Traditional machine learning (ML) education faces challenges in assessing student learning and providing practical exposure. Group project-based learning, an increasingly popular form of experiential learning in CS education, encounters issues with limited participation and non-participation from a few students. Studies also suggest that students find longer programming assignments and project-based assessments distracting and struggle to maintain focus when they coincide with other subjects. To address this, we introduce FlashHack: a monitored, incremental, in-classroom micro hackathon that combines project-based learning with hackathon elements. Engaging 229 students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe. Assessment criteria emphasized machine learning application, problem-solving, collaboration, and creativity. Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors. This experience report outlines the hackathon’s design and implementation, highlights successes and areas for improvement, and offers recommendations for instructors interested in adopting a similar approach, contributing to the computing education community.