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Lec-46: Principal Component Analysis (PCA) Explained | Machine Learning

Video Overview & Insights

In this video, we explain how Principal Component Analysis (PCA) works and how it's used for dimensionality reduction. Learn how PCA identifies the principal components of your data by calculating eigenvalues and eigenvectors, and how these components capture the most important features of the dataset.

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ai english

— @spnoone

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Timestamps:

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How calculate eigen vector atleast should have tell us

— @ayushkumardwivedi415

00:00 - Introduction

03:02 - Steps of Principal Component Analysis(PCA)

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abe kiya galat padara hai khudko aata ho to padhyega na

— @rusher-t91

04:18 - Step 1

04:35 - Step 2

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why you dont calculate eigen values and vector shi chl rha tha lecture pura confusion ho gya ab mujhe kahi eigen value and vector mil hi nhi rhi shi si video

— @shamsabbas110

08:50 - Step 3

11:02 - Step 4

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He said to calculate eigen vectors using larger lambda value, but the answer for vectors that he showed in next slide is accurate with the lower value, please clarify properly sir...

— @manasprb

11:53 - Step 5

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Thank You Sir ❤️

— @mr.priyanshu_Patel

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last past is not understand properly eigen vector

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Sir shouldn't the eigen value be >= 0, it can't be negative. I calculated the eigen values, it came out to be -
L1 = 34.32
L2 = 0.35

— @lolbuiiiii

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sir when i solve this by my own the u1 vector is 1 and u2 vector get 2

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How to solve eigen vector

— @KamranAkmal12348

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very helpful sir

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LAMDA KAYA HAA???

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you didnt even explain any calculations it was very fast paced solving

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Should you multiply the eigen vectors with original data or mean centred data? Step 5: Project: X_centred × eigenvectors?

— @AnkurPandeyBandhu

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Really ! useful video ....

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the value of cov(x1,x2) is 12.25 i am getting

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e₁ᵀ [ X₁k − X̄₁
X₂k − X̄₂ ] this is the crt value of principal component..there's a mistake in the video.. he has done.. e₁ᵀ [ X₁k ; X₂k ]

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The last step of project is wrong; we use mean center matrix to compute final dimensions, not the matrix that is given

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Thank you sir❤

— @snehabhattacharjee2865

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I do not get eigenvector in minus, I got u1 = 1 : 1.33. please, clearify I am correct or wrong

— @rajamanish1231

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Eigen vector here is calculated by taking smaller value of eigen value .335

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More User Perspectives

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when solving eigen vector the use the eigen value we calcualted earlier 34.3 for lamda in eigen vector matrix and then form two equation and solve them by letting in the end equation becimes something like u2=1.325ui which means u2 is 1.325 time the u1 as to my kwowledge by in video matrix has wrong inputs as 1.325 should be positive and not negative 11:04

@haziqzaman1099

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Yeah hindi main kaise karu AI Voice aa rhi

@90s-20sWWE_Fans

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Yeh video english main kyu aa rhi hai 😢

@90s-20sWWE_Fans

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Eigen Vectors are = [u1,u2] = [ 0.602, 0.798]

@md.akkhorhasan3025

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where did we use mean

@ayushgoyal8972

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Thank you, sir, for these lectures you have provided on YouTube.
I think, sir, in Step 3: Calculate the Eigenvalues and Eigenvectors, the eigenvector should be in normalized form.

@AjayramMeenaVIP

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SHOW THE CALCULATION OF EIGEN VECTOR

@RiteshAsole-pf5er

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he calculated wrong eigen vector it must be +1.325 not (-)

@pranavmahajan9443

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The eigen vector part should be taught, it is the most difficult part...

@AR_--007

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sir is it /n or /n-1 during the covariance calculation please reply fast sir tomorrow is my exam 🙏🙏

@Giant_Monster9

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DAMN WONDERFULLL

@Kunal-bu1ur

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11:08 i am getting u1 as 0.7548
to get -1.3256 as u1 then lamda should be 24.78

@I_N_M_A_R_K

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jab pdhana nhi aata to kyu pdhate ho sir... lab me 10 marks glt krva diye mere... ye u1 or u2 direct values oe sath multiply nhi hote .mean ke sath hote h.🙏🙏🙏

@additional6501

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Sir candidate elimination algorithm ka video karo please

@RakeshKumar-ey6fz

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Originall audio Needed here. I feel awkward when I listen to AI voice

@banil636

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many steps skipped, first time going with confusion from your video.

@Gk_1234-t2z

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if u are teaching something get a better mic

@anuragdalbehera1071

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