PCA Code Example Using Visualization | Dimensionality Reduction Lecture - 19 | Applied AI Course

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PCA Code Example Using Visualization | Dimensionality Reduction Lecture - 19 | Applied AI Course
#visualization #machinelearning #appliedaicourse
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#ArtificialIntelligence,#MachineLearning,#DeepLearning,#DataScience,#NLP,#AI,#ML
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Опубликовано:

18 сен 2024

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Комментарии : 19

@niharjamdar4869 3 года назад

One of the best explaination on PCA so far on RU-vid thanks a lot ❤️

@samcool6569 4 года назад

Very well explained sir. Just to add the entire code at 11:15 dataframe = pd.DataFrame(data= new_coordinates, columns= ("1st_Principal", "2nd_Principal", "labels"))

@devenderkumar-pr6ig 2 года назад

can you please provide the code if you have?

@laposanti534 2 года назад

Straightforward explanation. Thanks for the great content!!

@blendsadikaj888 Год назад

The reason why the 2 plots (your plot and the one from scikit-learn library) are not the same is that you are not reversing the vectors array along the columns (vectors = vectors[:, ::-1]), since eigh() is returning the vectors on ascending order and you need them on descending order.

@aliceiceberg581 4 года назад

Extremely useful. Thank you!

@devenderkumar-pr6ig 2 года назад

if you have the code please can you share it?

@nidhi_singh9494 2 года назад

Should we have to add labels according to row manually?

@nehamanpreet1044 4 года назад

Why you did the transpose you could have performed matmul(sample_data,vectors) ?

@prashantsingh1096 6 лет назад

why1/n is not multiplied with covar_matrix? Is it done in fit_transform(data) function?

@PrasannaChowdharyborn9th 3 года назад

As said in the appliedAI website under this video description: correction: The formula for covariance matrix is 1/n * (X^T*X) (we missed out in 1/n in the video) there is a typo in the ipython notebook, as eigenvalues generated are in ascending order, when we multiply vector*sample_data^T vector[0]*X[i] will be second principle component vector[1]*x[i] will be first principle component

@abhinavsinghtawar9157 3 года назад

where we are setting the number of principal components in the first expaination ?

@Will-bb4zy 3 года назад

in the data frame , the column part should include label as well. you did not show

@harshavardhanambati5963 4 года назад

can you please upload the code by any link

@sachindubey4315 4 года назад

sir from where i get the dataset used in this video please provide link of the dataset

@AppliedAICourse 4 года назад

That's just a Google search away. Just search for " MNIST dataset download" and you will get multiple sources for this data. This dataset is also inbuilt in most libraries like Scikit Learn.