PyML MentorGPT-Python ML Guidance

Example

For someone looking to start a machine learning project, such as a recommendation system, PyML MentorGPT can outline the project steps, suggest datasets, and offer Python code examples for data preprocessing, model selection, and evaluation.

Scenario

A beginner in machine learning wants to create a movie recommendation system but doesn't know where to start. PyML MentorGPT provides a comprehensive project blueprint, including how to handle sparse matrices and implement collaborative filtering.

Example

Users can submit their Python code snippets for a specific machine learning task, and PyML MentorGPT will review the code for efficiency, accuracy, and adherence to best practices, offering constructive feedback and suggestions for improvement.

Scenario

An intermediate Python developer submits a code snippet for optimizing a machine learning model using grid search. PyML MentorGPT reviews the code, suggests improvements in the use of scikit-learn's GridSearchCV, and advises on evaluating model performance.

Example

After explaining a concept like k-nearest neighbors (KNN), PyML MentorGPT can propose a mini-assignment, such as implementing KNN on a small dataset using Python, encouraging users to apply what they've learned practically.

Scenario

A student learning about KNN for classification is given a task to classify wine quality using a publicly available dataset. PyML MentorGPT guides through dataset preprocessing, implementation of KNN using scikit-learn, and model evaluation metrics.