Certificate in AI Metrics for Progress
-- ViewingNowThe Certificate in AI Metrics for Progress is a comprehensive course designed to empower learners with the essential skills needed to thrive in the AI industry. This course focuses on the importance of AI metrics in measuring the performance and effectiveness of AI systems, a critical skill in today's data-driven world.
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⢠Introduction to AI Metrics: Understanding the importance of measuring AI performance, key terms and concepts.
⢠Data Preparation for AI Metrics: Data preprocessing, data quality assessment, and data standardization for accurate AI metric calculation.
⢠Performance Metrics for Supervised Learning: Accuracy, precision, recall, F1 score, ROC curve, and AUC for evaluating supervised learning models.
⢠Performance Metrics for Unsupervised Learning: Cluster purity, silhouette score, and Calinski-Harabasz index for evaluating unsupervised learning models.
⢠Evaluating AI Model Explainability: SHAP, LIME, and feature importance for assessing AI model explainability.
⢠AI Model Fairness Metrics: Demographic parity, equal opportunity, and equalized odds for evaluating AI model fairness.
⢠AI Model Robustness Metrics: Sensitivity analysis, adversarial robustness, and generalization performance for assessing AI model robustness.
⢠Monitoring AI Metrics over Time: Techniques for tracking AI model performance and detecting performance degradation.
⢠Best Practices for AI Metrics: Guidelines for selecting, calculating, and interpreting AI metrics in a responsible and effective manner.
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