GLR algorithms and their Spark ML-based implementation in Scala. Finally, we will learn the hyperparameter tuning with cross-validation and the grid searching techniques. Concisely, we will learn the following topics throughout this end-to-end project:

An overview of regression analysis
Regression analysis algorithms
Learning regression analysis through examples
Linear regression
Generalized linear regression
Hyperparameter tuning and cross-validation

Key benefits

Construct and deploy machine learning systems that learn from your data and give accurate predictions

Unleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.

Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j library

Description

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.

What you will learn

Get acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4j

Learn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured data

Understand supervised and unsupervised learning techniques with best practices and pitfalls

Learn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques

Learn effective ways of clustering analysis with dimensionality reduction techniques

Learn recommender systems with collaborative filtering approach

Delve into deep learning and neural network architectures

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