Classification
Overall Progress
0%
Predict categories instead of numbers. Decision boundaries, sigmoid activation, and binary probability outputs.
Binary classifier from scratch: sigmoid + cross-entropy loss + gradient update. The building block of softmax policies.
Classify new points by majority vote among K closest training examples.
If/else questions on features, entropy, and information gain as splitting criteria.
Build a 2-layer MLP to classify handwritten digits using only NumPy. Full pipeline: data, init, training, evaluation.
End-to-end ML project combining loading, exploration, preprocessing, training, and evaluation.