Jukka Aakula’s Reviews > Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning > Status Update

Jukka Aakula
Jukka Aakula is on page 17 of 265
Nov 25, 2025 10:14PM
Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning

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Jukka Aakula
Jukka Aakula is on page 60 of 265
Data normalization perspective to PCA.
Dec 19, 2025 10:20PM
Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning


Jukka Aakula
Jukka Aakula is on page 53 of 265
Nov 29, 2025 10:17PM
Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning


Jukka Aakula
Jukka Aakula is on page 47 of 265
The writer has held lectures on this subject to undergraduates, every time improving his material.

And it has become a beautiful book. Different from a typical theoretical math book, where you first get the theory, and then you can work on the applications. On page ~40, you already know the idea quite well and what to do with the PCA.

But not an easy book.
Nov 29, 2025 09:35PM
Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning


Jukka Aakula
Jukka Aakula is on page 45 of 265
From intuition to a more formal part.
Nov 28, 2025 11:01PM
Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning


Jukka Aakula
Jukka Aakula is on page 30 of 265
The first chapter shows the idea in a way that is not just an intuitive introduction, but also shows a crude solution to the problem at hand.

But it is not an easy introduction. Also, you should have some understanding of matrix calculus.
Nov 27, 2025 10:20PM
Unraveling principal component analysis: A long-form guide through the mathematical fundamentals of data science and machine learning


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