model.fit(X, y) is one line, and that is the problem. scikit-learn is excellent, but reaching for it before you understand what it does leaves you able to call models without being able to reason about them. Build a few core pieces by hand first, and the rest of machine learning becomes readable.
1. Linear regression
Start here because everything else generalizes from it. A linear model is just prediction = weights . features + bias. Building it teaches you what a model parameter is and what "fitting" means: finding the weights that make predictions close to the truth.
def predict(x, w, b):
return sum(wi * xi for wi, xi in zip(w, x)) + b
