Teach Me Text Machine Learning
Budget: $15 – $25 USD
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.
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.