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