According to a recent survey conducted by the Corporate Member Council of the American Society of Engineering Education (ASEE), there exists a notable disparity in the skill sets of engineering graduates about Artificial Intelligence (AI). To address this disparity from the African context, the Africa Centre of Excellence on New Pedagogies in Engineering Education organized a machine learning (ML) workshop for engineering students from different disciplines. Seventy-three (73) students enrolled for the workshop and the modules covered during this workshop were: Introduction to ML Models, ML Frameworks, Additive Explanations in ML, Performance Metrics, and Introduction to Ensemble Learning Techniques. The hands-on session involved the use of categorical boosting (CatBoost), an ensemble learning technique, to predict the bulk modulus, a mechanical property, of 199 ABX3 perovskite materials which was used as a problem set. The input features influencing the CatBoost model decisions were subsequently established. Correlation analysis on the input feature space removed features with high collinearity. The SHapley Additive exPlanations (SHAP) was used to analyze the decision-making rationale of the model. Evaluation of the model performance based on the coefficient of determination R2 value (0.94) revealed that the model demonstrates good performance in predicting the bulk modulus of the perovskite materials used during the practical sections. The survey results after the teaching and practical sessions indicate that the learning modules are an effective introduction for novice engineering students in this domain and raise awareness of the importance of this important sub-section of AI.