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Principal Component Analysis Course

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

What Will I Learn?

Gain expertise in dimensionality reduction via Principal Component Analysis. Master data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Interpret loadings confidently, select optimal components, and produce insightful visualisations for segmentation and modelling. Compare PCA against t-SNE and UMAP while building reliable production pipelines.

Elevify Advantages

Develop Skills

  • Clean and encode data robustly for PCA, including scaling, imputation, and categorical handling.
  • Select optimal principal components using scree plots, variance explained, and parallel analysis.
  • Interpret PCA loadings and rotations to uncover business-relevant factors clearly.
  • Implement PCA in Python using scikit-learn for efficient, scalable dimensionality reduction.
  • Compare PCA with t-SNE and UMAP to select the most suitable method for your needs.

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
Great course. Lots of valuable information.
WiltonCivil Firefighter

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