As the frontiers of machine learning (ML) continue to expand, the gap between the public understanding of ML and state-of-the-art research widens. While laboratory researchers benefit from easily accessible and encouraged collaboration with domain experts, the same cannot be said of newcomers to the field. At the undergraduate level, where socioeconomic inequality means some students have stronger backgrounds than their peers, increasing the accessibility of practical, hands-on opportunities in machine learning is essential to narrowing this gap. In this paper, we detail the approach of Machine Learning @ Berkeley (ML@B), a university-based undergraduate student organization aimed at bridging this gap by encouraging collaboration with established figures in the field as well as within the organization itself. We hope that our perspectives gained from ML@B provide insights into successfully integrating undergraduates into a technical environment and fostering an academic culture that encourages collaboration.