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Amazing explanation! My professor, who has been teaching for over 30 years, could not have done it NEARLY as well as you just did. Thank you very much Mahesh! You are the teacher of the next generation.
Dear Mahesh sir... No where in this entire world I have seen explaining decision tree explaining till this level. It is much needed and very helpful tutorial. Thanks a lot sir.
He is good okay but entire world means how many countries u seen or how many playlists u seen 😅😅😅😅😅😅iam not saying he is bad he is very good very very good very very very good excellent teaching iam joking on ur statement that's it
@@sravanr2275 RUclips is the entire thing for me since I cant afford to go countries. Got little carried away with my comment🤣 here. But it really clarified my much needed doubt. Just my way of paying gratitude to him.🙏
@@230489shraddha no no bro he is excellent no doubt in that and all Graduation students choice is RUclips almost 😅😂🥲no doubt in that iam just saying is u said entire world means u went so many countries like that 🥲😅that's it u seen in RUclips okay that's okay that's fine that's completely fine 🙂🙃🫂🫶🏻🫶🏻🙌😕👋❤️♥️❤️🔥🫂🫂💥🔥Mahesh Huddur Sir's Explanation is just lit 🥵🥶i also having ML Exam Today afternoon just seeing sir's videos only😅atleast to pass
I watched your 5-6 videos, and I learned more in those ~45 mins compared to 1 month what they thought in college. You are our life saver. Thank you very much 🙏
Thank u so much first time I found your video every time I'm watching four to five channels for only one video whenever their way of teaching is not helpful or understand able but first time I got your tutorials thank u so much sir Afggany
Hi Bro, your tutorial is awesome and very nice explanation. I learned lot of information from your tutorial. but one small correction 19:53 please kindly check this. Decision Tree of Rain you should specify Entropy(Rain) , but you have mentioned Entropy(Sunny). You have mentioned Entropy(Sunny) instead of Entropy(Rain). like that Temp, Humidity and Wind all classes you should specify as Entropy(Rain).
You explained this perfectly! There are a ton of steps, but I can go through the video again. I understand what information gain is and that helps tremendously and I am able to determine which attribute to use as a node. This is awesome! Thank you, thank you, thank you!
Thank u so much sir for U to explain this problem. From that 1 example I have cleared all my doubts nd problems according to this topic....#Decision Tree .....Once again thnx alot sir 🙏👏
Thank you so much, for such a clear approach towards understanding DTrees. What I liked about you, is the hands-on approach and actually doing the maths. Thanks man. Loved it.
I am thoroughly impressed by your explanation of the decision tree algorithm. Your teaching style is exceptional and you made the complex concept easy to understand. I feel confident that I can apply this knowledge now and in the future. Thank you for your outstanding work.
Excellent teaching! I loved it however I have a question here, What about the variable 'temp' that was the part of the dataset, why is it not added anywhere in the tree building here? & Is it ok if any one such independent variables are left behind i.e. if they are not added in the tree?
first thing the gain(temp) is the lowest among all. second after going through the features we arrived to a place where all the data points are labelled so that's why it was enough to use 3 features not all 4. because the iteration stops when all the data points are labelled therefore its useless to use the temp. ^ this was my interpretation
@@emilerahal5313 What if few of the data points are left unlabeled clearly even after adding the 4th variable which in this case is the last variable of the dataset? How does one interpret that?
Thanks Mahesh, 99.9 percent of courses throw either mathematical jargon at you or python api calls this one is what your really need to know, i.e what are the mechanics of the algorithm when given a data set because thats what you will be faced with in the real world . . once you understand this the rest is easy. i.e coding it etc
Thanks for creating excellent videos on machine learning! I am often getting confused on which algorithm uses Information Gain or Gini Impurity for splitting attributes at each level.
Firstly, great explanation! Couldn't have done it any better. However, I have a concern. What to do if we come across same Info Gain values at any level?
@@MaheshHuddar dear sir...your video is highly appreciated and tremendous explanation.. Easy to grasp too... Can you provide me notes for ML - theory paper ..May i get your contact number pls
It takes a significant amount of time and energy to create these free video tutorials.
You can support my efforts in this way:
Buy me a Coffee: buymeacoffee.com/maheshhuddar
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definetly, have a coffee🍵🍵
Once you go through his videos basic becomes strong and crystal clear.
@@MaheshHuddar Learning Student while earning :-)
hell yeah theroies are destroyed and make it so damn clear
yas
Amazing explanation! My professor, who has been teaching for over 30 years, could not have done it NEARLY as well as you just did. Thank you very much Mahesh! You are the teacher of the next generation.
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@@MaheshHuddarI understand you
@@MaheshHuddaryou
Very helpful. If anyone didn't know anything about the decision tree he/she will also know what it is. Really very beautifully explained.
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Ur machine learning complete playlist is very helpful.. Thank you so much..ur teaching style is amazing
Amazing just amazing this man is filled with hell of a wisdom and teaching style 🔥🙏
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Dear Mahesh sir... No where in this entire world I have seen explaining decision tree explaining till this level. It is much needed and very helpful tutorial. Thanks a lot sir.
He is good okay but entire world means how many countries u seen or how many playlists u seen 😅😅😅😅😅😅iam not saying he is bad he is very good very very good very very very good excellent teaching iam joking on ur statement that's it
@@sravanr2275 RUclips is the entire thing for me since I cant afford to go countries. Got little carried away with my comment🤣 here. But it really clarified my much needed doubt. Just my way of paying gratitude to him.🙏
@@230489shraddha no no bro he is excellent no doubt in that and all Graduation students choice is RUclips almost 😅😂🥲no doubt in that iam just saying is u said entire world means u went so many countries like that 🥲😅that's it u seen in RUclips okay that's okay that's fine that's completely fine 🙂🙃🫂🫶🏻🫶🏻🙌😕👋❤️♥️❤️🔥🫂🫂💥🔥Mahesh Huddur Sir's Explanation is just lit 🥵🥶i also having ML Exam Today afternoon just seeing sir's videos only😅atleast to pass
@@sravanr2275 autism
Osm osm osm🔥🔥🔥 just one example cleared all my doubts👏
Itta badha hai
Can any one tell me 9/14 log2 9/14. How they are calculating?
So it's logarithms?
@@rama6249in scientific calculator u put as iam giving ((9/4)log2(9/4))
@@SuffStar I got it thanks 🙏
I really wish you were my machine learning teacher. Thank you Sir 😍
Nice ,it shows you have a habit of solving complete numerical ☺️☺️
Although it's hard to understand English with accent, this video is really helpful, thanks a lot.
Excellent. Such a nice explanation, we need this kind of teacher. Kudos!
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Best explanation of the ID3 algo. Thank you.
arguably the best video on decision tree. I watched a lot videos on this topic. None explained the entire process this clearly
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This is like full meals to me, got complete idea of how tree forms... appreciate your effort
I watched your 5-6 videos, and I learned more in those ~45 mins compared to 1 month what they thought in college. You are our life saver. Thank you very much 🙏
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@@MaheshHuddar Yes sir, my entire friend group is learning from this video
Last bench adyar attendance lagao
Most underrated channel for machine learning 🙏Thank you Mahesh huddar sir
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Best explanation on RUclips for this topic. Thanks alot!
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You simplified and rocked to the point. Thank you!
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Best Explanation ever. For the subjects AI, Data Mining , ML one can rely on your videos.
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Thank u so much first time I found your video every time I'm watching four to five channels for only one video whenever their way of teaching is not helpful or understand able but first time I got your tutorials thank u so much sir
Afggany
Hatsoff to ur patience, very well thought, although som many times many procedures were the same u explained those repeating things aswell
How can anyone solve this huge mess so calmly 🤕. Best Tutorial Thank You Sir 😇
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Thank You Sir ! I had a deep understanding of the algorithm !
Hi Mahesh,
Excellent tutorial with insightful calculation demo. Thanks a lot. I have subscribed. KM
Brilliant job sir, you have explained the working mechanism behind decision tree
Thanks!
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Hi Bro, your tutorial is awesome and very nice explanation. I learned lot of information from your tutorial. but one small correction 19:53 please kindly check this. Decision Tree of Rain you should specify Entropy(Rain) , but you have mentioned Entropy(Sunny). You have mentioned Entropy(Sunny) instead of Entropy(Rain). like that Temp, Humidity and Wind all classes you should specify as Entropy(Rain).
big deal bro
This should be pinned 👍🏻💯
I think this is the best explanation of ID3 theoram on internet..So well explained
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Yes that's why this is the most watched video of this channel ♥️.
Cleared all my doubts sir😍
you helped me alot sir even after 4 year your content is useful thank you
Thank you so much SIR!!! exam ke 2 hr pehele padliya apka video se.. thanks
Dear sir, you are so genius and Thank you so much for your patient explaination :)
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By far the best one..in terms of clarity and explanation..keep up your gd work🎉
The way you make this long question like a piece of cake for students is just amazing keep doing the awesome work. Really appreciates your efforts.
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Well explained 🙏🏻 thanks 👍🏻
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Thank you so much for creating this video, thank you, one day before final exam
Kudos to such great explanation😇
hats off to ur coaching sirr
Very clear video, great explanation. Good teaching ❤
Best explanation. Thank you.
The best explanation of ID3 I've found
Very beautifully explained. Thank you so much
Thank u for the clear explanation ✌✌
Best explanation on RUclips for decision tree.
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So good my whole concept is clear ❤
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Thank you so much sir. It's very helpful to me. By this vedio my all doubts are cleared.
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Sir, you are the best !! you make concepts look soo easy and understandable.....Hatts off to you sir for your hardwork and teaching style.
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Easily the best video of Decision Trees!
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Before watching this video I thought these questions were veryyyy complex but now just Loved it
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Concept is clear indeed
You explained this perfectly! There are a ton of steps, but I can go through the video again. I understand what information gain is and that helps tremendously and I am able to determine which attribute to use as a node. This is awesome! Thank you, thank you, thank you!
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This is very insightful video sir. Thanks for this video 🙏
OMG!! You helped me soo much!! Where do you find all of tthese examples for practicing???
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I have referred multiple Machine Learning books
Best explaination!! I understood completely
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very nice explanation sir ,thanks a lot
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Sir....although ur accent is kinda hard to understand, u made me understand the basic concept of decision tree in just 23 minutes and 52 secs. thanks
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no one can explain like this, crystal clear
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Awesome video, just one example cleared all my doubts!!
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very clear discription, thank you!!
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Great work sir. Please upload a video on Regression using Decision Tree.
Thank u so much sir for U to explain this problem. From that 1 example I have cleared all my doubts nd problems according to this topic....#Decision Tree .....Once again thnx alot sir 🙏👏
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😘 lovely lecture clear the concept crystal clear but in rain calculating the entropy for sunny it entropy(s rain)
Thankyouuu sooo muchhh sirrr😭😭❤ i can't explain you how muchh you helped me, this isss soo simple explanation of such a complex topic! Thankyou!
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very helpful, thank you sir!
crystal clear, thank you
Thank u so muchhhhh sir😇 .....your teaching style is super amazing.😎👌You should got five stars.💫💫💫💫💫😂😂
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Thank you so much, for such a clear approach towards understanding DTrees. What I liked about you, is the hands-on approach and actually doing the maths.
Thanks man. Loved it.
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I am thoroughly impressed by your explanation of the decision tree algorithm. Your teaching style is exceptional and you made the complex concept easy to understand. I feel confident that I can apply this knowledge now and in the future. Thank you for your outstanding work.
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Excellent teaching! I loved it however I have a question here, What about the variable 'temp' that was the part of the dataset, why is it not added anywhere in the tree building here? & Is it ok if any one such independent variables are left behind i.e. if they are not added in the tree?
Was also wondering about the the same
first thing the gain(temp) is the lowest among all.
second after going through the features we arrived to a place where all the data points are labelled so that's why it was enough to use 3 features not all 4.
because the iteration stops when all the data points are labelled therefore its useless to use the temp.
^ this was my interpretation
@@emilerahal5313 What if few of the data points are left unlabeled clearly even after adding the 4th variable which in this case is the last variable of the dataset? How does one interpret that?
Very clear Explanation Sir. Thank you.
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Thank you. This is extremely helpful.
Thank you sir for amazing lecture of machine learning.
It is very helpful for easy learning of this subject.
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Great explanation Sir ....thank you ...thank you sooooo much sir.. Cool👍👍👍👍👍
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syudying can be fun if there teachers like you, thank you sir
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Thank you sir for the most crystal clear explanation
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Ahhh thank you soooo sooo much....awesomeeee.....sb clear ho gya ek hi example m❤❤❤❤
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Thank You
U deserve an Oscar actually
those 23 mins were worth watching... thanks :)
Nice explanation, thanks a lot sir😢
Thanks Mahesh, 99.9 percent of courses throw either mathematical jargon at you or python api calls this one is what your really need to know, i.e what are the mechanics of the algorithm when given a data set because thats what you will be faced with in the real world . . once you understand this the rest is easy. i.e coding it etc
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excellent work!
Thanks for creating excellent videos on machine learning!
I am often getting confused on which algorithm uses Information Gain or Gini Impurity for splitting attributes at each level.
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Easy to understand by your explanation!
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thank you sir for the best explanation,
really, superbbbbb explanation, my concepts got very clear, Thank you so much
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Can I know if it possible for id3 algorithm to induce only one branch (left data) and been selected as the next attribute?
Excellent Brother, Thank you so much for your Videos. Am Extremely happy about finding your channel for best.
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Thank you so much sir. I'm from Bangladesh. Take love.
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Thank you very much 😊 ❤
Firstly, great explanation! Couldn't have done it any better. However, I have a concern. What to do if we come across same Info Gain values at any level?
Need to draw multiple trees by cons one of them as root node at a time
Thank yo mu sir machine learning ka sbse bdiya content bs thi pr mila
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I've ML exam tmrw..this video is a saviour!!
I hope, there won't be any numerical question from DT😢😢its soooooo long!!
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All the best for your exams
I'm which university you are studying..?
Hello sir , I'm studying at NIT Silchar.
@@MaheshHuddar dear sir...your video is highly appreciated and tremendous explanation..
Easy to grasp too... Can you provide me notes for ML - theory paper ..May i get your contact number pls
very best video to all math in ml for all beginers students
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@@MaheshHuddar my today math exam same to same question on the math paper 😊
@@sumitdhir9283 in which university/ college you are studying
@@MaheshHuddar Centurion University of Management & Technology, Bhubaneswar, Odisha
you are a really good teacher
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Awesome explanation sir..though the problem is big..u have explained it very well 👌👌
well explained sir. Great job. Learned how to solve it.thanks.
Many many thnks sir, u r not just a human u r a god.
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you are great master. Thank you :)
We r just wasting time by going clg . Sir you're giving very nice explanation TQ sir
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Great explanation❤