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Thanks❤

Very good explanation.thank you

Ma"am I was looking for this type of content i.e concept on Information gain and entropy. After Wasting 1-2 hours on different channel finally i landed here ..Thanks a lot ❤.

Notes please

sao bt đc ra v1 v2 vậy ko hiểu phần đó

you are great and try to make the whole lesson at inglish

The best pca class ever in youtube.

the formula for quadratic formula is not right you forgot -b in start

nice explanation

Thank you soo much Mam

4 yeass ago thanks ma'am 😊

🔥

mam idu exam choikan chance undo?derivation

Thankyyyyy

Thanks

you are amazing teacher...which book are you following mam?

Ithintte output onn kaanikkamo after deletion num nammal enter cheyyano

വളരെ നല്ല ക്ലാസ്സ്‌ ആണ് 👍👍

This steps are also applicable for 3 centroid also ? Help me.....!

It's really fantastic ❤

baji aap ny 0:57-1:07 kaya bola smaj nahi aaya

Thank you so much

Good

Madam, which language do you speak in the middle of your sentences sometimes? second you explain very well but your Quadratic Equation values are correct but not the formula you mentioned at 12:36

Thank you so much ❤

Best way!!! I learnt theory, but simply it wasnt enough… she just explained it so clealry!!

tq mam super voice

Beautiful

😊😊

nice teaching i liked it very much

Thanks, tomorrow I have machine learning exam. Concept manasillay. Anna university syllabus only cv and k-fold cv

Thanks

Formula is wrong

Your explanation is super but your translate English or Telugu.

my friend osama said he love you

讲的好

Nice video and excellent explanation! (¡Muchas gracias por el ejemplo!) It is a good exercise to teach in a class in conjunction with the video. Here is a R code in Spanglish that checks the result obtained in the video. # Step 1: Create the data xx <- c(4, 8, 13, 7) yy <- c(11, 4, 5, 14) datos <- data.frame(xx = xx, yy = yy) # Step 2: Compute de means and center the data colMeans(datos) datos_centrados <- scale(datos, center = T, scale = F) # Step 3: Find the covariance matrix matriz_de_covarianzas <- cov(datos_centrados) # Step 4: Calculate the eigenvalues and the eigenvectors autovalores <- eigen(matriz_de_covarianzas)$values autovectores <- eigen(matriz_de_covarianzas)$vectors # (Like columns) autovectores <- -autovectores # The minus sign is arbitrary # Step 5: Derive the new dataset matriz_de_loadings <- t(autovectores) matriz_de_loadings # e1 = first row, e2 = second row # PCA1 = matriz_de_loadings[1, 1]*xx + matriz_de_loadings[1, 2]*yy # PCA2 = matriz_de_loadings[2, 1]*xx + matriz_de_loadings[2, 2]*yy datos_transformados <- t(matriz_de_loadings%*%t(datos_centrados)) datos_transformados

Is it a derivation

Plz teach in english

Good mam, please teach in only English

nicezzyy

Thx ma'am the vedio was very useful ✨

*What is this language in between aare ooo aaddaa* 🤮🤮🤮😂😂😂

Thank you so much mam...this was really helpful 🌟

Thankyou madam I don't understand that malyalam but understood the concept. Nandi🙏💝

I'm from Andhrapradesh But i understand this prblm very well ❤‍🩹 Thank you mam

You need to come to my college to teach statistics.....so nice explanation....

Thanks mam

Distance formula is wrong mam

Is the normalization part correct? 11/27.3848 and -16.3848/27.3848 is what I thought is correct. Also for eigen vector, Taking the second eqn. would have yield diff eigen vector, so how to decide on which eqn to take.