Book Image

Scala Machine Learning Projects

Book Image

Scala Machine Learning Projects

Overview of this book

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.
Table of Contents (17 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Summary


In this chapter, we implemented two end-to-end projects to develop item-based collaborative filtering for movie similarity measurement and model-based recommendation with Spark. We also saw how to interoperate between ALS and MF and develop scalable movie recommendations engines. Finally, we saw how to deploy this model in production.

As human beings, we learn from past experiences. We haven't gotten so charming by accident. Years of positive compliments as well as criticism have all helped shape us into what we are today. You learn what makes people happy by interacting with friends, family, and even strangers, and you figure out how to ride a bike by trying out different muscle movements until it just clicks. When you perform actions, you're sometimes rewarded immediately. This is all about Reinforcement Learning (RL).

The next chapter is all about designing a machine learning project driven by criticisms and rewards. We will see how to apply RL algorithms for developing options...