Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: bit.ly/2OpzQWw
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Great to see that our videos and contents are making you perform better and understand better :) We are glad that you've enjoyed your learning experience with us .Thank you for being a part of Edureka's team:) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
We are very glad to hear that your a learning well with our contents 😊 continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
Well explained !! got a high level overview , for intuitive understanding of few terms referred google . All in all thankyou for this vid just one correction at 30:15 one entropy should of rainy but both are written as sunny.
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
I HAVE A QUESTION: Why was the temperature feature totally ignored in the dataset while building the decision tree? While Outlook, Humidity and Windy were all chosen.
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
very informative video I really need to learn more about Machine Learning in Python I wish that you post more videos in that useful and interesting topic. Like d this video , Thank you
Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: th-cam.com/video/b2q5OFtxm6A/w-d-xo.html Hope this is helpful. Cheers!
Sir , I was assigned a project on fraud detection . Which algorithms should I learn to train many transactions and detect if a transaction is legitimate or fraud ? I wanted to implement in Python . Please guide me in this project .
Hey Manivarma, Classification and clustering algorithms are good for fraud detection and anomoly detection. So algorithms like, SVM, KNN, K-Means, Decison trees, Random forest are relevant. Hope this helps!
Hi there, this probably depends on the kind of data you have collected. If you have good labelled datasets which can help you understand the persons mental state then this algorithm would definitely work. But if it is something where your dataset is broken and not complete and you cannot predict any reason which causes the suicide, then unsupervised algorithms will work. Hope that is helpful.
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
This lecture was exactly hitting on the nail, this helped in clarifying my doubts. Is it possible to share the link for this python code, that could be very helpful! Thanks
Absolutely stunning, very well explained, clear ideas, awesome stuff & remarkable cases...substantial learning at high level. Thanks & regards from Costa Rica.
The code is not there in the description .. can you get me that? The video lecture was awesome.. I tried doing alongside..but have some errors. If given the sample code may be I can check it out. Cheers
Hey Rishi, thank you for watching our video. We are glad that you liked our content. Sure, mention your email address and we will share it with you. Cheers :)
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks for the appreciation, Keerthi! Please mention your email id (it will not be published). We will forward the code and dataset to your email address.
Hey Raj, You can set the variable to r rangle. For example, if you have around 5000 tuples, then you can use just 200 tuples and then assign it to train data. After that you can use the algorithm and test the data based on the chosen tuples
Good to know your learning with Edureka 😊 please share your mail id to share the data sheet! We'll Update you soon ! Do subscribe our channel for more such videos..
Hey Vishwanath, "indico.cern.ch/event/472305/contributions/1982360/attachments/1224979/1792797/ESIPAP_MVA160208-BDT.pdf This might be useful to you. Cheers!"
Thanks for showing interest in Edureka kindly visit the channel for more videos our content creators are eagerly waiting for your suggestion to make new videos on your interest :) DO subscribe for the video update
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Why didn't 'temperature' come into picture while constructing the decision tree? Is it because the information gain is least compared to all other nodes? if yes, then every time when we construct the decision tree should we ignore the parameter with least info gain?
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
We are happy that Edureka is helping you learn better ! We are happy to have learners like you :) Please share your mail id to share the data sheets :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Hello, Thanks a lot for this tutorial...can I get the code please? as I'm facing few errors while running it like I Got the error that Question takes no argument. Please share the code.
Hi Abhishek, in practice, some form of cross-validation is typically applied. However, there are ways to make an informed pre-selection. You can go with a maximum margin classifier such as support vector machines. It can be considered the best off the shelf classifier to date.
Hey, great video but I have a question i am working with a data set that has 569 instances and 30 variables, problem is that the variables aren't like the example, they are not standard options, like shown in the video where outlook has 3 distinct options, these variables are all doubles, they range from 7.33 up to 22.45 or something like that, so i'm really not sure how to calculate entropy for that
edureka! In the above video explanation was superb and appreciated . But for the same above example how can I calculate the probability for each user or each id after building the model?
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: bit.ly/2OpzQWw
code explanation should be slow as it is a key area just moving ver fast
Best tutorial on TH-cam!
Excellent teaching, great explanation.
Thank you sir.
How did you decided the position of windy and humidity?
Good teaching and animation......
Thank you! I have been looking for a video all week that would break "decision tree" down for me. This is it!
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
WOW great work
Crystal clear 🙂 thanks 🙏
You are welcome 😊 Glad it was helpful!!
Best video!
thank you for the clear explanation!
You are welcome :) Glad it was helpful!
Very Nicely Explained .. Thanks ..
Thanks for great explanation
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Satisfactory explanation among all resources.... 10 out of 10
Great to see that our videos and contents are making you perform better and understand better :) We are glad that you've enjoyed your learning experience with us .Thank you for being a part of Edureka's team:) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Kindly . Correct The thing
On timestamp 28:42
The last Entropy calculation should be of rainy but taken of sunny again.
thank you, it's really helpful
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Right, Alright!
very well explained the math behind the decision tree. thank you
We are very glad to hear that your a learning well with our contents 😊 continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
Well explained !! got a high level overview , for intuitive understanding of few terms referred google . All in all thankyou for this vid just one correction at 30:15 one entropy should of rainy but both are written as sunny.
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
I HAVE A QUESTION: Why was the temperature feature totally ignored in the dataset while building the decision tree? While Outlook, Humidity and Windy were all chosen.
what an explanation.simply superb sir .so simple and easily you explained
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you a lot for creating this for a beginner like me.
You're welcome 😊 Glad you liked it!! Keep learning with us
its really awsm........
very helpful...
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
I think it is best video for decision tree. Can you please give me the notes that you used to teach.
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
very informative video I really need to learn more about Machine Learning in Python I wish that you post more videos in that useful and interesting topic. Like d this video , Thank you
Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: th-cam.com/video/b2q5OFtxm6A/w-d-xo.html Hope this is helpful. Cheers!
Awesome explanation
Sir , I was assigned a project on fraud detection . Which algorithms should I learn to train many transactions and detect if a transaction is legitimate or fraud ?
I wanted to implement in Python .
Please guide me in this project .
Hey Manivarma, Classification and clustering algorithms are good for fraud detection and anomoly detection. So algorithms like, SVM, KNN, K-Means, Decison trees, Random forest are relevant.
Hope this helps!
Am building a model to predict a likelihood that someone may commit suicide..
Can i use this algorithm?
Hi there, this probably depends on the kind of data you have collected. If you have good labelled datasets which can help you understand the persons mental state then this algorithm would definitely work. But if it is something where your dataset is broken and not complete and you cannot predict any reason which causes the suicide, then unsupervised algorithms will work. Hope that is helpful.
Thank you for making it clear and concise, additionally can you please provide the source code ?
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
Great explanation! Thanks a lot!
This lecture was exactly hitting on the nail, this helped in clarifying my doubts. Is it possible to share the link for this python code, that could be very helpful! Thanks
Please share your email id with us (it will not be published). We will forward the code to your email address.
Thx .it's great
Really great explanation ... awsome video ... i understand very clearly and like it🥰
hey great explanation .. covered all the topics neccesaary could you please share the code
Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.
Great Video . Thanks much.
Excellent session . Is it possible to provide the python codes ?
Hi, This is Surender, great explanation, can you please provide the python code.
Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.
Excellent explanation
Thank you for the explanation. Can you please share the code?
Thanks for showing interest in Edureka! Kindly share your mail id for us to share the datasheet/ source code :) Do subscribe for more videos & updates
Nice tutorial， Decision Tree well explained
very nice explanation sir .........Great Thanks to You...
Hey Ram, glad you loved the video. Do subscribe and hit the bell icon to never miss an update from us in the future. Cheers!
Very helpful, really obliged of edureka 🙏
wonderful
great video! You made it simple and clear, thank you so much
Anyone with no prerequisites will able to understand Edureka all classes.
Hey Souman, that is our aim. Thank you for appreciating our efforts! Keep supporting us, cheers :)
Absolutely stunning, very well explained, clear ideas, awesome stuff & remarkable cases...substantial learning at high level. Thanks & regards from Costa Rica.
CART Algorithm uses Gini Index but you have implemented the dataset using entropy and Information gain so it will not be ID3 Algorithm?
The code is not there in the description .. can you get me that?
The video lecture was awesome..
I tried doing alongside..but have some errors. If given the sample code may be I can check it out.
Cheers
Hey Rishi, thank you for watching our video. We are glad that you liked our content. Sure, mention your email address and we will share it with you. Cheers :)
What is decision tree with relearning of nodes?
Nice one ....good
very clear. Thank you so much :)
Hey Santosh, we are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
Good explanation
Awesome video guys. one small request can I get the demo code used here for my practice ?
Hey Indrajit! We are glad you loved the video. Please do mention your email ID over here and we will send the files to you. Cheers!
Great session
Very clear explain. May I have the source code?
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Nice explanation. Could you please share the code, it would be helpful, Many thanks in Advance!
Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.
Very Well explained ..👍
Can I get code file??
Hi Ashwat, kindly drop in your email id to help us assist you with the required source codes. Cheers :)
Awesome video. May I have the code that was used at the end of the tutorial? Thanks in advanced.
Thanks for the compliment! Please share your email id with us (it will not be published). We will forward the code to your email address.
All Right!
Thanks alot for the wonderful video. kindly share the code plz asap.
Please share your email id with us (it will not be published). We will forward the code to your email address.
Thank you, you made it clear. Could you please share the code?
Hi, kindly mention your email id in the comments to help us assist you with the required source codes, cheers :)
What are computational complexity for Decision Tree? Can you please give analysis for tree training phase and prediction phase please.
Hi Sir, Please share the link to the code that you have explained above. Thanks.
Please share your email id with us (it will not be published). We will forward the source code to your email address.
hii sir...nice video please share the source code and dataset
Thanks for the appreciation, Keerthi! Please mention your email id (it will not be published). We will forward the code and dataset to your email address.
If the training_data is huge then how can we make the necessary changes and get the same correct output?
Hey Raj, You can set the variable to r rangle. For example, if you have around 5000 tuples, then you can use just 200 tuples and then assign it to train data. After that you can use the algorithm and test the data based on the chosen tuples
Excellent analysis. Thank you and remain blessed.
Thanks for the great video. Can i have the code please?
Good to know your learning with Edureka 😊 please share your mail id to share the data sheet! We'll Update you soon ! Do subscribe our channel for more such videos..
Hi, Could you guys give me some guidance to implement the decision tree algorithm in torch7 using Lua.
Hey Vishwanath, "indico.cern.ch/event/472305/contributions/1982360/attachments/1224979/1792797/ESIPAP_MVA160208-BDT.pdf
This might be useful to you. Cheers!"
thank you
great explanation..may i have the code
Thanks Obaid. Please share your email address, we will send you the code.
Very informative video.
Can you share the code as our reference?
Thanks.
Thank you. Please share your email id with us (it will not be published). We will forward the source code to your email address.
Nice Video and great explanation .Can i get the source code pls
Thank you. Please mention your email id (it will not be published). We will forward the code to your email address.
Sir please can you keep python live class pls sir
Thanks for showing interest in Edureka kindly visit the channel for more videos our content creators are eagerly waiting for your suggestion to make new videos on your interest :) DO subscribe for the video update
Nice video. I was really helpful. Please can you send me the source code??
Can you please provide the code, it will be a great help!
Please share your email id with us (it will not be published). We will forward the code to your email address.
Great video. But could you please provide the source code. It will be helpful for us to study it.
Hi great to hear from you :) please share your mail id ! so that we can share the data sheet with you :)Do subscribe the channel for more updates : )
Great explanation. Can you share the code
Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.
Well explained. Can you please share the source code?
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Is the range of entropy 0 to 1 as shown at 23:28 ?
Hey Sai! Yes, entropy has a value between 0 and 1.
can u please share the code it would be helpful
Prior thank you
Hey Pratima, hope you found the video informative. Please do share your email id(we won't publish it) so that we can mail the files to you. Cheers!
Can u provide practice problems with solution.
Thanks you for showing interest in edureka and Thanks for you priceless suggestions & feedbacks :) DO subscribe for more updates and videos to come
A great refresh of decision tree. Thanks!
Hey Terry, we are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
Why didn't 'temperature' come into picture while constructing the decision tree? Is it because the information gain is least compared to all other nodes? if yes, then every time when we construct the decision tree should we ignore the parameter with least info gain?
Hey, You are correct, it is because it is the least compared element. Not that you should always ignore it but it depends on the use case.
Nice video. I was really helpful. Please can you send me the source code t practice
Hi Habiba! Can you please share your email id with us (it will not be published). We will forward you the source code to your email address.
great help for me. could I get the code?
Hi Hasan, kindly drop in your email id to help us assist you with the required source codes. Cheers :)
amzng..sir
hi could you please share the ppt material and code used in this video. This is very helpful for my assignment
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
The video is awesome, can i get the link to source code?
Hey Tanmay, we are glad you loved the video. Please do mention your email ID over here and we will send the files to you. Cheers!
Can anyone tell how windy child decided to strong and or weak and how its value is true and false also decided?
Nice video, may i have the code that was used at the end of the video.
Thank you. Please share your email id with us (it will not be published). We will forward the source code to your email address.
Can I please get the entire source code to review please? It'd be of great help!
We are happy that Edureka is helping you learn better ! We are happy to have learners like you :) Please share your mail id to share the data sheets :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
The code and data is not available in description. Please share the link.
Please share your email id with us (it will not be published). We will forward the code and dataset to your email address.
Hello, Thanks a lot for this tutorial...can I get the code please?
as I'm facing few errors while running it like I Got the error that Question takes no argument.
Please share the code.
Hi Parul, we do provide practice codes to enhance your learning experience, kindly drop in your email id to help us assist you with it. Cheers :)
what did you import for doing tree decision?
Hi Nur, You have to first import the required libraries and datasets. Hope this helps.
How we know that which algorithm is best for our data????
Hi Abhishek, in practice, some form of cross-validation is typically applied. However, there are ways to make an informed pre-selection. You can go with a maximum margin classifier such as support vector machines. It can be considered the best off the shelf classifier to date.
sir can i get code of it?
Please share your email id with us (it will not be published). We will forward the source code to your email address.
where is code in description
Hey Digi Bro, mention your email address and we will send it over. Cheers :)
hi!! could you please share the link to python code explained in the video??
Please share your email id with us (it will not be published). We will forward the code to your email address.
Hey, great video but I have a question i am working with a data set that has 569 instances and 30 variables, problem is that the variables aren't like the example, they are not standard options, like shown in the video where outlook has 3 distinct options, these variables are all doubles, they range from 7.33 up to 22.45 or something like that, so i'm really not sure how to calculate entropy for that
Hey, Entropy is straightfoward and really simple to calculate. Can you elaborate?
Have few doubts...can Any kne clarify
Hey Sai,sure. We will try helping you out. You can mention it here itself. Cheers :)
edureka! In the above video explanation was superb and appreciated . But for the same above example how can I calculate the probability for each user or each id after building the model?
edureka! I mean to say can I calculate the probability for my train data set and test data set based on individual??