"🔥Caltech Post Graduate Program In AI And Machine Learning - www.simplilearn.com/artificial-intelligence-masters-program-training-course?Z6ZNFjo0&Comments&UA-cam 🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?Z6ZNFjo0&Comments&UA-cam 🔥Purdue - Post Graduate Program in AI and Machine Learning - www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?Z6ZNFjo0&Comments&UA-cam 🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitg-generative-ai-machine-learning-program?Z6ZNFjo0&Comments&UA-cam 🔥Caltech - AI & Machine Learning Bootcamp (US Only) - www.simplilearn.com/ai-machine-learning-bootcamp?Z6ZNFjo0&Comments&UA-cam"
Can I just say how helpful you have been by making everything look so simple on python, there's other youtubers who just presume we are all qualified Data Scientists and know how to always write Machine Learning functions from scratch. You on the other hand teach us the basics of each ML topic and teach us how to use implement ML in datasets SIMPLY!!!!
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
@@SimplilearnOfficial Thank you for the reply Simplilearn! I have a quick question, in the confusion matrix you referenced 'mat.T', surely it should just be 'mat'? If it is mat.T, what does the T actually represent? I have looked at the SNS.heatmap condition breakdown on their website but I don't see any information about a .T Any help would be appreciated! Kind regards, Shimmy
16:27 Result is: ≈0.02 17:16 Result is: ≈0.98 19:15 Then sum of probabilities is: ≈1 (AND NOT 1.164, because sum of probabilities of two related events equals 1) Likelihood of Purchase: 0.98 / 1 = 98% Likelihood of NO Purchase: 0.02 / 1 = 2% And then as 98% > 2% we can conclude that customer will buy on a holiday with discount and free delivery.
I must say your explanation is excellent compared to others. You’re to the point and not unnecessarily stretching, as I’ve seen on other tutorials. Also Please help me with the data set
Hey Dean, thank you for appreciating our work. We are glad to have helped. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
1. For the first problem (12:00 into the lesson), P(No Buy | Weekday) can also be calculated by seeing that there are 11 visits on the weekday and 2 of them are No Buy. Therefore the answer is 2/11. Which does match the answer via Bayes (if the Bayes calculation does not round the fractions) 2. On the example using Holiday, Discount = Yes and Delivery = Yes, I was surprised that P(No Buy | B) + P(Buy | B) does not equal 1. Given that these are the same condition isn't the probability of Buy or No Buy 100% ? 3. P(A|B) When you doing the classification of articles ... is it true that the likelyhood tables are - The article types as the row headers and the key words as the column headers ... so there would be one very large likelyhood table.
@ 13:07 min in the slide of calculation P(B|A) = P(WeekDay | Buy) is wrong. It should be 9/24 not 2/6. 2/6 is P(weekDay | No Buy). Is my understanding correct?
Heyy thank you so much. I am doing project called order Prediction my dataset has following columns count year month day hour working_day weekend_day public_holiday then how should I apply naive bayes on it?
Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin. Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing Naive Bayes Classifier in Python, please comment below and we will get back to you. Thanks for watching the video. Cheers!
Hello Altaf, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello @ Simplilearn! I have become glued to your videos, please can you kindly help with the dataset too to this email? xxxxxxxxx - It will be greatly appreciated. Thanks in anticipation.
Hi Jay, we are super happy that you love your videos. We are glad to have helped. We have also shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Hey Shivam, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every day on all your favourite topics Have a good day!
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Nice and useful! Just curious to ask a question. As far as I understand, in the shopping use case, we used three features (Day, free delivery & discount). And, in the text categorization use case, we used one feature (text containing email content). Is that really treated as one feature here? Or, will the naive bayes algorithm, internally creates multiple features for the given input (text containing email)?
Our feature in the use case of text categorization is "each individual word". Here, we train the model to predict the probability of this particular work being in any of the different categories. The final probability is derived by calculating the overall conditional probability of that particular combination of words (just like we found the probability of different combinations of day, free delivery and discount).
Greetings! Thank you for your kind words. We have sent the requested dataset to your mail ID. Spread the word by liking, sharing and subscribing to our channel! Cheers :). You can also explore our playlists for more Machine Learning Videos - ua-cam.com/video/ukzFI9rgwfU/v-deo.html.
Hey Vignesh, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
at 19:47 timestamp, it is mentioned as sum of probabilities = 0.986+0.178 = 1.164. but, probability can range between (0..1). Can you please explain how sum of probabilities can exceed more than 1?
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
Hi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Hello Naveena, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Aisha, we are glad you found our tutorial informative. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello, we are glad you found our tutorial helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Hi jerry, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Hello Gaurav, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial I'm just having a little bit of problem with the dataset, do you have a video where you show how to do them and how you assign the right tags to the training group?
Hello. First of all thanks for the video. Video contains at least two mistakes. One is typo. Other is a logical error which is at 17:09. P(A|B,C,D) (where A ∈ {True,False}) is the question. If P(A=True|B,C,D) = p, then P(A=False|B,C,D) = 1-p because sample space of A contains only "True" or "False". Am I wrong? It is probably calculation error. The typo is at 13:10 (P(B|A)=2/6=0.375), it should be 9/24 instead of 2/6. It is left from the previous slide. That's all, have a good day!
Hello Narendra, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
You can post it in the comment section and we will send you required file directly. If you want your email ID to be kept hidden from others, we can do that as well. Hope that helps!
Hello Sougandh, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hello Lampa, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello Sandipan, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Simplilearn, It was really a nice tutorial and the example of Shopping was superbly explained. I have one doubt regarding code, when you were providing some different input like "Presedent of India", then you were getting output(lable) correctly. How was that working?
This works on the principle of text categorization. We have used Naive Bayes to achieve this in this video, though other algorithms could also be used for the same purpose.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Hello Trisit, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hello Shubhangi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello Nyjah, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
I don't know if it's just me but i think it would've really improved the tutorial if you at least had a mouse pointer pointing where you are explaining. Or the text clould've appeared progressively. I think it makes it hard to follow the tutorial. My eyes just don't know where to look at.
Thank you for letting us know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hello Sachin, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hello Shivani, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
You took mat variable for confusion matrix, can you tell me what is the difference between mat and mat.T??? (mat and mat.T both are printing different tables)
I have twitter text data around in millions , that i fetched from twitter based on the terms ' Coronavirus'. 'COVID-19', Now i put some labels manually to approximately 2000 posts, Now can i use this algorithm to classify the remaining textual data based on my labels that i gave? PS: I classified the data into eight textual categories! And also for text data classification which algorithm is best ?
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hello Eduardo, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hello Neema, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Hi Baloch, thanks for checking out our tutorial. We have sent the requested dataset to your mail ID. Do support us by subscribing to the channel as well as giving a thumbs up to the video. Cheers!
Hello Maxwell, thanks for watching our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Thanks for the video. I couldn't catch (for the purchase&no purchase example) why the sum of probability of a purchase and probability of no purchase doesn't make 1. When B = (Day=Holiday , Discount=Yes , Free Delivery = Yes) , there are only 2 possible results , buy or no buy. So the sum of the probabilities should equal 1 , isn't it?
@@SimplilearnOfficial There is a calculation mistake @ 15:35 . I think your intuition is wrong, however, I'm not sure as I am still very inexperienced in the field(student). However there is a normalization probability in Bayes(denominator).
Hello Muhammad, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Jay, we are super happy that you love your videos. We are glad to have helped. We have also shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Hi, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Hello, thanks for appreciating our work. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hey Dean, thank you for appreciating our work. We are glad to have helped. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. Cheers!
Hello Chantal, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Hema, we are glad you found our video interesting and useful. We have sent the requested dataset to your mail ID. Do show your love by subscribing our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Hello Maya, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hello Dellainey, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Dellainey, thanks for sharing your mail ID. We have sent the requested dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello Srikanth, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Claudia, thanks for appreciating our work. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello Tanneru, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello Carey, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Carey, thanks for sharing your mail ID. We have sent the requested dataset to your mail ID. Do subscribe to our channel and get our new video updates directly into your email. If you have any questions related to these videos, you can post in the comments section, we will clear your queries/doubts.
Hi, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
hi i really like the videos that you guys made and thought that it might be helpful if you guys simply provided your datasets in the descriptions of the video so I wouldn't have to request every video. With that said, could i have the data set from this video? thanks
Hi Au, we are glad that you found our content helpful. It will be possible to share the dataset if you can share your email id with us. If you wish your email id not to be seen in the comments, we can do that as well. Hope that helps!
Hi, my email is bjgau@outlook.com, also I believe there may be an error at 13:00 since 2/6 is not the same as 0.375, the correct fraction should be 9/24
Hi B Au, yes, as you rightly pointed out, at 13:06, P(B|A) should be equal to 9/24 which equals 0.375 as it is shown in the Likelihood table. We are sorry for the inconvenience!
Hi Roshini, I hope you must have received the dataset from our team. Let me know in case you find any difficulty regarding Naive Bayes. Also, do subscribe to our channel and do not forget to hit the BELL icon for never missing another update. Cheers!
Hello Su Laet, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
"🔥Caltech Post Graduate Program In AI And Machine Learning - www.simplilearn.com/artificial-intelligence-masters-program-training-course?Z6ZNFjo0&Comments&UA-cam
🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?Z6ZNFjo0&Comments&UA-cam
🔥Purdue - Post Graduate Program in AI and Machine Learning - www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?Z6ZNFjo0&Comments&UA-cam
🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitg-generative-ai-machine-learning-program?Z6ZNFjo0&Comments&UA-cam
🔥Caltech - AI & Machine Learning Bootcamp (US Only) - www.simplilearn.com/ai-machine-learning-bootcamp?Z6ZNFjo0&Comments&UA-cam"
Can I just say how helpful you have been by making everything look so simple on python, there's other youtubers who just presume we are all qualified Data Scientists and know how to always write Machine Learning functions from scratch.
You on the other hand teach us the basics of each ML topic and teach us how to use implement ML in datasets SIMPLY!!!!
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
@@SimplilearnOfficial Thank you for the reply Simplilearn!
I have a quick question, in the confusion matrix you referenced 'mat.T', surely it should just be 'mat'? If it is mat.T, what does the T actually represent? I have looked at the SNS.heatmap condition breakdown on their website but I don't see any information about a .T
Any help would be appreciated!
Kind regards,
Shimmy
16:27 Result is: ≈0.02
17:16 Result is: ≈0.98
19:15 Then sum of probabilities is: ≈1 (AND NOT 1.164, because sum of probabilities of two related events equals 1)
Likelihood of Purchase: 0.98 / 1 = 98%
Likelihood of NO Purchase: 0.02 / 1 = 2%
And then as 98% > 2% we can conclude that customer will buy on a holiday with discount and free delivery.
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
The Demo part was just awesome ... Thanks a lot for the effort
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
I must say your explanation is excellent compared to others. You’re to the point and not unnecessarily stretching, as I’ve seen on other tutorials.
Also Please help me with the data set
Hey Dean, thank you for appreciating our work. We are glad to have helped. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
THANK YOU SO MUCH,
YOUR EXPLANATION IS EASY TO FOLLOW AND UNDERSTAND.
Glad it was helpful!
1. For the first problem (12:00 into the lesson), P(No Buy | Weekday) can also be calculated by seeing that there are 11 visits on the weekday and 2 of them are No Buy. Therefore the answer is 2/11. Which does match the answer via Bayes (if the Bayes calculation does not round the fractions)
2. On the example using Holiday, Discount = Yes and Delivery = Yes, I was surprised that P(No Buy | B) + P(Buy | B) does not equal 1. Given that these are the same condition isn't the probability of Buy or No Buy 100% ?
3. P(A|B) When you doing the classification of articles ... is it true that the likelyhood tables are - The article types as the row headers and the key words as the column headers ... so there would be one very large likelyhood table.
Great insights and fantastic questions.
Hey Daniel, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
@ 13:07 min in the slide of calculation P(B|A) = P(WeekDay | Buy) is wrong. It should be 9/24 not 2/6. 2/6 is P(weekDay | No Buy). Is my understanding correct?
I agree with you. The answer for P(No Buy | Weekday) = 2/11, P(Buy | Weekday) = 9/11. These can be verified from the frequency table directly.
Thanks for sharing your thoughts. Cheers!
@19:20 why is the sum of probabilities higher than 1, how does that come about? I thought probabilities sum to 1
TQVM for the Machine Learning series!!!
Our pleasure!
Heyy thank you so much.
I am doing project called order Prediction
my dataset has following columns
count year month day hour working_day weekend_day public_holiday
then how should I apply naive bayes on it?
very clear explanation with great examples, thank you!
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin.
Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing Naive Bayes Classifier in Python, please comment below and we will get back to you.
Thanks for watching the video. Cheers!
3:11 P(b) = Probability Of event b .?
Thank you for pointing out, Sandeep. P(B) is equal to the Probability of occurrence of event B.
@15:00, I guess it's typo "P(B)=Probability of event A".
Hi, my email is kartha@vivaldi.net
Could you please send me the dataset?
At the loc 13:20 , P(B|A) is nothing but P(Buy|Weekday) and should be 9/24 instead of 2/6.Isn't it?
The way you explain is amazing. Could you do a favour by providing the dataset.
Hello Altaf, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello @ Simplilearn! I have become glued to your videos, please can you kindly help with the dataset too to this email? xxxxxxxxx - It will be greatly appreciated. Thanks in anticipation.
Hi Jay, we are super happy that you love your videos. We are glad to have helped. We have also shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Thanks very much. I subscribed already. And I have the dataset too.
Hi Tee Jay, thanks for subscribing to our channel. We welcome you to our community!
YOUR tutorials are awesome!!!.Thankx for sharing.God bless u!!
Hey Shivam, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Your videos are amazing. Thanks! I have subscribed!
Woah! Thanks for the overwhelming comment. We hope you love our community as well. Cheers!
Probability Probability Probability Probability and done
Very good explanation. I have a question at 19:36 sum of probabilities is 1.164, how sum of probabilities is greater than 1?
You must normalize the probabilities by dividing both by 1.164, then you have the sum of the probabilities equal to 1. Sorry for my bad english
This is superb.
We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!
Great explanation, ty
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every day on all your favourite topics Have a good day!
Wonderful tutorial! Great work and thanks! :)
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
Just what I was looking for thank you so much.
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Excellent 🌙
Thank you! Cheers!
Nice and useful! Just curious to ask a question. As far as I understand, in the shopping use case, we used three features (Day, free delivery & discount). And, in the text categorization use case, we used one feature (text containing email content). Is that really treated as one feature here? Or, will the naive bayes algorithm, internally creates multiple features for the given input (text containing email)?
Our feature in the use case of text categorization is "each individual word". Here, we train the model to predict the probability of this particular work being in any of the different categories. The final probability is derived by calculating the overall conditional probability of that particular combination of words (just like we found the probability of different combinations of day, free delivery and discount).
Great tutorial again SimplyLearn you guys are amazing for simplifying the topics! can you please share the dataset for this!
Greetings! Thank you for your kind words. We have sent the requested dataset to your mail ID. Spread the word by liking, sharing and subscribing to our channel! Cheers :). You can also explore our playlists for more Machine Learning Videos - ua-cam.com/video/ukzFI9rgwfU/v-deo.html.
Great content that you so much!
Glad you enjoy it!
Good video on Naive Bayes classifier
Hey Vignesh, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Great Tutorial! What I'm searching for!
Greetings! Thank you for your kind words. Do show your support by liking, sharing and subscribing to our channel! Cheers :).
at 19:47 timestamp, it is mentioned as sum of probabilities = 0.986+0.178 = 1.164. but, probability can range between (0..1). Can you please explain how sum of probabilities can exceed more than 1?
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
Why do I see sometimes people ignore the the P(B) in the denominator because it is assumed that it does not change the outcome.
Hello thank you for this interesting explanation.
please how to i get the dataset you used for this?
thank you
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
wow thank you so much!!! excelente content !!!
Hi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Fantastic explaination....can i get dataset for above classification test?
Hello Naveena, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Thank you Simplilearn team for the clear explanation. Can you please provide the dataset and the python notebook used in the video?
Hi Aisha, we are glad you found our tutorial informative. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
was really helpful to understand!! Can you please share this dataset for practice?
Hello, we are glad you found our tutorial helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Thank you for the presentation. Love to see the data set.
Hi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Thank you for taking your time to prepare this, it's very helpful. I'll appreciate if you can share the dataset. Thanks again.
Here's my mail : jmiri@josagro.com.ng
Hi jerry, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
why are we transposing the matrix in the case of confusion matrix in @37.02
very helpful
Glad to hear that
This was really helpful to understand. Great tutorials!!! Can you please share this dataset for practice?
Hello Gaurav, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial plz share dataset on chandrasejal06@gmail.com
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Mistake at 6:58, P(B|A) should be correctly mentioned as Probability of B given A.
13:28 P(B|A) is 9/24 (0.375) not 2/6 (0.333...), but the result is correct.
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
Just subscribe to your channel, you do a great job explainig I love it!
Thanks for subscribing to our channel. We welcome you!
@@SimplilearnOfficial I'm just having a little bit of problem with the dataset, do you have a video where you show how to do them and how you assign the right tags to the training group?
Hello. First of all thanks for the video. Video contains at least two mistakes. One is typo. Other is a logical error which is at 17:09. P(A|B,C,D) (where A ∈ {True,False}) is the question. If P(A=True|B,C,D) = p, then P(A=False|B,C,D) = 1-p because sample space of A contains only "True" or "False". Am I wrong? It is probably calculation error. The typo is at 13:10 (P(B|A)=2/6=0.375), it should be 9/24 instead of 2/6. It is left from the previous slide. That's all, have a good day!
Thanks for your response! You are correct, we are sorry for the error!
Awesome tutorial..... :) Dataset please???
Hello Narendra, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@@SimplilearnOfficial do you want to share my ID here...?
You can post it in the comment section and we will send you required file directly. If you want your email ID to be kept hidden from others, we can do that as well. Hope that helps!
@@SimplilearnOfficial can you send me the dataset also
Hello Sougandh, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Keep up the good work Simplilearn team, please send me the dataset mentioned at around 8:41
Hello Lampa, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Thanks very much for the swift and positive response, here it is >> lampapoultaal@gmail.com
thanks for this tutorial. Please i need the shopping cart dataset
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
This is on a slight tangent, but i gotta ask: why do you call numpy (py as in python) "numPEE" and sci (sci as in science) as "SKY"?
How can we check for the accuracy on test set for the above 20 news group model????
Absolutely loved it!! Can you please forward me the data sets?
Hello Sandipan, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
At 3:10 mins, P(B) has been mentioned as probability of (A) , this should be probability of (B) .
Good eye! It's our mistake.
Hi Simplilearn, It was really a nice tutorial and the example of Shopping was superbly explained. I have one doubt regarding code, when you were providing some different input like "Presedent of India", then you were getting output(lable) correctly. How was that working?
This works on the principle of text categorization. We have used Naive Bayes to achieve this in this video, though other algorithms could also be used for the same purpose.
@13.24 - P(Weekday|Buy) should be 9/24. In slides it is 2/6
Also could you please send the data sets @ manishgaurav84@gmail.com
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
16:34 Why P(Discount = Yes, FreeDelivery = Yes, Day = Holiday | No) = P (Discount = Yes | No) * P (FreeDelivery = Yes | No ) * P (Day = Holiday | No) ? Are P (Discount = Yes | No), P (FreeDelivery = Yes | No ), P (Day = Holiday | No) probabilities of independent events?
Thanks for the video!! Can you share the dataset plz?
Hello Trisit, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@19:20 why is the sum of probabilities higher than 1, how does that come about? I thought probabilities sum to 1
thank you ^^
Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : )
Hello, I really found the conceptual part easily comprehendible. Can you please send me the dataset?
Hello Shubhangi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello thank you so much for this!. If possible, can I get the shopping data to try and analyze myself in Python? Thank you!
Hello Nyjah, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
I don't know if it's just me but i think it would've really improved the tutorial if you at least had a mouse pointer pointing where you are explaining.
Or the text clould've appeared progressively.
I think it makes it hard to follow the tutorial. My eyes just don't know where to look at.
Thank you for letting us know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.
great content. can you provide me the dataset ?
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
2:38 there is a typo P(B) should be probability of event B and not A
That's correct. You have an eye for detail. Cheers!
awesome explanation..could u please share the code and dataset..it will be really helpful
Hello Sachin, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
@@SimplilearnOfficial cricketfanhere@gmail.com
Great tutorial. Please can you send the datasets please. Thanks in advance
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Nice tutorial. Pl confirm how to import classified text in CVS or excel file.
Hello Shivani, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@@SimplilearnOfficial sheevani.thakur@gmail.com
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial I am unable to open a middle file you have sent. Pl confirm, have you created a data set in excel.
@@SimplilearnOfficial I have sent you an email pl check.
You took mat variable for confusion matrix, can you tell me what is the difference between mat and mat.T??? (mat and mat.T both are printing different tables)
mat.T is the transpose
I have twitter text data around in millions , that i fetched from twitter based on the terms ' Coronavirus'. 'COVID-19', Now i put some labels manually to approximately 2000 posts, Now can i use this algorithm to classify the remaining textual data based on my labels that i gave? PS: I classified the data into eight textual categories! And also for text data classification which algorithm is best ?
Great class, please send me the data set.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hi could please share the database used for the demo? Thank you
Hello Eduardo, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Great tutorial, could you please share the dataset?
Hello Neema, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Amazing explanation, please could you send me a copy of the data set - many thanks for these tutorials!
Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Thanks for uploading such type of videos. kindly send me the dataset thanks
Hi Baloch, thanks for checking out our tutorial. We have sent the requested dataset to your mail ID. Do support us by subscribing to the channel as well as giving a thumbs up to the video. Cheers!
great tutorial, very easy to understand
Please can i have a dataset to test your demo
Hello Maxwell, thanks for watching our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Nice one! i have almost downloded all the videos here pls, help me with the datasets for the tutorial so as to practice it plss
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
why is P(A|B) plus P(NOT A | B) not equal to 1?
Thanks for the video. I couldn't catch (for the purchase&no purchase example) why the sum of probability of a purchase and probability of no purchase doesn't make 1. When B = (Day=Holiday , Discount=Yes , Free Delivery = Yes) , there are only 2 possible results , buy or no buy. So the sum of the probabilities should equal 1 , isn't it?
I too have the same doubt, I think the clarification given earlier is ambiguous.
We need to normalize the conditional probabilities and these normalized probabilities do add up to 1 (Go to 19:14 in the video).
We need to normalize the conditional probabilities and these normalized probabilities do add up to 1 (Go to 19:14 in the video).
@@SimplilearnOfficial
There is a calculation mistake @ 15:35 .
I think your intuition is wrong, however, I'm not sure as I am still very inexperienced in the field(student).
However there is a normalization probability in Bayes(denominator).
Dataset please ?? Will be helpful,
thanks in advance
Hello Muhammad, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@@SimplilearnOfficial faizii.jay@gmail.com
Hi Jay, we are super happy that you love your videos. We are glad to have helped. We have also shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Hi, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Awesome explanation, could you please share me the dataset. Thanks!!
Hello, thanks for appreciating our work. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Thank you!!
We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!
Wonderful Video! very helpful. Can I please have the data set. Thank you
Hey Dean, thank you for appreciating our work. We are glad to have helped. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. Cheers!
Hi thank you for this great video.
could you share the dataset please
Hello Chantal, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
video is very interesting and useful. I would like to work on the dataset can u please share
Hi Hema, we are glad you found our video interesting and useful. We have sent the requested dataset to your mail ID. Do show your love by subscribing our channel using this link: ua-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial Sir, I haven't received any email regarding dataset yet!
can i get the shopping cart data
good video .can I get the data set please?
Hello Maya, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Why you haven't put data set link in description ? You are collecting email ids of people by asking it to provide datset.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Great tutorial video! very well explained. Could i have the dataset so i could use it.
Hello Dellainey, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Dellainey, thanks for sharing your mail ID. We have sent the requested dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Hi thank you for this video! Please could i ask for the dataset and source code?
Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hi
It's a very good tutorial. Would appreciate if you can send the dataset.
Hello Srikanth, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
nice explination
Hey Paray, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
great tutorial, can you please send the dataset? thanks.
Hi Claudia, thanks for appreciating our work. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Thanks for such a great tutorial! Can I have the dataset, please?
Hello Tanneru, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial can you send me the Dataset? My Email is thies.schomaker@gmx.de
Hello! Thanks for creating such a wonderful tutorial! Could you please send me the dataset?
Hello Carey, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@@SimplilearnOfficial Hi! Thanks for reply. My email is careywang1231@gmail.com.
Hi Carey, thanks for sharing your mail ID. We have sent the requested dataset to your mail ID. Do subscribe to our channel and get our new video updates directly into your email. If you have any questions related to these videos, you can post in the comments section, we will clear your queries/doubts.
Nice Video sir can you please share this dataset.
Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :).
Can I get the dataset?
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
@@SimplilearnOfficial thanks a lot 😊
Hello can u pls share the datasets u used in this demo
Hi, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
hi i really like the videos that you guys made and thought that it might be helpful if you guys simply provided your datasets in the descriptions of the video so I wouldn't have to request every video. With that said, could i have the data set from this video? thanks
Hi Au, we are glad that you found our content helpful. It will be possible to share the dataset if you can share your email id with us. If you wish your email id not to be seen in the comments, we can do that as well.
Hope that helps!
Hi, my email is bjgau@outlook.com, also I believe there may be an error at 13:00 since 2/6 is not the same as 0.375, the correct fraction should be 9/24
Hi B Au, yes, as you rightly pointed out, at 13:06, P(B|A) should be equal to 9/24 which equals 0.375 as it is shown in the Likelihood table. We are sorry for the inconvenience!
Can you please provide dataset
Hi Roshini, I hope you must have received the dataset from our team. Let me know in case you find any difficulty regarding Naive Bayes. Also, do subscribe to our channel and do not forget to hit the BELL icon for never missing another update. Cheers!
Can you please share the dataset
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Great explaination! can i have the dataset? Thanks
Hello Su Laet, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
There is an error at 13:09 when P(weekday| buy) = 2/6 however it should be 9/24.
Good eye! Our mistake.
hello, thanks so much for the tutorial, can you send me please the dataset?
Sent!