Data Science Series:
Principal Component Analysis (PCA) In-depth

## Zacharias Voulgaris PhD
## Estimated time to complete: 23m
## Topics Data Science
## Published byTechnics Publications 2019

>> 2018x12
>> 01x50

// https://technicspub.com/
\\ https://www.safaribooksonline.com/
// https://www.oreilly.com/

Explore PCA with  us in  detail  so you  can confidently
apply this  unsupervised learning  method  in your  data
science projects.  We  cover  variance as  a  proxy  for
information in  a  dataset  and  how  to  transform  the
dataset with matrix  rotation. How  does PCA  compare to
Singular Value  Decomposition (SVD)?  Find  out in  this
video, along with exploring the many PCA use cases.

WEE ASUMME NO LIIABILLITY FUR A WROONG SPELING
COZ WEE ARRE A NON (iL)LiTERATE GRUP!!

TANNKS TU THE LITERATE ASKII FRIIEND!!