What I teach
and how well

From elementary equations to abstract algebraic structures - building logic and symbolic reasoning.

Limits, derivatives, and integration taught with geometric intuition before formal notation.

Vectors, matrices, and transformations - the backbone of machine learning and computer graphics.

Making sense of data through probability theory, distributions, and inferential methods.

Python from fundamentals to data manipulation and visualization using NumPy, Pandas, and Matplotlib.

Supervised and unsupervised algorithms explained mathematically and implemented with scikit-learn.

Neural networks, CNNs, and training principles - demystifying the "black box" of modern AI.

Conceptual understanding of how AI systems think, learn, and make decisions.