I am ready to dive into natural language processing and would like structured, hands-on coaching that takes me from the basics to building reliable text-classification models. My preferred language is Python, so examples should rely on common stacks such as Jupyter Notebook, Pandas, scikit-learn or, when it makes sense, PyTorch and Hugging Face Transformers. Java and R are not in scope for this engagement. Here is what I need from you: • A clear learning roadmap that starts with data cleaning and exploratory analysis, then walks through feature engineering (tokenisation, embeddings, etc.), model selection, training, evaluation and deployment. • Well-commented Python notebooks and sample datasets so I can reproduce every step on my own machine. • Short explanations of the underlying math concepts, delivered in plain language. • At least one mini-project where we build and benchmark a text-classifier end-to-end. • Live or recorded walkthroughs so I can watch your workflow and ask questions. I learn fastest by doing, so each concept should be paired with code I can immediately run and modify. If this format works for you, let me know how you would structure our sessions and what materials you already have that can accelerate the process.