PCA Indepth Geometric And Mathematical InDepth Intuition ML Algorithms

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science.

[exploreEverything]

First I understood pca concept 3 years back from nptel lecture. It was full of mathematics and It went far above my head because the theory part was missing. Believe me with your explanations I can understand his lecture too. No one could explain the way you have explained. It was outstanding.

[aditinautiyal]

Thank you so much for not only sharing your knowledge but also putting so much effort to cover each and every point of the particular topic.

[IshanGarg-yu]

This is a good video, I recommend first you watch PCS step by step guide from stat quest to get a high level view with animations, then you watch this video to get more details and understanding alongside some code. Then in case you want to know the mathematics behind it refer to some articles online where the explain why we calculate the covariance matrix, then build the objective function using lagrange multiplier and then derive why eigen values of covariance matrix are the desired results

[aj_actuarial_ca]

PCA is so very well explained in your video sir. You're really the best teacher ever !!!

[pritamrajbhar]

thanks a lot, Krish this is the simplest and most detailed video about PCA.

[akashpaul]

You really are a good teacher brother... Teaching with relatable examples help to understand each topic so perfectly and easily.. Thank you so much brother.. Keep teaching us...
Love from Bangladesh

[manmj]

thank you for this elegant effort in explaining PCA

[syco-brain]

best video about pca on internet so far

[Harsh_Yadav_IITKGP]

Krish your efforts are remarkable in this ml

[adnanshujah]

best of the best lecture .covers all the required concepts about subject . most of videos available only shows how to perform PCA but not whay it is required and concept behind it .but sir Krish thankyou so much for such a detailed lecture and clearing the concepts . highly recommended lecture and his channel
🥰🥰🥰🥰🥰🥰

[yogendrapratap]

Everything had been really resourceful in lecture series but this lecture was overly extended, 30 min topic has been extended to 1 hours 30 mins repeating same stuff again and again

[paneercheeseparatha]

Wonderful try to explain PCA without much mathematics. Though it would be great if you also do a video on implementing PCA from scratch in python. Loved your playlist! kudos to you!

[ashwintiwari]

No where I can find this explanation it's too good no confusion no complex demonstration use cases a cleanest and simplest way to understand PCA in depth thanks alot Krish it takes lot of takes and research to explain single topics in data science and in this way it's all appreciated work

[SanthoshKumar-dkvs]

Thanks for sharing Krish really helpfull, last two days am refreshing this topic only🤗

[vinothkumar]

You are a great teacher I ever seen in my entire life.The way you are teaching even makes the lazy or slow learner to a strong learner using Krish Naik g(ji) Boosting algorithm.Just Kidding 😃😃.Hatsoff to your effort to help the people.

[taslima]

You are my favourite youtuber and teacher.

[samareshms]

This guy is single handedly carrying the AI ML community in the India 🙇‍♂🙇‍♂

[dipamsarkar]

This guy should be named as "God father of Data Science India" an absolute legend

[amitx]

Sir, I thing have felt strongly is that you expain and deliver a little better in recorded videos. Thanks for providing such great content for us for free!

[viratkumar]

Its quite vage to say if pearson correlation value is zero there is no relationship between x and y. Example consider Y= mod(X) line the person correlation is 0, but still there is relationship easily visible after plotting