Fundamentals of Data Science

4: Machine learning

Abstract

This chapter presents an in-depth discussion on the foundational concepts of machine learning and introduces a number of assessment metrics that are essential for evaluating the effectiveness of machine learning models. To emphasize the importance of labeled and unlabeled data in various circumstances, it first discusses various machine learning paradigms such as supervised, unsupervised, and semi-supervised learning. When conducting machine learning experiments, the chapter emphasizes the significance of adequate experimental design and reporting. The discussion of evaluation measures for supervised learning models takes up a sizable section of the chapter. In order to give readers a clear idea of how these measures ...