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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[karimafifi]

Best explanation on the internet. Thank you.

[gisellcelis]

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

[hetav]

Damn this guy is awesome in explaining stuff. Really good work mate!

[seb]

I need more videos man!!
Awesome work. Please post more videos or helpfull links that explain as well as you do!

[amargupta]

i have searched so many explanation but finally understood by your videos. Thank

[lieutanant]

I'm wondering how the hell this is filmed. Some mirror magic or does he actually write backwards?

[abdulgadirhussein]

Great video dude and very neatly explained. Please keep up the great!

[pratikd]

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

[nazrulhassan]

just fabulous explanation there are tons of videos on logistic regression but most of them gloss over the mathematical details.

[TheElementFive]

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

[tuannguyenxuan]

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

[batatambor]

When we are talking about linear regressions we normally have to satisfy a few assumptions like (linearity, normality of error, homoskedasticity and etc). Do these conditions also have to hold in order to perform a logistic regression? You have after all a kind a of a linear regression inside the sigmund function, right?

[heesukson]

This is the best explanation ever. Thx!

[kamrangurbanov]

That was really helpful. Thank you very much

[igormishurov]

Please come out with new lessons! Very clear and cool!

[majedalhajjaj]

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[yoyoIITian]

Wow! Nice explanation. Thank you so much.

[abdulkareemridwan]

Endless engineering.. Fantastic work.. You Explaining everybit... really appreciate

[Felicidade]

subscribed because this was so good :D