Machine learning has made a huge impact on academia and industry by turning data into actionable intelligence. Scala, on the other hand, has been observing a steady rise in its adoption over the last few years, especially in the field of data science and analytics. This book has been written for data scientists, data engineers, and deep learning enthusiasts who have a solid background with complex numerical computing and want to learn more hands-on machine learning application development.
So, if you're well-versed in machine learning concepts and want to expand your knowledge by delving into practical implementations 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, Zeppelin, DeepLearning4j, and MXNet.
After reading this book and practicing all of the projects, you will be able to dominate numerical computing, deep learning, and functional programming to carry out complex numerical tasks. You can thus develop, build, and deploy research and commercial projects in a production-ready environment.
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 simply ignites your interest. But any kind of improvement feedback is welcome.
Happy reading!
