Once the data is preprocessed and relevant features are selected, the next step is training AI models. Model training involves feeding the data into machine learning algorithms to learn patterns and make predictions. AI automates this process using techniques like automated machine learning (AutoML), which selects the best algorithms, tunes hyperparameters, and evaluates model performance without human intervention. Gradient descent algorithms optimize model parameters iteratively to minimize error rates. AI also employs techniques such as cross-validation to prevent overfitting and ensure model generalization to new data. Furthermore, advanced optimization methods like Bayesian optimization and genetic algorithms are used to enhance model accuracy and efficiency, making the training process faster and more robust.