data-science-foundations
Flexibility vs Interpretability: Finding Your Model’s Sweet Spot
Complex models can capture wiggly patterns but often speak gibberish to humans. Here’s how to balance predictive power with the need to explain ‘why’.
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data-science-foundations
How Do We Estimate f? Turning Data into a Working Rule
From choosing a loss function to running gradient descent and validating with cross-validation—this guide walks beginners through the nuts and bolts of training a model.
data-science-foundations
Prediction vs Inference: Asking the Right Question
Before choosing an algorithm, choose your goal. Here’s how to tell if you need the right number or the right story—and why that choice changes everything that follows.
