Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Derivations for Math Lovers (Optional): Lo

The video tutorial explains logistic regression, focusing on the likelihood function and its optimization using the log likelihood. It discusses the relationship between log likelihood and cross entropy loss, and derives the error function. The tutorial concludes with the necessity of gradient descent for parameter estimation in logistic regression due to the absence of a closed form solution.

What does it mean to maximize the log likelihood function?

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How does the average log likelihood relate to the overall likelihood function?

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What challenges arise when trying to find a closed-form solution for W in logistic regression?

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In what scenarios is gradient descent used in logistic regression?

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