Principal Component Analysis (PCA) is a statistical process that turns a set of correlated variables into a set of uncorrelated variables using an orthogonal transformation. In exploratory data analysis and machine learning for predictive models, PCA is the most extensively used tool.
Principal Component Analysis is an unsupervised learning approach used in machine learning to reduce dimensionality. With the help of orthogonal transformation, it is a statistical technique that turns observations of correlated features into a set of linearly uncorrelated data.
The major components are orthogonal because they are the eigenvectors of a covariance matrix. The dataset on which the PCA approach will be applied must be scaled. The relative scale has an impact on the outcomes. It is a means of describing data in layman's terms.
PCA is a method for lowering the dimensionality of such datasets, boosting interpretability while minimising information loss. It accomplishes this by generating new uncorrelated variables that optimise variance in a sequential manner.
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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
Extremely helpful and I recommend to my peer as well for this course.
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Aniket Kumar prasad
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Very helpful and easy to understand all the concepts, best teacher for learning ML.
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Rishu Shrivastav
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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
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very nicely explained
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Vrushali Kandesar
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Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Juboraj Juboraj
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Joydeb
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Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
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Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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