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
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.
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.
"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.
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.
This guy is not normal teacher. He is a legend and genius. He has a way to transfer the knowledge and explains the concepts in very very simple way. Thank you so much for every single second you spent making this video. Please try to make more videos and publish books.
Thank you for such a kind message! It's people like you who give me energy to continue making content. I'm have a bunch of videos coming out in the next few weeks, keep an eye!
@@SerranoAcademy I finished my PhD and I am preparing for job interview in ML. I spent too much understanding the intuition behind NB until I found your masterpiece. I promise if I am hired, I will buy your books.
I hope if you can make a series for LLMs from scratch. I haven't found anyone who has made a LLM content for absolute beginners. In the meantime, there is a big hype around LLMs. Thank you so much for your valuable time.
This is the best explanation of Naive Bayes I have seen or read. Better than Bishop. It starts with a crystal clear, intuitive example and concludes with a thorough explanation of how to apply Bayes' theorem. Thank you so much for this.
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!!
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!
Top notch explanation Luis ! In 20 minutes I went from mindlessly plugging in values into a formula to an intuitive understanding of how the algorithm and its calculations work. Thank you !
Muchísimas gracias, profesor Serrano! =) I've seen many explanations in UA-cam regarding naive bayes, most of them from channels that I really appreciate, but your explanation is the best one by far. Thank you so much for making the link between the logic and the bayes formula!
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
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%.
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 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
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.
Very Nice Explanation and visual representation of the "Naive Bays" algorithm. Thanks, Luis Sir for this video. It really clears the concepts. Now, a little request is to make video on "Decision Tree" and "Random Forrest" like the same in visual representation.
This was great ... I'm just starting out studying prob/stats .... and I think this will go a long way to overcome some of the hurdles I was having get past the Bayes theory. Now I am ready to hit the books again.
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.
Amazing explaination so far .. I am watching this in morning and you literally made my morning ... I have one question (after that I would understand it perfectly).. After training your Naive Bayes model, the output of the whole model is in terms of probability, ie, P(S/(buy and cheap)) and P(not S / (buy and cheap)).. right ? if yes, what happens in testing phase of the naive bayes model, I mean how this works during testing phase.. eagerly waiting for your reply sir :)
This is the best explanation of Naive Baye’s & Baye’s theorem ... you rocked it ... Thanks for this
Great Video!
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
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.
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.
"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.
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!
Beautiful Luis. You clearly draw the distinction between an educator and an instructor.
This guy is not normal teacher. He is a legend and genius. He has a way to transfer the knowledge and explains the concepts in very very simple way. Thank you so much for every single second you spent making this video. Please try to make more videos and publish books.
Thank you for such a kind message! It's people like you who give me energy to continue making content. I'm have a bunch of videos coming out in the next few weeks, keep an eye!
@@SerranoAcademy I finished my PhD and I am preparing for job interview in ML. I spent too much understanding the intuition behind NB until I found your masterpiece. I promise if I am hired, I will buy your books.
I hope if you can make a series for LLMs from scratch. I haven't found anyone who has made a LLM content for absolute beginners. In the meantime, there is a big hype around LLMs. Thank you so much for your valuable time.
@@hamzawi2752 Thanks! I made this playlist about LLMs and attention, check it out! ua-cam.com/play/PLs8w1Cdi-zva4fwKkl9EK13siFvL9Wewf.html
This is the best explanation of Naive Bayes I have seen or read. Better than Bishop. It starts with a crystal clear, intuitive example and concludes with a thorough explanation of how to apply Bayes' theorem. Thank you so much for this.
Thomas Bayes wouldn’t have explained it better !! Thank you for this explanation 👏🏼👏🏼👏🏼
This is the simplest and most effective video on NB
Such a brilliant explanation. Thank you Luis. Kindly add more lectures on traditional ML related topics.
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!!
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!
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.
😂
Great Explanation for Bayes Theorem, I have never understood naive Bayes so well....Thanks for this Luis
Top notch explanation Luis ! In 20 minutes I went from mindlessly plugging in values into a formula to an intuitive understanding of how the algorithm and its calculations work. Thank you !
Without any doubt the best explanation I've ever seen on Naive Bayes. Thank you
Muchísimas gracias, profesor Serrano! =)
I've seen many explanations in UA-cam regarding naive bayes, most of them from channels that I really appreciate, but your explanation is the best one by far. Thank you so much for making the link between the logic and the bayes formula!
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
Beautiful and easy. Gentle speaking but excellent teaching ability. Luis Serrano, thank you!
First time ever I understood this Naive Bayes. Thank you so much
Appreciate the way (visualization) you explained a more complicated concept.
This is the really the best explanation of naive bayes...that beats even Andrew Ng's and many other's...
best Explanation. This Channel is a hidden gem
I got a very clear understanding of Bayes and Naive Bayes - you are a great story teller! Thank you!
so much more clearer than my professor explaining it for 80 minutes
one of the best videos on Naive Bayes
Thank you, Luis. Your classes are amazing, keep the good work.
Best regards from Brazil.
Fantastic explanation! Such amazing teaching skills! I wish every teacher was like you! Great work and thank you!
Dude, you're a life saver! This is by far the most clearly explained video out there on youtube.
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
Came here from "codebasics" youtube channel.
pretty amazingly explained by you man.. Thanks a lot..
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!
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 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
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 was AWESOMELY EXPLAINED!!!!! He needs to write a book
Explained with great simplicity, thanks for this!
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.
My first encounter with your teaching style was in the Pytorch Udacity Challenge , I loved it ,and following you since then.
indeed! best explanation of Naive Baye’s & Baye’s theorem
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! I was struggling so long with this one, because my prof. only explained it with one posterior, not two or more.
Very Nice Explanation and visual representation of the "Naive Bays" algorithm. Thanks, Luis Sir for this video. It really clears the concepts. Now, a little request is to make video on "Decision Tree" and "Random Forrest" like the same in visual representation.
Wow ... I love the way you present this topic. Thank you very much.
Best Naive Bayes explanation ever! thank U Luis
Finally, I noticed that there is a difference between Bayes theorem and Naive Bayes.
This was great ... I'm just starting out studying prob/stats .... and I think this will go a long way to overcome some of the hurdles I was having get past the Bayes theory. Now I am ready to hit the books again.
this one is the best explaination i have seen so far and i can unique too
The best explanation ever of Naïve Bayes
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.
This is really good! Thank you so much for your time and effort to make this topics accessible to the masses 🙂
Thank You for Such Clear and Well structured Explanation!.
Great explanation , perfectly paced.
This was a very clear and beginner-friendly explanation. Thank you!
Amazing, been researching this for a while and the way you break this down really gets through
Great explanation...It was very easy to understand Naive Bayes ...Thank you very much for this video...!!
Beautifully and clearly explained....Thank You sir.
no thank you Luis you are an excellent educator
Amazing explaination so far .. I am watching this in morning and you literally made my morning ... I have one question (after that I would understand it perfectly).. After training your Naive Bayes model, the output of the whole model is in terms of probability, ie, P(S/(buy and cheap)) and P(not S / (buy and cheap)).. right ? if yes, what happens in testing phase of the naive bayes model, I mean how this works during testing phase.. eagerly waiting for your reply sir :)
This is really FRIENDLY. Thank you!
Thank you so much for the crisp and clear explanation !!
You rock Luis .I am looking forward to more videos from you
Absolutely fantastic explaination. Thank you so much for this.
Awesome session.. Thanks a million!! God Bless..
Awesome!!!! Now I understand, about what Naive Bayes actually is. Thanks!!!
Thank you! This is so much more clear than my textbook!
Great explanation, Luis, and many thanks!
"Any classifier that tells you something is a 100% is too strong" haha. Thanks for the video, Luis.
Beautifully concise..
This is a beautiful explanation!
thank you for excellent explanation !
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.
Thanks for your detailed and friendly explanation. It really helps me a lot :)
Best explanation...Luis
Pretty neat. Very easily understandable. Well explained.
Awesome explanation...👍
Excellent video. So well explained!
Explained intuitively! Thanks! :)
Would love to see you explaining Kalmanfilter
very nice explanation, can you please mention how to find the naive Bayesian classifier's uncertainty?
Best explanation. Thank you.
You Sir are a hero!
best video on Naive Baye's
GREAT explanation. But it didn't click for me until the 2nd half. Stick with it folks....and thank you, Professor Serrano!
Beautiful explanation!
Excellent explanation. Thank you 🙏
thank you! its really well explained and good animation. please do a video on Generalized linear model.
you made this so easy to understand, thank you!
Amazing explanation, thank you for making my life easier.
You nailed it. Was awesome. I really want to know; is Naive Bayes and Bayes Theorem same?
Thank you so much for this video, Luis! Respect!
very clear explanation, thank you!
Really amazing sir❤ love from India, watching you videos on 3G internet 😅
I must say thank you so much for this fantastic upload..
An excellent explanation.
Great Explanation! Can someone please explain how we get for P(B/S) = 20/25? @ 17:45
Excellent explanation..