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Principal Component Analysis Course

Principal Component Analysis Course
from 4 to 360h flexible workload
valid certificate in your country

What will I learn?

Gain expertise in Principal Component Analysis to reduce data dimensions effectively. Cover data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Master interpreting loadings, selecting components confidently, visualising results for segmentation and modelling, and comparing with t-SNE and UMAP for robust pipelines.

Elevify advantages

Develop skills

  • Clean and prepare data for PCA using scaling, imputation, and encoding techniques.
  • Select the best principal components with scree plots, variance checks, and parallel analysis.
  • Interpret PCA loadings and rotations to uncover meaningful business factors.
  • Implement PCA in Python using scikit-learn for efficient data reduction.
  • Compare PCA against t-SNE and UMAP to pick the ideal method.

Suggested summary

Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.
Workload: between 4 and 360 hours

What our students say

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EmersonPolice Investigator
The course was essential to meet the expectations of my boss and the company where I work.
SilviaNurse
Very great course. Lots of rich information.
WiltonCivil Firefighter

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