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Principal Component Analysis Course

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

What will I learn?

Gain expertise in Principal Component Analysis for dimensionality reduction. Master data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Learn to interpret loadings, select components confidently, visualise results for segmentation and modelling, and compare PCA with t-SNE and UMAP while developing production-ready pipelines.

Elevify advantages

Develop skills

  • Clean and preprocess data for PCA using scaling, imputation, and encoding.
  • Select key principal components via scree plots, variance explained, and parallel analysis.
  • Interpret PCA loadings and rotations to uncover meaningful business factors.
  • Implement PCA in Python with scikit-learn for efficient dimensionality reduction.
  • Compare PCA against t-SNE and UMAP to pick the best 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 I work for.
SilviaNurse
Really great course. Lots of valuable information.
WiltonCivil Firefighter

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