Machine Learning is becoming a standard part of Computer Science curriculums at universities. This paper aims to contribute to the education of Machine Learning in Computer Science, specifically through teaching concepts related to Gradient Descent (GD) through analogies. First, concepts related to Gradient Descent were collected through the use of academic textbooks, and analogies were created based on the definitions found. These analogies were then evaluated by experts, scoring the analogies on Target Concept Coverage, Mapping Strength, and Metaphoricity. The analogies that scored highest on a mean average were then used in an A/B survey distributed amongst Computer Science students that had not followed any Machine Learning course. One group was given the concept definitions, the other both the definitions and the analogies. The learning proficiency was measured, and no statistically significant result was found. In the end, this research explores the possibilities of creating analogies to explain machine learning concepts, and provides a modular framework for evaluating quality and measuring effectiveness of analogies.