Principle Component Analysis (PCA) | Part 1 | Geometric Intuition
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- Опубліковано 16 лип 2024
- This video focuses on providing a clear geometric intuition behind PCA. Learn the basics and set the foundation for understanding how PCA works in simplifying and preserving important information in your data.
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⌚Time Stamps⌚
00:00 - Intro
00:44 - What is PCA
05:16 - Benefits of using PCA
07:33 - Geometric Intuition
25:01 - What is variance and why is it important?
So grateful for the videos that you make. I have burnt my pockets, spent hours on various courses just for the sake of effective learning. But most of the times I end up coming back at campusx videos. Thank you so much.
ho gya to?
Same
04:15 PCA is a feature extraction technique that reduces the curse of dimensionality in a dataset.
08:30 PCA is a technique that transforms higher dimensional data to a lower dimensional data while preserving its essence.
12:45 Feature selection involves choosing the most important features for predicting the output
17:00 Feature selection is based on the spread of data on different axes
21:15 PCA is a feature extraction technique that creates new features and selects a subset of them.
25:30 PCA finds new coordinate axes to maximize variance
29:45 Variance is a good measure to differentiate the spread between two data sets.
33:54 Variance is important in PCA to maintain the relationship between data points when reducing dimensions.
Unbelievable...nobody taught me PCA like this.... Sir 5/5 for your teachings 🙏🙏 god bless you ❤️
I am interested with you for group study, reply me bro
It's like im watching a NF series , at first you're introduced to different terms, methods their usecases and in the last 10 mins of the video everything adds up and you realize what ahd why these stratigies are in use. Amazing.
You have done good research on every topic bro ,nice explanation ..I am so happy I found this channel at the same time feeling bad for not finding it earlier
Exactly. same here
Can't have better understanding of PCA than this..Saved so much time and energy..Thanks a lot
Wow , i regret why I did not get to this channel, very clear as a story , i can explain a 6 year old and make him/her understand ❤️👏
I am loving this channel more and more everytime I see a video here.The way content is presented and created is really awesome.Keep Inspiring and motivating us.I am learning a lot here.
Awesome explanation and best part is how he drops important info in between the topic, like such a good interpretation of scatter plot is in this video which i wouldn't find even in dedicated scatter plot video. So perfect.
Sir , the way you explained the Curse of Dimensionality & its Solutions in Previous vedio -- Just mind blowing ..... YOU ARE GOD
No words to express how precious your teaching is....
one of the finest explanation of pca I have ever seen Thankyou Sir!
Totally an Awesome playlist for learning Data Science/Mining or for ML. Thank you so much sir! Means a lot!!
Never have I seen a better explanation of PCA than this!
Beautifully explained !!! Probably the best analogy one could come up with. Thank you, sir.
Top have this level of teaching, one should have deep level of understanding both from theoritcal as well as practical aspects. You have proved it again. Thank for providing such valuable teaching.
Very nicely explained topics. One of the best teacher on ML.
I thank God for blessing me with this teacher.
Amazing explanation....NO one can explain pca as easily as you have done. Better than IIT professors.
Excellent sir
I have listened to different video lectures on PCA,
But i didn't understand it properly.
But your's is the best one.
Thank you so much
First of all the playlists is amazing you have done a really good job in explaining the concepts and intrusions behind the algorithms, I was wondering could you create a separate playlist for ARIMA SARIMAX and LSTM algorithms i really want to see those above algorithms in future class
Wow, how simply you did it.
Best Video for PCA. I'll definitely recommend to my friends 🙂
U r outstanding for me sir...i can't able to understand untill i watch your video
U rock dude! Really appreciate that
Amazing explanation!!
thanks for the great explaination please keep explaining in this way only
बहुत सुंदर है👍👍🙏❤️🔥
Thanks for the explanations!
Thank You Sir.
Great content!!!
Amazing Explanation
hats off to you sirrrr
amazing explanation
Just Wow 🔥 😍
You are so good in this, i m like 'tbse kha thae aap'
Nice Presentation sir
great example
best explanation
Wowww!!!! Best video
Damn, you are the Messiah in ML teaching
your teaching style is amazing , you are gem
I am interested with you for group study, reply me bro
Such an underrated channel for ML.
thanks
Best course for ML
Variance of grocery shop is greater than number of rooms but you have shown reverse..
Amazing explanation... Can you share this one note for windows 10 notes of this entire series "100 days of Machine Learning"
Excellent
Hi Bro,please make videos on feature selection techniques
you are the god
what is the difference between feature extraction and feature contruction as both are reducing the no of features?
Very nice explanation my i know which hardware you use to write on the notepad?
sir you have no idea, how much you are helping data learners like me. thanks a lot. how can i help you. is there any where i can pay to you as a token of appreciation ?
Is it possible to have an example of pictures to classify them into two categories?
If the dimensions are reduced in pca and classification in knn is better , please
Cleaver explaination
that is what we do
I have a doubt, If a variable is in range 0 to 1 and another variable is in range 0 to 1000(will have more variance / spread ). Why choosing 2nd variable just by looking at variance make sense? It may be matter of units like in km and cm. For this problem we use scaling. Am I right?
but agar PCA ke geometric intuition mai mai clockwise ghumau axis ko toh variance toh rooms ka kam ho jaega na , or agar mai same process kru by taking washroomn on x axis and rooms on y tab toh washroom select ho jaega na ??
solid
Dear sir I am confused about the variance formula and your interpretation. Kindly recheck.
24:24 Aha! So PCA finds an alternate co-ordiante system and uses the change of basis matrix to transform the data.
bhai ye video viral kiyu nahi ho raha hai ..thank you sir ❤
sir your videos are really amazing, I had learned a lot from your videos. But I have a doubt in feature construction and feature extraction. They both are looking similar. So can you please ,tell me the one major difference between these two.
I am interested with you for group study, reply me bro
Bhai ek playlist dedo for statistical application in Data Science
Day3:
Date:11/1/24
sir i just wanted to ask that can we write our own machine learning algorithms instead of using sklearn and tensorflow i mean from scratch plz make a video about that. I have been following you whole series. Sir do reply. Thanks to your efforts
Ha Likhsaktr ho yaar...
Yes you can...
I am interested with you for group study, reply me bro
@@vikramraipure6366 actually currently i am working on some other project so.. i am sorry..
thanks for the proposal!
My suggestion is use sklearn library for existed algorithms. If that doesn't work create your own algorithm.
Sir notes milege app ki
Thanks you sir
I am interested with you for group study, reply me bro
Done....Give me your mobile no.
..... I will call i when I free
❤❤❤❤❤❤❤❤❤❤❤❤❤❤
This work same SVM? 🤔
Yes
@@campusx-official tq sir .. your class aswame 🙏
04:15 PCA is a feature extraction technique that reduces the curse of dimensionality in a dataset.
08:30 PCA is a technique that transforms higher dimensional data to a lower dimensional data while preserving its essence.
12:45 Feature selection involves choosing the most important features for predicting the output
17:00 Feature selection is based on the spread of data on different axes
21:15 PCA is a feature extraction technique that creates new features and selects a subset of them.
25:30 PCA finds new coordinate axes to maximize variance
29:45 Variance is a good measure to differentiate the spread between two data sets.
33:54 Variance is important in PCA to maintain the relationship between data points when reducing dimensions.
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