Logistic Regression with Maximum Likelihood

Logistic regression is a statistical model that predicts the probability that a random variable belongs to a certain category or class. In this video we use the Sigmoid function to form our hypothesis (statistical model). After that we form our likelihood function as a Bernoulli distribution given a data set, and using the maximum likelihood estimation method the model parameters are estimated using the gradient ascent algorithm.
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[@gisellcelis5375]

I have been looking for a good explanation in books and other videos but I couldn't understand this topic until I found your video. Thank you! :)

[@RanCui]

This is so far the best video I've watched on youtube that explains this topic

[@amargupta7123]

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[@karimafifi5501]

Best explanation on the internet. Thank you.

[@kamrangurbanov4364]

That was really helpful. Thank you very much

[@tuannguyenxuan8919]

Thank you ! This video help me a lot to understand what MLE in logistic does

[@seb2118]

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[@pratikd5882]

Thanks a ton!!, you explained it so well !! Please keep making videos on ML math topics.

[@patrickbyamasu1353]

This is a very good explanation of the math behind Logistic Regression I ever seen!

[@nizardwicahyo76]

Thank you, dir! Great explain!

[@yoyoIITian]

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[@heesukson3581]

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[@Felicidade101]

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[@drachenschlachter6946]

Beste video on KZbin to this topic!

[@TheElementFive]

Just enough math to capture the intuition behind the algorithm. You got yourself a sub sir.

[@salmanashraf2153]

it was easily explained and convenient to comprehend. thanks