Preface

Machine learning has made a huge impact not only in academia, but also in industry, by turning data into actionable intelligence. Scala is not only an object-oriented and functional programming language, but can also leverage the advantages of Java Virtual Machine (JVM). Scala provides code complexity optimization and offers concise notation, which is probably the reason it has seen a steady rise in adoption over the last few years, especially in data science and analytics.

This book is aimed at aspiring data scientists, data engineers, and deep learning enthusiasts who are newbies and want to have a great head start at machine learning best practices. Even if you're not well versed in machine learning concepts, but still want to expand your knowledge by delving into practical implementations of supervised learning, unsupervised learning, and recommender systems with Scala, you will be able to grasp the content easily!

Throughout the chapters, you'll become acquainted with popular machine learning libraries in Scala, learning how to carry out regression and classification analysis using both linear methods and tree-based ensemble techniques, as well as looking at clustering analysis, dimensionality reduction, and recommender systems, before delving into deep learning at the end.

After reading this book, you will have a good head start in solving more complex machine learning tasks. This book isn't meant to be read cover to cover. You can turn the pages to a chapter that looks like something you're trying to accomplish or that ignites your interest.

Suggestions for improvement are always welcome. Happy reading!