Fundamentals of AI and ML Proficiency (Advanced Level)

Topics covered

• analyze the trade-offs between false positives and false negatives in decision-making
• define common evaluation metrics for classification and their importance based on the nature of the problem
• define common evaluation metrics for regression and their importance
• identify techniques to handle class imbalances in classification tasks
• illustrate the potential impact of AI on job roles and workforce dynamics
• interpret receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) as indicators of model robustness
• outline how data science (DS), machine learning (ML), and AI are relevant in the modern business landscape
• outline the benefits and challenges associated with integrating AI and ML into business approaches
• outline the challenges of bias and fairness in artificial intelligence (AI)/machine learning (ML) model evaluations
• outline the concept of cross-validation and its role in estimating model performance
• outline the concept of interpretability in AI/ML models and its importance
• outline the concept of overfitting and its implications on model generalization
• recognize emerging trends in AI/ML evaluation, such as explainable AI and fairness auditing
• recognize the key differences between AI and traditional programming approaches
• recognize the significance of confusion matrices in evaluating model performance
• recognize the significance of tracking performance over time and adapting models accordingly