Principal Component Analysis (PCA) Explained: Simplify Complex Data for Machine Learning
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- Опубликовано: 22 авг 2024
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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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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!!!
Great Video! I am a ds in a financial institution.
you saved me in my academy work, thanks! (i'm using it with clustering)
thank you, great topic with easy understanding explanation
thanks for this, awaiting for many more similar to come!
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.
This guy also happens to make superb craft beer!
INTERESTING.😀👍🏾
can you use pca for likert scale data
This sounds a lot like Dimentionality reduction in Unsupervised learning. Newbie here, Is that right?
👍 from India
Imagine not understanding neural nets and using Ai or math other people came up with to remove factors that are calculable from other factors 😂
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