Background and context: Researchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K-12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context. Objective: The purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings. Method: Using a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3-13 years constructed their interactions with a machine-based technology (Google's Teachable Machine). Findings: This study provides empirical support for the claim that Google's Teachable Machine contributes to the development of data literacy and conceptual understanding across K-12 irrespective of the learners' backgrounds. The results also confirmed children's ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning. Implications: The results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K-12 context.