About Machine Learning
A Python machine learning skill for practitioners building and evaluating models with scikit-learn, PyTorch, and supporting tools. It covers feature engineering and basic MLOps practices through deployment.
Key features
- 01scikit-learn preprocessing and training
- 02PyTorch models and training loops
- 03Feature engineering techniques
- 04Model evaluation and comparison
- 05Basic MLOps practices
Use cases
- 01Build a model training pipeline
- 02Tune and evaluate model performance
- 03Prepare features and handle missing data
- 04Track experiments and serve models
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/machine-learningin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
assets/config.yamlschema.jsonreferences/GUIDE.mdPATTERNS.mdscripts/validate.pyDetails
From the project READMEPython Machine Learning Skill
Overview
Build machine learning models using Python libraries including scikit-learn, PyTorch, and supporting tools.
Topics Covered
Scikit-learn
- Data preprocessing
- Model selection
- Training pipelines
- Cross-validation
- Hyperparameter tuning
PyTorch Basics
- Tensor operations
- Neural network modules
- Training loops
- DataLoader usage
- GPU acceleration
Feature Engineering
- Feature selection
- Dimensionality reduction
- Feature scaling
- Encoding techniques
- Missing data handling
Model Evaluation
- Metrics selection
- Confusion matrix
- ROC curves
- Learning curves
- Model comparison
MLOps Basics
- Model serialization
- Experiment tracking (MLflow)
- Model versioning
- Serving models
- Reproducibility
Prerequisites
- Python fundamentals
- NumPy and Pandas
- Statistics basics
Learning Outcomes
- Train ML models
- Evaluate model performance
- Build ML pipelines
- Deploy models to production
Comments
Markdown supported