Machine Learning Math Tutor based on book mathematics for machine learning

Sessions will revolve around linear algebra, calculus, and probability as presented in the open-access text https://mml-book.github.io/book/mml-book.pdf, always tying theory back to practical machine-learning use-cases. I am a Beginner and progress fastest through Kinesthetic (hands-on activities), so each concept should be paired with worked examples, code snippets in Python or MATLAB where relevant, and plenty of guided problem practice.

What I need from you
• Live, interactive lessons that unpack the core ideas behind vectors, matrices, multivariable calculus, limits, derivatives, integrals, random variables, and distributions, then connect them to algorithms such as gradient descent, PCA, and logistic regression.
• Custom practice sets after every session, plus detailed walk-throughs of the solutions.
• Brief recap notes highlighting key takeaways and next steps so I can review between meetings.

Acceptance criterion
By the end of our work together I should comfortably manipulate matrix factorizations, compute gradients for simple models by hand, and evaluate basic probability expressions without external help.

Please outline your proposed lesson structure, preferred tools, and any prior experience teaching math for machine learning when you reply.