Principal Component Analysis | Learn the Basics of Data Analysis and Machine Learning
Principal Component Analysis | Learn the Basics of Data Analysis and Machine Learning
A linear dimensionality-reduction technique:
Transforming variables (or features) of a large dataset (i.e., multivariate data) into a smaller one that still contains most of the information in the large dataset
By using PCA, we reduce the number of features of a dataset, while preserving as much information as possible.
Reducing data by projecting (geometrically) into a lower dimensions which called principal components (PCs)
Principal components are the directions where there is the most variance, or the directions where the data is most spread out.
PCA is used to extract the important information from a multivariate data table and to express this information as a set of few new variables called PCs (principal components). These new variables correspond to a linear combination of the originals. The number of principal components is less than or equal to the number of original variables.
#PCA
#machinelearningengineer
#dataanalysis
#eigenvector
#eigenvalue
#R_Programming
PCA analysis using R packages
Created by Dr Reza Rafiee
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Music Track: Alan Walker - Fade [NCS Release]
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Music Track: Alan Walker - Spectre [NCS Release]
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Видео Principal Component Analysis | Learn the Basics of Data Analysis and Machine Learning канала 5-Minute Analytics
A linear dimensionality-reduction technique:
Transforming variables (or features) of a large dataset (i.e., multivariate data) into a smaller one that still contains most of the information in the large dataset
By using PCA, we reduce the number of features of a dataset, while preserving as much information as possible.
Reducing data by projecting (geometrically) into a lower dimensions which called principal components (PCs)
Principal components are the directions where there is the most variance, or the directions where the data is most spread out.
PCA is used to extract the important information from a multivariate data table and to express this information as a set of few new variables called PCs (principal components). These new variables correspond to a linear combination of the originals. The number of principal components is less than or equal to the number of original variables.
#PCA
#machinelearningengineer
#dataanalysis
#eigenvector
#eigenvalue
#R_Programming
PCA analysis using R packages
Created by Dr Reza Rafiee
Please share this video and subscribe the channel for more videos.
✓ SUBSCRIBE FOR MORE VIDEOS ✓
#################################
All rights reserved.
Music Track: Elektronomia - Limitless [NCS Release]
Watch: https://www.youtube.com/watch?v=cNcy3...
Free Download / Stream: http://ncs.io/Elimitless
Music Track: Alan Walker - Fade [NCS Release]
Watch: https://www.youtube.com/watch?v=bM7SZ5SBzyY
NCS Spotify: http://spoti.fi/NCS
Music Track: Alan Walker - Spectre [NCS Release]
Watch: https://www.youtube.com/watch?v=AOeY-nDp7hI
Music Track: Different Heaven & EH!DE - My Heart [NCS Release]
Watch: https://www.youtube.com/watch?v=jK2aIUmmdP4
Music Track: Disfigure - Blank [NCS Release]
Watch: https://watch?v=p7ZsBPK656s
Видео Principal Component Analysis | Learn the Basics of Data Analysis and Machine Learning канала 5-Minute Analytics
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