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Regression Is Not a Formula. It’s a Derivation.
Most Machine Learning content today focuses on usage,
which library to import, which function to call, which parameter to tune.

Very little time is spent on origins.

In this lecture, I go back to first principles and build Linear Regression and Binary Logistic Regression from scratch, starting with Maximum Likelihood Estimation.

No shortcuts.
No memorized loss functions.
No “just trust the intuition”.

You’ll see:
• How probability becomes optimization
• Why the loss function has the form it does
• How Linear and Logistic Regression emerge naturally from MLE
• What most tutorials silently skip

This is not surface-level ML.
This is the mathematical backbone behind the models we use every day.

If you’re serious about Machine Learning,
not just applying models, but understanding them,
this content is for you.

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