This course develops the programming, statistical, and mathematical foundations required to understand how intelligent systems learn, generalize, optimize, and make decisions under uncertainty. Learners use Python, NumPy, Pandas, SciPy, and visualization tools to prepare and analyze data, apply descriptive and inferential statistics, model uncertainty through probability and Bayesian reasoning, and understand the linear algebra, calculus, optimization, information theory, and learning-theory concepts that underpin machine learning, deep learning, and foundation-model training. The emphasis is on connecting mathematical ideas to data, executable code, model behavior, assumptions, and limitations.