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Course Content

Principal Component Analysis

What’s after?What’s after?

We used PCA and received modified data, with a smaller dimension. What will be our next step?

As mentioned earlier, the data obtained is used in machine learning models, i.e. PCA basically acts only as a data processing step.

Once you have your data processed by PCA, you can use it in any machine learning model. It can be a model that solves the problem of classification, regression, clustering, etc. As an example, the use of PCA when working with images, because datasets are often large and with a lack of capacity, a dataset with more than 500,000 images can already become difficult to process. A few examples of how PCA can be used on its own:

  • Visualization of multidimensional data
  • Information compression

And examples of when PCA is used as a data process:

  • Data dimension reduction
  • Noise reduction in data

In the following chapters, you will explore in detail some of the most common uses for PCA.


Choose the types of data that PCA can work with effectively:

Select a few correct answers

Section 5.

Chapter 2