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We use Mean Squared Error (MSE) to train Linear Regression models. But why? Why do we square the error? Why don't we cube it? The answer isn't just "because it works." The answer comes from Probability. In this video, we decode Maximum Likelihood Estimation (MLE). We will prove—using Math and Python code—that finding the "Most Likely" model ...
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