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Learn how to diagnose and improve underperforming machine learning and deep learning models. This path takes you from weak baselines to stronger models through a structured process of evaluation and refinement.
For classical models, you will explore regularization, capacity tuning, early stopping, and systematic hyperparameter searches using scikit-learn and XGBoost. You will use evaluation strategies to identify problems and select appropriate adjustments.
For neural networks, you will apply dropout, batch normalization, learning rate schedules, optimizer selection, weight initialization, and early stopping in PyTorch. The path is designed for learners who want practical experience improving model performance.
