I want to sharpen my understanding of advanced machine-learning techniques in Python through a concise, hands-on session. The focus should be on three core areas: • Data preprocessing • Model selection • Evaluation metrics Please walk me through the theory just enough to set context, then dive straight into practical demonstrations in a Jupyter notebook. By the end, I’d like a runnable notebook (with comments) and any slides or reference links you use so I can review them afterward. A live, screen-sharing format suits me best, though I’m flexible if you prefer recording the walkthrough in one clear take. I value clear explanations, real-world examples, and succinct code over lengthy lectures. If this sounds like your teaching style, let’s get started.