● Designed and conducted lab sessions on foundational machine learning topics, including NumPy, data visualization, data preprocessing, handling imbalanced datasets, and applying classification/regression models. ● Built lab materials and Jupyter notebooks covering decision trees (CART), logistic/linear regression, Naive Bayes, neural networks, and clustering. ● Developed custom Kaggle competitions to enhance students’ hands-on skills in neural network modeling and evaluation. ● Assisted students in written assignments and practical exams by explaining the inner workings of machine learning models, such as PCA, linear models, Naive bayes models, and Neural Networks. ● Implemented baseline models for Kaggle assignments to serve as scoring benchmarks for students.