A Framework and Its User Interface to Learn Machine Learning Models

Abstract

In order to develop a system related to machine learning (ML), it is necessary to understand various contents such as prerequisite knowledge, implementation procedures, verification methods, and improvement methods. However, although general learning sites on the Web provide extensive learning contents such as videos and textbooks, they are insufficient for acquiring practical skills. In this paper, we propose a framework for learning ML and its user interface. The framework manages the ML learning phases, which includes learning the theory and practical knowledge, implementation, validation, improvement, and completion. In the model validation phase, checks are automatically applied according to the target ML model. Similarly, in the model improvement phase, improvement methods are automatically applied according to the target ML model. As a case study, we have developed contents on linear regression, classification, clustering, and dimensionality reduction.