How a machine actually learns

From data to a trained model you evaluated honestly.

1. Features and labels Turn a real problem into a table a model can learn from.
2. Loss: how wrong is the model? Explain what a loss function measures and why training minimises it.
3. Gradient descent Describe how a model walks downhill to better parameters.
4. Train/test split, and why it is non-negotiable Evaluate a model on data it has never seen.
5. Overfitting and underfitting Diagnose which of the two you have from train vs test performance.
6. Train your first model with scikit-learn Train, predict and evaluate an actual model end to end.

Ends with a graded checkpoint: 10 questions, pass mark 70%.