Machine Teaching (MT) is an emerging practice where people, without Machine Learning (ML) expertise, provide rich information beyond labels in order to create ML models. MT promises to lower the barrier of entry to creating ML models by requiring a softer set of skills from users than having ML expertise. In this paper, we explore and show how end-users without MT experience successfully build ML models using the MT process, and achieve results not far behind those of MT experts. We do this by conducting two studies. We first investigated how MT experts build models, from which we extracted expert teaching patterns. In our second study, we observed end-users without MT experience create ML models with and without guidance from expert patterns. We found that all users built models comparable to those built by MT experts. Further, we observed that users who received guidance perceived the task to require less effort and felt less mental demand than those who did not receive guidance.