Principal Component Analysis (PCA) Explained: Simplify Complex Data for Machine Learning

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Discover how Principal Component Analysis (PCA) can simplify complex data sets and improve your machine learning models. In this video, we break down PCA, a powerful technique for reducing data dimensions while retaining crucial information. Learn how PCA helps in risk management, data visualization, and noise filtering, and see real-world examples of its applications in finance and healthcare. Whether you're a data scientist or a machine learning enthusiast, this guide will help you understand and apply PCA effectively.
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[@vitorribeirosa]

Thank you for introducing this topic. I appreciate how your videos provide an overview of various ML methods.
As a suggestion for future videos, I would like to recommend one covering the principles of Independent Component Analysis (ICA).
This method has recently been in high demand in my projects.
Cheers!!!

[@juanhe3430]

Great Video! I am a ds in a financial institution.

[@natan.mendes]

you saved me in my academy work, thanks! (i'm using it with clustering)

[@mjacfardk]

thank you, great topic with easy understanding explanation

[@ashraf_isb]

thanks for this, awaiting for many more similar to come!

[@ZirghamIlyas]

Explanation is fine but if you could come up with a video relying more on visualization of PC1 and PC2 then it would be great. Thanks.

[@paolobagares2522]

This guy also happens to make superb craft beer!

[@tyrojames9937]

INTERESTING.😀👍🏾

[@josephtaanyan6162]

can you use pca for likert scale data

[@Codetutor-DemystifyCoding]

This sounds a lot like Dimentionality reduction in Unsupervised learning. Newbie here, Is that right?

[@mdrehan4all]

👍 from India

[@ValidatingUsername]

Imagine not understanding neural nets and using Ai or math other people came up with to remove factors that are calculable from other factors 😂

[@NancyMendozai]

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