Model Training is the process of teaching machine learning algorithms to make predictions or decisions by feeding them data and allowing them to learn patterns, relationships, and features that enable accurate performance on new, similar data.

Key components of model training include:

• Training Data: Labeled examples used to teach the model
• Loss Function: Measures how far predictions are from actual values
• Optimization Algorithm: Methods like gradient descent to minimize loss
• Hyperparameters: Configuration settings that control learning behavior
• Epochs: Complete passes through the training dataset
• Batch Size: Number of samples processed before updating model parameters

The training process iteratively adjusts model parameters to minimize prediction errors. Successful training requires quality data, appropriate algorithms, sufficient computational resources, and careful monitoring to prevent overfitting.