LOGISTIC REGRESSION TUTORIAL

where's the application video?

thanks man

Very very nice explanation

That was wonderful, informative and interesting demonstration. Thank you so much!

Thank you for making it easy to understand this concept. How do we fit this sigmoid curve for best fit, do we have to change its shape, or give it offset ? Some sort of that example would have made it more interesting. Thanks alot again. It was quite helpful.

That was a very good explanation! Thanks

So smart way to explain this question in my mind for a long time. Thank you!

Best video thank you so much! I hope some other concepts of Machine Learning in the upcoming days

Great Explanation

You are a Good Tutor but content is very Basic ..
No Solved Examples ,,, Purpose not solved.
Please also add Learning Outcomes at the beginning so that we can save our time.

I was just as confused as you stated you were when I was first introduced to this topic. You gave me a eureka moment and suddenly it is clear. Thank you.

BEST explanation. Thank you

great explanation...

so satisfied

This was a great explanation

Nice graphics, but if I was seeing it without prior knowledge, I would be deeply confused by what appears to be a plot of ln (p/(1-p)) on the Y axis. While p is forced to follow the sigmoid shape, the ln (odds) range from negative to positive infinity, and the prediction will be a straight line, not a curve. So I cannot recommend this video without a clear explanation of that step.

Very good but i wished that you work more with math and ln(p/1-p) etc

I have worked with logit models a lot (binomial, multinomial, ordered). This video is so useful and simply expressing the transition from linear regression to logistic one. Thanks.

You completely failed to explain the mathematics behind it and how to obtain a function of probability from some example data points.

It was a very good tutorial. Thanks a lot Kirill for that. Been always a follower of your classes. Have a small query. Being a beginner into data science field, do I need to know how does the mathematical functions like sigmoid function or the MLE works mathematically in Logistic regression? Or will the information imparted in this video related to Log. Reg. is sufficient enough to sustain in this field? Please suggest. P.S. The mathematical derivations seems to be a bit complicated

Awesome explanations. Thanks for creating such a excellent presenation with clear explanation.

So well explained thanks. I liked the part that you said "So this is a model if front of you right there" :)))

Speaking about linear regression, presented here: how can experience be negative? Please, check the plot again.

Thanks for the nutshell video. Great way to refresh my knowledge :)

Excellent, thank you.

This is nice and simple explanation of a complicated problem...

Great video! It was very helpful and gave me some intuition on logistic regression. Thank you.

Hi, Thanks so much. It's nice, clear and easy to follow. Waiting for your next video..

A link to the next video in the description would be a nice touch, since you're referring to it by the end of the video ;)
Thanks for the video. And good job on the voice, it's not easy to do, and you're very easy to follow.

Very much simplified good explanation . Thanks. I would love to see more machine learning algorithms.

Why do we need to use sigmoid function only?

Good simplified explanation

I get it now. Thank you so much for this excellent tutorial!

Very good and clear explanation. Thanks.