Gradient descent: Repeatedly nudging a model's parameters downhill on the cost function to reduce error.
Leads to
Classification evaluation metrics: Accuracy alone can lie; precision, recall, F1, and ROC-AUC each capture a different way a classifier can be right or wrong.
Support vector machines: A classifier that finds the widest possible margin between classes, and can bend that margin into curves using kernels.
Neural network basics: Stacking layers of logistic-regression-like units, connected by non-linear activation functions, to learn more complex patterns.
Related topics
Classification evaluation metrics: Accuracy alone can lie; precision, recall, F1, and ROC-AUC each capture a different way a classifier can be right or wrong.
Support vector machines: A classifier that finds the widest possible margin between classes, and can bend that margin into curves using kernels.