Machine Learning education faces significant challenges due to the abstract and mathematically-complex nature of fundamental models, such as Multilayer Perceptrons (MLPs). This paper investigates the effectiveness of conceptual metaphors and analogies as pedagogical tools to improve novice learner's understanding of key MLP concepts. Using large language models, we generated a set of analogies for core MLP topics. These analogies were then evaluated by experts to assess their quality, followed by a user study with novice learners employing a between-subject A/B test comparing analogy-based explanations to formal definitions. Although the study found no statistically significant improvement in knowledge gain or engagement that could be attributed to analogy-based explanations, trends suggest potential benefits in learner confidence and motivation. The research contributes a curated set of expert-evaluated analogies for ML education and discusses methodology limitations and directions for future work. This study highlights both the promise and complexity of integrating analogy-based teaching approaches into ML education.