SKILL.md
ML Model Training
Training machine learning models involves selecting appropriate algorithms, preparing data, and optimizing model parameters to achieve strong predictive performance.
Training Phases
- Data Preparation: Cleaning, encoding, normalization
- Feature Engineering: Creating meaningful features
- Model Selection: Choosing appropriate algorithms
- Hyperparameter Tuning: Optimizing model settings
- Validation: Cross-validation and evaluation metrics
- Deployment: Preparing models for production
Common Algorithms
- Regression: Linear, Ridge, Lasso, Random Forest
- Classification: Logistic, SVM, Random Forest, Gradient Boosting
- Clustering: K-Means, DBSCAN, Hierarchical
- Neural Networks: MLPs, CNNs, RNNs, Transformers