Track
Data science from zero
From a train/test split to transformers, in the order each page needs.
Steps in order
- 01Train-test splitWithhold rows from training so the score you report was measured on data the model never saw. The split has to come first, be large enough, and respect how the rows arrive.Free
- 02MSEThe average of the squared differences between predicted and actual values — the standard way to measure how wrong a regression model's predictions are.Free
- 03Linear regressionA model that predicts a numeric outcome as a weighted sum of the input features, fitted by choosing the weights that make the squared prediction errors as small as possible.Free
- 04Gradient descentRepeatedly step in the direction the loss decreases fastest, by an amount set by the learning rate — the algorithm underneath almost all model training.Free
- 05OverfittingFitting the training sample more closely than the process that generated it — so the model reproduces noise that will not recur, and does worse on new data the better it looks on old.
- 06RegularizationAdding a penalty on coefficient size to a model's objective, so it trades a little fit on the training data for far better behaviour on data it has not seen.
- 07Cross-validationSplitting the data k ways and letting each part be the test set once, so every sample is predicted by a model that never saw it — and the estimate uses all the data instead of a single lucky split.
- 08Decision treeA model that predicts by asking a sequence of yes/no questions about the features, splitting the data at each answer until it reaches a leaf holding a prediction.
- 09Random forestBagged decision trees with a second source of randomness — each split may only consider a random subset of the features, which decorrelates the trees and lets averaging do far more work.
- 10PerceptronThe simplest neural unit — it multiplies each input by a weight, adds them up with a bias, and fires 1 if the total clears a threshold and 0 if it doesn't.Free
- 11BackpropagationThe chain rule applied backwards through a computational graph. One traversal produces the derivative with respect to every parameter, at roughly the cost of one forward pass.
- 12TransformerA stack of identical layers, each self-attention followed by a position-wise feed-forward network, both wrapped in a residual connection and layer normalisation, with word order supplied separately.