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

Chapter 4. Population-Scale Clustering and Ethnicity Prediction

Understanding variations in genome sequences assists us in identifying people who are predisposed to common diseases, curing rare diseases, and finding the corresponding population group of individuals from a larger population group. Although classical machine learning techniques allow researchers to identify groups (that is, clusters) of related variables, the accuracy and effectiveness of these methods diminish for large and high-dimensional datasets such as the whole human genome.

On the other hand, Deep Neural Networks (DNNs) form the core of deep learning (DL) and provide algorithms to model complex, high-level abstractions in data. They can better exploit large-scale datasets to build complex models.

In this chapter, we apply the K-means algorithm to large-scale genomic data from the 1000 Genomes project analysis aimed at clustering genotypic variants at the population scale. Finally, we train an H2O-based DNN model and...