Lesson Focus
What this lesson covers
Once data is prepared, models must be built and evaluated with rigor. This episode covers the three pillars of evaluation: training, validation, and testing. Training introduces the algorithm to data, refining weights and parameters over multiple epochs. Validation checks progress midstream, guiding hyperparameter tuning and preventing overfitting. Testing provides the final check, using unseen data to confirm performance. Listeners will learn about accuracy, precision, recall, F1 scores, and regression metrics as ways to measure effectiveness.