No concept is too difficult to understand if its explained in the way that it can be comprehended. Great job Luis! I keep coming back to your videos whenever I am stuck. You style of explanation with examples is amazing.
"So if you like formulas..." OMG! Thank you so much, Dr. Serrano. You helped my brain find the missing piece in my puzzle. The whole explanation was so clear but the formula helped me transition from Bayes to Naive Bayes. I was looking for the missing piece in youtube and somehow landed on your video. I actually came here after attending your AIND class.
I have to say, this is the best explanation of Naive Bayes on youtube. All other videos start off with the formula, while you started with an example then went on to the formulae. Keep posting videos man, you are one gem of a teacher!!
You have some of the best explanations of the topic in the entire business (literally hundreds of courses and YT series and stuff is out there). But you are the best
Can't thank you enough for this beautiful explanation on this kinda confusing topic. I mean, you didn't made any "naive" assumption about the watcher's background and explained it all in detail. Thanks a lot!
Thanks Luis. This was a lot easier to follow than most of my profs to be honest. The fact that you explained first and then put it in equation terms now helpsme remember the equation and understand it better. Many many thanks ! and God Bless
Maybe I missed this part in the video, but Naive Bayes assumes only conditional independence. For example, this training set suggests that the words "Buy" and "Cheap" are far from being (unconditionally) independent. Namely, P("Buy")=P("Cheap")=25/100=1/4. So, if the two words were independent, we would expect P("Buy" and "Cheap")=1/16=6.25%. However, there are 12 emails containing both words out of 100, which is 12%.
It's been around 8 months, I'm moving towards ML and your guidance, teaching strategy are playing major role in it. I can't simply say thank you. Stay blessed.
Just add the numerator with 'alpha' and denominator with 'k'*'alpha' for each class probabilities, where k is no of classes (here binary, so 2) and typically alpha is a hyper parameter (varies between 10^-3 to 1).
you are the coach who could teach students two weeks before the exams and get all students to get distinctions. I only wish if you took up Master classes.. Guys would be pouring to take up your master classes
I love these easy explanations though it's that easy I cant connect them to the formulas and stuff I read before. That would have been great if you've done that too.
Thank you very much for this video. I've spent days trying to work out intuition on how to apply the Naive Bayes for spam detection, but all other videos just repeat the Bayes probability formula and show you the answer. Formulas give you 0 understanding unless you figure out the logic behind the approach, and only then they become useful.
Gemini: This video is about Naive Bayes classifier, a spam detector which is based on Bayes theorem. The video uses an example of building a spam detector to illustrate the concept. The idea is that we can classify an email as spam or not spam based on the presence of certain words in the email. For instance, emails containing the word "buy" are more likely to be spam than those which do not contain "buy". Bayes theorem allows us to calculate the probability of an event (e.g. an email being spam) given another event (e.g. the email containing the word "buy"). The video uses a simple example with two properties (presence of "buy" and presence of cheap") to illustrate this concept. However, the challenge arises when we want to consider more than two properties at the same time. Ideally, we would like to calculate the probability of an email being spam given the presence of all the properties we are considering (e.g. "buy", "cheap", "work"). But calculating the probability of all these properties appearing together becomes cumbersome as the number of properties increases. This is where the Naive Bayes assumption comes in. Naive Bayes assumes that all these properties are independent of each other. This assumption although not always true, simplifies the calculation significantly. The video concludes by explaining how the Naive Bayes classifier works with this assumption and shows how to calculate the probability of an email being spam given multiple properties.
First I saw the video from 3b1b then from statquest. Both of them are great videos. But I was not able to find a connection between them. Your video helped me to connect all the dots
Thank you very very much, I would probably liked your video twice if it was possible. It's so clear and plain that after a while, I again came back to it for reviewing naive bayes.
This is the best explanation of Naive Baye’s & Baye’s theorem ... you rocked it ... Thanks for this
Great Video!
This is explained so well, this video is so beautiful that I want to cry
same here
I am crying as I type this line...... *snif* .....so good !
No concept is too difficult to understand if its explained in the way that it can be comprehended. Great job Luis! I keep coming back to your videos whenever I am stuck. You style of explanation with examples is amazing.
"So if you like formulas..." OMG! Thank you so much, Dr. Serrano. You helped my brain find the missing piece in my puzzle. The whole explanation was so clear but the formula helped me transition from Bayes to Naive Bayes. I was looking for the missing piece in youtube and somehow landed on your video. I actually came here after attending your AIND class.
Beautiful Luis. You clearly draw the distinction between an educator and an instructor.
I have to say, this is the best explanation of Naive Bayes on youtube. All other videos start off with the formula, while you started with an example then went on to the formulae. Keep posting videos man, you are one gem of a teacher!!
Without any doubt the best explanation I've ever seen on Naive Bayes. Thank you
The two people who disliked were looking for baes, but got Bayes.
haha! Imagine how they would have felt by end of the video!
they were naive.
😂
You have some of the best explanations of the topic in the entire business (literally hundreds of courses and YT series and stuff is out there). But you are the best
Can't thank you enough for this beautiful explanation on this kinda confusing topic. I mean, you didn't made any "naive" assumption about the watcher's background and explained it all in detail.
Thanks a lot!
Beautiful and easy. Gentle speaking but excellent teaching ability. Luis Serrano, thank you!
Thanks Luis. This was a lot easier to follow than most of my profs to be honest. The fact that you explained first and then put it in equation terms now helpsme remember the equation and understand it better. Many many thanks ! and God Bless
Wow ... I love the way you present this topic. Thank you very much.
best Explanation. This Channel is a hidden gem
indeed! best explanation of Naive Baye’s & Baye’s theorem
This is the simplest and most effective video on NB
Maybe I missed this part in the video, but Naive Bayes assumes only conditional independence. For example, this training set suggests that the words "Buy" and "Cheap" are far from being (unconditionally) independent. Namely, P("Buy")=P("Cheap")=25/100=1/4. So, if the two words were independent, we would expect P("Buy" and "Cheap")=1/16=6.25%. However, there are 12 emails containing both words out of 100, which is 12%.
I have exactly the same remark
Great explanation...It was very easy to understand Naive Bayes ...Thank you very much for this video...!!
Great explanation , perfectly paced.
The best explanation ever of Naïve Bayes
This was a very clear and beginner-friendly explanation. Thank you!
Absolutely fantastic explaination. Thank you so much for this.
It's been around 8 months, I'm moving towards ML and your guidance, teaching strategy are playing major role in it.
I can't simply say thank you.
Stay blessed.
Thank you, that's really nice to hear! Keep up the good work in ML!
Thank you! This is so much more clear than my textbook!
Great Explanation for Bayes Theorem, I have never understood naive Bayes so well....Thanks for this Luis
you made this so easy to understand, thank you!
thank you for excellent explanation !
You Sir are a hero!
An excellent explanation.
I must say thank you so much for this fantastic upload..
Great Beginning!!
Such a brilliant explanation. Thank you Luis. Kindly add more lectures on traditional ML related topics.
Great explanation! Thanks!
Wonderful explanations !!
Would love to see you explaining Kalmanfilter
Thank You for this video. You are an inspiration ❤️
Awesome explanation 👍 👍👍👏👏👏
Great explanation!
Thanks for the episode
you're such a legend, thank you for this video !
Kindly Make a video on Expectation Maximization
Beautiful and Effective.
Thank you!
Awesome session.. Thanks a million!! God Bless..
You brother! have my respect.
This is the really the best explanation of naive bayes...that beats even Andrew Ng's and many other's...
so much more clearer than my professor explaining it for 80 minutes
Thanks A LOT! great video!
Great explanation
You're crushing it!
thankyou so much for this !!
It was gold! thanks
Awesome explanation...👍
Hey Luis, can you explain bayes theorem with laplace corrections applied to it when, cond prob is zero
Just add the numerator with 'alpha' and denominator with 'k'*'alpha' for each class probabilities, where k is no of classes (here binary, so 2) and typically alpha is a hyper parameter (varies between 10^-3 to 1).
very nice explanation, can you please mention how to find the naive Bayesian classifier's uncertainty?
you are the coach who could teach students two weeks before the exams and get all students to get distinctions. I only wish if you took up Master classes.. Guys would be pouring to take up your master classes
I love these easy explanations though it's that easy I cant connect them to the formulas and stuff I read before. That would have been great if you've done that too.
Explained with great simplicity, thanks for this!
I think the probabilities you picked might be a bit confusing, didactically: P(S|B) happens to be equal to P(B|S) in the Bayes formula at 17min.
This video is SOLID!!! Thank you so much for this and please keep making more videos! You made this concept so digestible it is not even funny.
Fantastic explanation! Such amazing teaching skills! I wish every teacher was like you! Great work and thank you!
Finally, I noticed that there is a difference between Bayes theorem and Naive Bayes.
This is really good! Thank you so much for your time and effort to make this topics accessible to the masses 🙂
Perfect!
Great video dude, helped a lot. Thank U.
the best of best
Thanks a lot
Thank you very much for this video. I've spent days trying to work out intuition on how to apply the Naive Bayes for spam detection, but all other videos just repeat the Bayes probability formula and show you the answer. Formulas give you 0 understanding unless you figure out the logic behind the approach, and only then they become useful.
Thank you, Luis. Your classes are amazing, keep the good work.
Best regards from Brazil.
Came here from "codebasics" youtube channel.
pretty amazingly explained by you man.. Thanks a lot..
Gemini: This video is about Naive Bayes classifier, a spam detector which is based on Bayes theorem.
The video uses an example of building a spam detector to illustrate the concept. The idea is that we can classify an email as spam or not spam based on the presence of certain words in the email. For instance, emails containing the word "buy" are more likely to be spam than those which do not contain "buy".
Bayes theorem allows us to calculate the probability of an event (e.g. an email being spam) given another event (e.g. the email containing the word "buy"). The video uses a simple example with two properties (presence of "buy" and presence of cheap") to illustrate this concept.
However, the challenge arises when we want to consider more than two properties at the same time. Ideally, we would like to calculate the probability of an email being spam given the presence of all the properties we are considering (e.g. "buy", "cheap", "work").
But calculating the probability of all these properties appearing together becomes cumbersome as the number of properties increases. This is where the Naive Bayes assumption comes in. Naive Bayes assumes that all these properties are independent of each other. This assumption although not always true, simplifies the calculation significantly.
The video concludes by explaining how the Naive Bayes classifier works with this assumption and shows how to calculate the probability of an email being spam given multiple properties.
Thanks for your detailed and friendly explanation. It really helps me a lot :)
Thank you!
Amazing breakdown! I liked how you made it visually first and gradually turned it into the formula. That really made it click in my head!
Really amazing sir❤ love from India, watching you videos on 3G internet 😅
your video should be the first result of "naive bayes"
Great Explanation! Can someone please explain how we get for P(B/S) = 20/25? @ 17:45
Thank You for Such Clear and Well structured Explanation!.
Appreciate the way (visualization) you explained a more complicated concept.
Very well done thank you
First I saw the video from 3b1b then from statquest. Both of them are great videos. But I was not able to find a connection between them. Your video helped me to connect all the dots
This is really FRIENDLY. Thank you!
Holy, what a good explanation of the concept, damn dude. Thanks alot!
First time ever I understood this Naive Bayes. Thank you so much
How 10% of 5% is equal to 0.5?
Thank you so much for this amazing explanation
Explained intuitively! Thanks! :)
Really great 🙏 my understanding is much better now 😊
no thank you Luis you are an excellent educator
awesome explanation. thank you very much :)
Why is it assumed that 0.5% of the words contain buy and cheap
amazing tutorial! very easy to follow!
1:31 I counted 80 non spam mails, you should have chopped off one column there!
At 6:55 how did you conclude 0.5% ? Please make it clear
wow, that was wonderful explanation. thatnks!
Awesome!
Best Naive Bayes explanation ever! thank U Luis
Thank you very very much, I would probably liked your video twice if it was possible. It's so clear and plain that after a while, I again came back to it for reviewing naive bayes.
Thank you so much for this video, Luis! Respect!
PLEASE COULD YOU SEND LINK TO DOWNLOAD THIS PRESENTATION?
Excellent explanation. Thank you 🙏