Learn the essential concepts of evaluating machine learning models. This course covers fundamental metrics for classification and regression tasks, explaining how to interpret results and understand model performance. Start building a solid foundation for assessing AI models.
Prerequisites Basic ML concepts
Level:
Model Evaluation Purpose
Understand why evaluating machine learning models is essential for development.
Classification Metrics
Identify and calculate fundamental metrics like accuracy, precision, recall, and F1-score.
Regression Metrics
Identify and calculate fundamental metrics like MAE, MSE, RMSE, and R-squared.
Confusion Matrix
Interpret a confusion matrix to understand classification performance.
Data Splitting
Learn the importance of train/test splits for reliable model evaluation.
Basic Evaluation Workflow
Apply a simple workflow to evaluate a model using appropriate metrics.