I want to level-up my data-analysis skills by diving into machine learning that works specifically with text data. I’m looking for a mentor who can break concepts down clearly, walk me through hands-on examples, and leave me with resources I can revisit afterward. What I need • A short series of live, screen-share sessions (Zoom, Google Meet, or similar) that cover the core workflow of building an ML model for text: cleaning, vectorizing, training, evaluating, and iterating. • A well-commented notebook (Python preferred—think scikit-learn, pandas, maybe a peek at TensorFlow if time allows) that mirrors what we covered in the calls. • A concise checklist or roadmap highlighting next steps so I can continue practicing on my own. Focus areas – Machine learning fundamentals applied to text classification or clustering. – Explanation of why and when to choose supervised vs. unsupervised techniques; I’m open to your recommendation. – Practical tips on data sourcing, preprocessing, and avoiding common pitfalls. Success looks like I finish our sessions confident enough to load a fresh text dataset, choose an appropriate model, and evaluate its performance without getting lost. If you enjoy teaching, communicate clearly, and can keep examples simple yet meaningful, let’s chat.