Fundamentals of AI & ML: Metrics & Evaluation

Artificial Intelligence 2023    |    Intermediate
  • 14 videos | 59m 6s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 2 users Rating 5.0 of 2 users (2)
Understanding model evaluation is crucial for making reliable, accurate, and ethical decisions when using artificial intelligence (AI) and machine learning (ML) in practical scenarios. In this course, you'll explore AI/ML model evaluation and interpretability in-depth, gaining a strong grasp of these essential components to make AI/ML work effectively for your organization. This course focuses on the key concepts and metrics needed to assess how well models perform. Understanding model evaluation is crucial for making reliable, accurate, and ethical decisions when using AI/ML in practical scenarios. Upon completing this course, you will be well-prepared to make informed decisions and maximize the potential of AI/ML within your organization.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    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
    Outline the concept of overfitting and its implications on model generalization
    Recognize the significance of confusion matrices in evaluating model performance
    Interpret receiver operating characteristic (roc) curves and area under the curve (auc) as indicators of model robustness
    Outline the challenges of bias and fairness in artificial intelligence (ai)/machine learning (ml) model evaluations
  • Analyze the trade-offs between false positives and false negatives in decision-making
    Outline the concept of cross-validation and its role in estimating model performance
    Identify techniques to handle class imbalances in classification tasks
    Outline the concept of interpretability in ai/ml models and its importance
    Recognize the significance of tracking performance over time and adapting models accordingly
    Recognize emerging trends in ai/ml evaluation, such as explainable ai and fairness auditing
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 53s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 13s
    After completing this video, you will be able to define common evaluation metrics for classification and their importance based on the nature of the problem. FREE ACCESS
  • Locked
    3.  Evaluation Metrics for Regression
    4m 8s
    Upon completion of this video, you will be able to define common evaluation metrics for regression and their importance. FREE ACCESS
  • Locked
    4.  Overfitting
    4m 52s
    After completing this video, you will be able to outline the concept of overfitting and its implications on model generalization. FREE ACCESS
  • Locked
    5.  Confusion Matrices
    6m 33s
    Upon completion of this video, you will be able to recognize the significance of confusion matrices in evaluating model performance. FREE ACCESS
  • Locked
    6.  ROC and AUC
    3m 28s
    After completing this video, you will be able to interpret receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) as indicators of model robustness. FREE ACCESS
  • Locked
    7.  Bias in Model Evaluation
    4m 21s
    Upon completion of this video, you will be able to outline the challenges of bias and fairness in artificial intelligence (AI)/machine learning (ML) model evaluations. FREE ACCESS
  • Locked
    8.  False Positives and False Negatives in Decision-making
    4m 50s
    After completing this video, you will be able to analyze the trade-offs between false positives and false negatives in decision-making. FREE ACCESS
  • Locked
    9.  Cross-validation
    4m 10s
    Upon completion of this video, you will be able to outline the concept of cross-validation and its role in estimating model performance. FREE ACCESS
  • Locked
    10.  Class Imbalances
    4m 6s
    After completing this video, you will be able to identify techniques to handle class imbalances in classification tasks. FREE ACCESS
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    11.  Interpretability in Artificial Intelligence (AI) and Machine Learning (ML)
    4m 46s
    Upon completion of this video, you will be able to outline the concept of interpretability in AI/ML models and its importance. FREE ACCESS
  • Locked
    12.  Performance Tracking
    4m 45s
    After completing this video, you will be able to recognize the significance of tracking performance over time and adapting models accordingly. FREE ACCESS
  • Locked
    13.  Emerging Trends in AI/ML Evaluation
    5m 28s
    Upon completion of this video, you will be able to recognize emerging trends in AI/ML evaluation, such as explainable AI and fairness auditing. FREE ACCESS
  • Locked
    14.  Course Summary
    34s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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