machine-learning

# Machine Learning Comprehensive machine learning skill covering the full ML lifecycle from experimentation to production deployment. ## When to Use This Skill - Building machine learning pipelines - Feature engineering and data preprocessing - Model training, evaluation, and selection - Hyperparameter tuning and optimization - Model deployment and serving - ML experiment tracking and versioning - Production ML monitoring and maintenance ## ML Development Lifecycle ### 1. Problem Definition **Classification Types:** - Binary classification (spam/not spam) - Multi-class classification (image categories) - Multi-label classification (document tags) - Regression (price prediction) - Clustering (customer segmentation) - Ranking (search results) - Anomaly detection (fraud detection) **Success Metrics by Problem Type:** | Problem Type | Primary Metrics | Secondary Metrics | |--------------|-----------------|-------------------| | Binary Classification | AUC-ROC, F1 | Precision, Recall, PR-AUC | | Multi-class | Macro F1, Accuracy | Per-class metrics | | Regression | RMSE, MAE | R², MAPE | | Ranking | NDCG, MAP | MRR | | Clustering | Silhouette, Calinski-Harabasz | Davies-Bouldin | ### 2. Data Preparation **Data Quality Checks:** - Missing value analysis and imputation strategies - Outlier detection and handling - Data type validation - Distribution analysis - Target leakage detection **Feature Engineering Patterns:** - Numerical: scaling, binning, log transforms, polyn...