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Principal Component Analysis Course

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

What will I learn?

Master dimensionality reduction with this focused Principal Component Analysis Course. Learn practical data cleaning and preprocessing, smart feature selection, and how to run PCA efficiently on large datasets. Interpret loadings, choose components with confidence, and create clear visualizations for segmentation and modeling. Compare PCA with t-SNE and UMAP, and build robust, production-ready pipelines.

Elevify advantages

Develop skills

  • Clean and encode data for PCA: robust scaling, imputation, and category handling.
  • Select optimal principal components using scree plots, variance, and parallel analysis.
  • Interpret PCA loadings and rotations to reveal clear, business-ready factors.
  • Apply PCA in Python with scikit-learn for fast, scalable dimensionality reduction.
  • Compare PCA with t-SNE and UMAP to choose the right dimensionality 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
Very great course. Lots of rich information.
WiltonCivil Firefighter

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