Present novice-friendly machine studying (ML) modeling instruments focus on a solo consumer expertise, the place a single consumer collects solely their very own information to construct a mannequin. Nonetheless, solo modeling experiences restrict beneficial alternatives for encountering various concepts and approaches that may come up when learners work collectively; consequently, it usually precludes encountering essential points in ML round information illustration and variety that may floor when completely different views are manifested in a group-constructed information set. To handle this subject, we created Co-ML – a tablet-based app for learners to collaboratively construct ML picture classifiers via an end-to-end, iterative model-building course of. On this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case research of a household (two youngsters 11 and 14-years-old working with their mother and father) utilizing Co-ML in a facilitated introductory ML exercise at dwelling. We share the Co-ML system design and contribute a dialogue of how utilizing Co-ML in a collaborative exercise enabled freshmen to collectively interact with dataset design issues underrepresented in prior work similar to information range, class imbalance, and information high quality. We focus on how a distributed collaborative course of, during which people can tackle completely different model-building duties, gives a wealthy context for kids and adults to study ML dataset design.