Maximum Likelihood : Data Science Concepts
ฝัง
- เผยแพร่เมื่อ 28 ก.ย. 2024
- The story behind max likelihood .... fully explained!
Sigmoid Video : • The Sigmoid : Data Sci...
Logistic Regression Video : • Logistic Regression - ...
My Patreon : www.patreon.co...
![[🔴LIVE] งานประกาศรางวัล MAYA TV AWARDS 2024 : มายามหาชน](http://i.ytimg.com/vi/nwmWYXXVwBY/mqdefault.jpg)
Undeniably you're creating some of the best videos concerning statistics out there. And this is another one. Thank you so much
this is art
wow thanks!
As always, great videos! I mainly use them as supplement to masters level courses. What I love are the parts where you cover the intuitions about the formulas, it makes them so much more understandable. Keep up the good work!
Thank you so much for preferring to actually help people understand concepts rather than throwing fancy multi-step jargon that gets people frustrated at math topics. I would do anything I can to help keep the channel alive and I will recommend your channel to all my friends and colleagues. Thanks again and keep the excellent work.
Love your videos! Very nice to revise + learn new things, not missing on intuition either. Hope, your followers number will soar soon.
Man I watch your videos all the time. I can write the code and understand the outcome but you’re the only way I can understand the math. Thanks!
You are a brilliant teacher - thanks so much for doing this
I swear to god, when I was searching for maximum likelihood estimation, I was hoping you would make a video on this, scrolled down a bit and bingo!! I see Rithvik Math. My happiness knows no bounds. Thank you for existing.
Thank you very much. Been struggling to get the intuition of MLE and you helped me to understand it. Would be awesome if you could do a video on GMM estimation. Thank you!!!
this was a very beautiful lecture
Thank you for the video! I wanted to say they are great for revision, but I usually learn a lot more than I did in school on the topic... so thank you!
Just Brilliant! thanks for demystifying logistic regression equations for me🤝
Great stuff. I'm taking an online MIT class. Complicated topic? I come to your channel to solidify my understanding!
Glad it was helpful!
excellently explained. Very clear and logical! Tks !
Have you ever done a video on Maximum a Posteriori (MAP)?
Thanks for making things seem to be quite simpler ☺
Thanks for these lectures. You're great at explaining this stuff. Keep it up!
excellent explanation, very good as an introduction. Can you make something which delves into maths even more? Explanation of the last part it necessary and would be fun to understand. :)
very well explained. I am gaining confidence by these fundamentals lectures.
Amazing video. But during the whole time, I was distracted by figuring out what number series is on the tattoo. :D
Thanks for the great lecture. I really liked the word "seeing" outcomes.
Isn't likelihood should be inverse to probability P(y|x,beta), i.e. L(beta|x,y)?
exactly my thoughts. From what I read from other sources, Likelihood is given by L(parameters or distribution|observed data). Perhaps @ritvikmath can explain better
WOW! Thank you!
keep putting out your bamgers. Use them to learn how to communicate concepts. Shared this one with my network
Thanks for the video, will you be able to guide to a reference about why EM converges to a maximum, either local or global?
In the logistic regression part on the left, its probably better to callout that probability p is not the same as number of data points p.
Amazing! Amazing! thanks a lot
thank you ! these videos are good refresher
Question: In 8:03, you're introducing the conceptions of the likelihood, which from my understanding is the probability of real observation emerging given the y; but in the formula, it looks like the posterior probability, not likelihood, is there something missing?
Amazing Content. Thanks
My pleasure!
This is truly amazing!!!
I am confused. You are saying the probability and likelihood are the same according to what you have written by your equations. For likelihood are you not trying to say given the acceptance into med school the likelihood of having these values for gpa, mcat score etc…For instance if probability is P(y|x) then likelihood is L(x|y)? You have these two being equal.
The Gods of Statistics finally decided to send us a prophet :)
100%
He us a magician
I don’t understand how smoothly he makes the concept sweep into one’s brain
What a guy! Amazing stuff
This guy makes Machine Learning easy bro, subscribed
When the derivative is 0, it could also be a local minimum right? How to assure when the derivative is 0 then the likelihood is maximized?
while it's true that derivative=0 could mean min or max, we can distinguish since a min has a decreasing gradient on the left and increasing gradient on the right. a max is the opposite. hope that helps!
@@ritvikmath thanks! I got it.
Neat 'Mathematese'...
Wow that t-shirt ❣️❣️❣️ cool 😎
The collection of info *chefs kiss*
What should be the probability P(y_i| x_i, beta) where actual y_i is reject(= 0). If P(y_i| x_i, beta) is close to 0 or 0. Max Likelihood estimator will not be max even though beta parameters are fine tuned.
Just 3K views on this? people are crazy. This deserves wayyyy more.
Can I know what is the difference between log likelihood vs negative log likelihood graphically ? How do I choose which of the loss functions?
Great video, just did not understand where the sigma came from?
Your videos are absolutely helpful!! You're soooo damn good teacher and really good at delivering the complicated lessons into the easier way to digest. I hope I can pass this semester with your videos bc without your video it would be even worse! 😂 THANK A MILLION ❤️
would the likelihood function be between 0 - 1?
Hmmm how u bind the Yi to the sigmoid function..
Thank you!!!
L
Gold!
Please marry me😍😂😂. I can listen to you forever. Such a passionate teacher!
Amazing style of teaching. Thank you!
You're an awesome teacher, thanks a lot!
I love your videos.
Great & helpful video ! From 12:40 says : "This's the probability of seeing all the real world outcomes that i actually see in my data". I think its better to replace "real world" with "predicted" or other synonym since the former kind of mislead the viewers to think those "outcomes" are recording of happened event which actually not, they stem from the model prediction, it's the "data"(y:nx1) that recorded the real result. and from 20:05 that's the correct way to express it.
In English most people use the terms Probability and Likelihood interchangeably - I can't help but correct this when I hear it. One nuance is that the Maximum Likelihood result is often very small, meaning the parameter value isn't very likely, it is just more likely than the alternatives. Ranked Choice Voting is designed to promote the Most Likable Choice past the 50% threshold. Great video as always; I hope you become a stats and data science professor!