We review how numpy arrays are the lingua franca of sklearn, PyTorch tensors as numpy cousins, and typical flows: X as float32, y as integer labels, train-test split as views or copies, and feature scaling as element-wise or per-column operations. The lesson prepares you to read documentation that says array-like and to debug shape bugs before they reach a training loop.
