ML Model Training

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