Fundamentals of AI and ML Proficiency (Advanced Level)

  • 16m
  • 16 questions
The Fundamentals of AI and ML Proficiency (Advanced Level) benchmark measures your knowledge of the key concepts and use cases of artificial intelligence (AI). You will be evaluated on your ability to outline strategies for each part of the AI life cycle, assess the performance of AI/ML models, and recognize the metrics used to measure success. A learner who scores high on this benchmark demonstrates that they have a deeper grasp on the fundamentals of AI and ML and can start working on the AI/ML projects.

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