Lessons
1
Why linear regression fails for classification
2
The sigmoid function
3
Logistic regression
4
Cross-entropy loss
5
The decision boundary
6
Multi-class: softmax
7
Confusion matrices and class metrics
8
ROC curves and AUC
9
Class imbalance and weighted loss