Large-scale cancer genomics data often comes in multi-platform and heterogeneous forms. These datasets impose great challenges in terms of the bioinformatics approach and computational algorithms. Numerous researchers have proposed to utilize this data to overcome several challenges, using classical ML algorithms as either the primary subject or a supporting element for cancer diagnosis and prognosis.
Machine Learning with Scala Quick Start Guide
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Machine Learning with Scala Quick Start Guide
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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)
Preface
Free Chapter
Introduction to Machine Learning with Scala
Scala for Regression Analysis
Scala for Learning Classification
Scala for Tree-Based Ensemble Techniques
Scala for Dimensionality Reduction and Clustering
Scala for Recommender System
Introduction to Deep Learning with Scala
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