Certificate in Model Analysis & Performance Optimization
-- ViewingNowThe Certificate in Model Analysis & Performance Optimization is a crucial course for professionals seeking to enhance their data analysis skills. This program focuses on teaching learners how to evaluate and improve machine learning models, making them more accurate and efficient.
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⢠Introduction to Model Analysis: Fundamentals of model analysis, understanding model accuracy, and performance metrics.
⢠Data Preprocessing for Model Analysis: Data cleaning, normalization, and transformation techniques for effective model analysis.
⢠Performance Metrics for Regression Models: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and Adjusted R-squared.
⢠Performance Metrics for Classification Models: Accuracy, Precision, Recall, F1-score, Confusion Matrix, and ROC Curve.
⢠Model Evaluation Techniques: Cross-validation, Bootstrapping, and Train-test split.
⢠Hyperparameter Tuning: Grid search, Random search, and Bayesian optimization.
⢠Model Optimization Techniques: Regularization (L1 and L2), Early stopping, and Ensemble methods.
⢠Model Interpretation and Visualization: Feature importance, Partial dependence plots, and SHAP values.
⢠Bias-Variance Tradeoff: Understanding the concept and techniques to balance bias and variance.
⢠Deploying Optimized Models: Best practices for deploying optimized models in production environments.
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