Global Certificate in Model Confidence & Performance
-- ViewingNowThe Global Certificate in Model Confidence & Performance is a vital course for professionals seeking to enhance their machine learning skills. This certificate program focuses on model validation, an essential aspect of building reliable and accurate models, ensuring that your predictions are trustworthy and have a real-world impact.
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⢠Model Evaluation Metrics: Understanding and measuring model performance using various metrics such as accuracy, precision, recall, F1 score, ROC-AUC curve, etc.
⢠Model Selection & Tuning: Techniques for selecting and fine-tuning machine learning models, including cross-validation, grid search, and random search.
⢠Bias-Variance Tradeoff: Understanding the tradeoff between bias and variance in machine learning models and how to identify and address it.
⢠Regularization Techniques: Techniques for reducing overfitting in machine learning models, including L1 and L2 regularization, dropout, and early stopping.
⢠Ensemble Methods: Introduction to ensemble methods such as bagging, boosting, and stacking, and their impact on model confidence and performance.
⢠Interpretability & Explainability: Understanding the importance of model interpretability and explainability, and techniques for improving it, such as SHAP and LIME.
⢠Real-World Challenges: Discussion of real-world challenges in measuring model confidence and performance, including class imbalance, missing data, and data drift.
⢠Evaluation Strategies: Techniques for evaluating model performance in a real-world setting, including A/B testing, online evaluation, and offline evaluation.
⢠Model Monitoring & Maintenance: Best practices for monitoring and maintaining machine learning models in production, including model versioning, model retraining, and model drift detection.
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