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.
Table of Contents (9 chapters)

Scala for Recommender System

In this chapter, we will learn about different approaches for developing recommender systems. Then we will learn how to develop a book recommendation system. Technically, it will be a model-based recommendation engine based on alternating least squares (ALS) and matrix factorization algorithms. We will use Spark MLlib-based implementation of these algorithms in Scala. In a nutshell, we will learn the following topics throughout this chapter:

  • Overview of recommendation systems
  • Similarity-based recommender system
  • Content-based recommender system
  • Collaborative approaches
  • Hybrid recommendation systems
  • Developing a model-based book recommendation system