Certificate in AI Metrics for Effective Implementation
-- ViewingNowThe Certificate in AI Metrics for Effective Implementation is a comprehensive course designed to equip learners with essential skills for successful AI implementation. This program emphasizes the importance of measuring and evaluating AI systems' performance, a critical aspect of AI projects that often gets overlooked.
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โข Introduction to AI Metrics: Understanding Key Performance Indicators (KPIs), Types of AI Metrics, Importance of AI Metrics
โข Data Preparation for AI Metrics: Data Collection, Data Cleaning, Feature Engineering, Data Visualization
โข Performance Metrics for Supervised Learning: Accuracy, Precision, Recall, F1 Score, ROC Curve
โข Performance Metrics for Unsupervised Learning: Cluster Purity, Normalized Mutual Information (NMI), Silhouette Score
โข Evaluating Deep Learning Models: Loss Functions, Confusion Matrix, Classification Report, ROC Curve
โข Evaluating Natural Language Processing (NLP) Models: Perplexity, BLEU Score, ROUGE Score, Named Entity Recognition (NER) Metrics
โข AI Metrics for Time Series Analysis: Autocorrelation, Partial Autocorrelation, MAPE, MAE, RMSE
โข AI Metrics for Computer Vision: Intersection over Union (IoU), Precision, Recall, F1 Score, AP Score
โข Ethical Considerations in AI Metrics: Bias and Fairness, Explainability, Privacy, Accountability
โข Best Practices for AI Metrics Implementation: Defining Metrics, Monitoring and Evaluation, Continuous Improvement
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