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Please make videos on other dimensionality reduction techniques like Factor Analysis, Linear Discriminate Analysis, Canonical Discriminate Analysis, Cluster Analysis & where to use which method including PCA. It will be really helpful for learners.
Hi Kabir, thank you for your brief list of topics. We have considered it and we will soon come up with the tutorials. In the meantime, do subscribe to our channel and also, checkout: bit.ly/3jbgpQc the certification courses we offer. Happy learning :)
Thank you soo much for explain PCA in detail, I was tried to understand the concept behind the PCA by reading few articles, but i didn't got exact point. But here, in this video i understand PCA clearly.
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:)
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 : )
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:)
How to get and print these values: 1. number of features? 2. number of samples? 3. number of PC components? 4.percentage of variance explained by each of these PC components?
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Within a 6 minute of timing, I come up in chat box and drop a " Gulabi dil"( Golden heart) ❣ ❤️ I've also subscribed the channel because this video won my heart.your explanation is truly remarkable and outstanding Your each word is worthwhile and have a significant meaning.thanx for being my partner in troubles and removing the confusion. From eureka import concepts. Thank u 💟💯
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 : )
Hi ! Good to know that our videos are helping you to learn better :) Please share your mail id to share the data sheets :)We’ll update you soon . Do subscribe the channel for more updates : )
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Machine Learning & AI Masters Course Curriculum, Visit our Website: bit.ly/2QixjBC
Thank you for this clear explanation. I've been struggling with this for a few days and this helps me get on a road to finishing a project!
Great Job Edureka while others are looting money and providing poor content and you guys providing the wonderful content for free. Take a bow.
Great video, I like the youtube videos of Edureka than their classroom content.
Mam u exolained so beautifully. I understood the topic very well better than any other TH-cam channel I viwed. ❤❤❤
It's my pleasure
This helps a lot! Great intro on PCA with good detailed info and clear steps. Thank you.
One of the best videos on PCA. Great job.
thanku mam .your teaching method is best. from pakistan
Please make videos on other dimensionality reduction techniques like Factor Analysis, Linear Discriminate Analysis, Canonical Discriminate Analysis, Cluster Analysis & where to use which method including PCA. It will be really helpful for learners.
Hi Kabir, thank you for your brief list of topics. We have considered it and we will soon come up with the tutorials. In the meantime, do subscribe to our channel and also, checkout: bit.ly/3jbgpQc the certification courses we offer. Happy learning :)
Explanation was just Simply Great.
Video was awesome.pls make a separate video on t-sne also.thanks
Thank you so much for explaining the concepts thoroughly behind these techniques!!
Clear explanation !
Glad you think so!
Very good explanation. Thank you..
really great yar
Thank You 😊 Glad you liked it!!!
I have reached to a stage where I first press like button of your videos and then watch them. Kudos to Edureka team!
thanks for the explanation. However I have a question. How do we know which features\ components are selected after PCA implementation?
Thank you soo much for explain PCA in detail, I was tried to understand the concept behind the PCA by reading few articles, but i didn't got exact point. But here, in this video i understand PCA clearly.
End to end covered...... Thanks edureka!
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:)
Greatest video...everrrrr ...thank you mam...really appriciated
Great video!!
Thank you so much. I have been struggling a lot to understand this topic from few days. I have got clear explanation here❤.
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 : )
Good Video for beginners
Nice explanation.
Thank you for this...it was a great explanation
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:)
The explanation in the video is good. Thanks
Wow what a coincidence😙
Actully i was browsing in google for thus topic today and now ur video😀
superbb.......
perfect
how to take projection of the data set in a choosen dimension ?
Amazing, thank you Edureka.
You are welcome👍
Do subscribe to our channel to stay posted on upcoming tutorials: th-cam.com/users/edurekaIN.
Awesome! Edureka
Good video
Very very informative
How to get and print these values:
1. number of features?
2. number of samples?
3. number of PC components?
4.percentage of variance explained by each of these PC components?
And also , How to solve problem using biplot in this given data which is in video?
Plz provide the dataset link mam... It will help me to practice more..
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 : )
Dear Mam , please share the code and data in description so that we will do practice on that
Glad to hear our contents are helping you ! Please provide your mail id to share the data sheet :) We’ll update you soon
Within a 6 minute of timing, I come up in chat box and drop a " Gulabi dil"( Golden heart) ❣ ❤️
I've also subscribed the channel because this video won my heart.your explanation is truly remarkable and outstanding
Your each word is worthwhile and have a significant meaning.thanx for being my partner in troubles and removing the confusion.
From eureka import concepts.
Thank u 💟💯
where do we find this dataset? Could you please provide the link!
You can get it from github. Use the following link: github.com/datasets
can we have dataset after reduction? I mean the no. of columns are not reduced right....... so if i want to reduce the no. of columns what should i do
How do u know which columns got removed?
Do you have the python example somewhere available for download?
can you please share the code and the dataset
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how can we join your course
Hi thanks for showing interest in edureka click on this link
bit.ly/33gyWaF to get all the details for our course and enroll to that course
please share the code used in the video, cannot read them in the video.
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can I have the code discussed in the video????
Please share your email id with us (it will not be published). We will forward the code to your email address.
Great explanation
Excellent explanation.
Nice explanation .
Thank you. Do subscribe to our channel for more amazing content. Cheers!
Great Explaination