The course emphasizes classical, interpretable models, practical data workflows, and hands-on problem solving. Weekly lectures and tutorials guide students through core concepts, including supervised and unsupervised learning, classification and regression, and models such as k-nearest neighbors, linear and logistic regression, decision trees, random forests, clustering methods and so on. Students learn to prepare datasets, train and validate models, avoid overfitting, and interpret results critically. Tutorials focus on Python-based tools including NumPy, Pandas, Matplotlib, and scikit-learn. Assessments include assignments, quizzes, and a midterm exam. The course concludes in an in-class modelling challenge and a guest industry seminar highlighting real-world AI applications. By the end, students gain the ability to apply machine learning appropriately and evaluate its limitations in scientific and engineering contexts.