✦Sparkl
builder · learns

Model Evaluation: How to Judge AI Fairness and Performance

Learn to measure AI accuracy, spot bias, and audit machine learning models like a pro. Master the metrics that keep AI fair and reliable.

20 modules·Difficulty: ★★★★☆· 5 Free
Start module 1

Modules

  • 1
    Free8 min
    Welcome to Model Evaluation
    Discover why judging AI is just as important as building it 🎯
  • 2
    Free10 min
    Understanding the Confusion Matrix
    Learn how a simple grid reveals all four types of AI prediction outcomes.
  • 3
    Free9 min
    True Positives and False Alarms
    Explore how AI gets things right and when it cries wolf.
  • 4
    Free11 min
    Accuracy Is Not Enough
    See why a model can be accurate yet still unfair or useless.
  • 5
    Free10 min
    Precision: How Often Is AI Correct?
    Calculate the fraction of positive predictions that are actually true.
  • 6
    Paid12 min
    Recall: How Much Does AI Miss?
    Measure the fraction of real positives the model successfully finds.
  • 7
    Paid11 min
    The Precision-Recall Trade-Off
    Understand why boosting one metric often hurts the other.
  • 8
    Paid10 min
    F1-Score: The Balanced Metric
    Combine precision and recall into a single fair performance score.
  • 9
    Paid9 min
    Building a Mini Spam Detector
    Apply precision and recall to evaluate a simple email classifier.
  • 10
    Paid12 min
    Class Imbalance: When Data Is Skewed
    Learn how rare events break accuracy and demand smarter metrics.
  • 11
    Paid11 min
    ROC Curves: Plotting Performance
    Visualize the balance between true positives and false positives.
  • 12
    Paid10 min
    AUC: The Area Under the Curve
    Compare classifiers with a single number that summarizes ROC performance.
  • 13
    Paid12 min
    Threshold Tuning: Finding the Sweet Spot
    Adjust the decision boundary to match your real-world priorities.
  • 14
    Paid11 min
    Bias in AI: What Can Go Wrong
    Discover how unfair training data leads to discriminatory predictions.
  • 15
    Paid10 min
    Fairness Metrics: Measuring Equity
    Use demographic parity and equalized odds to check for bias.
  • 16
    Paid12 min
    Cross-Validation: Testing on Fresh Data
    Split data smartly to prevent overfitting and get honest metrics.
  • 17
    Paid11 min
    Documenting Your Evaluation
    Write clear reports that explain metrics, trade-offs, and fairness findings.
  • 18
    Paid10 min
    Real-World Case Study: Medical Diagnosis AI
    Analyze how a hospital evaluates an AI tool for detecting disease.
  • 19
    Paid12 min
    Capstone: Audit a Classifier for Bias
    Conduct a full evaluation report on a practice model using all learned techniques.
  • 20
    Final exam25 min
    Final Exam: Model Evaluation Mastery
    Prove your skills by answering questions on metrics, fairness, and real-world scenarios.