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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 dimensionality reduction via Principal Component Analysis. Master data cleaning, preprocessing, feature selection, and efficient PCA on big datasets. Interpret loadings confidently, select components, visualise for segmentation and modelling, compare with t-SNE and UMAP, and develop production pipelines.

Elevify advantages

Develop skills

  • Clean and prepare data for PCA using scaling, imputation, and encoding techniques.
  • Select best principal components via scree plots, variance explained, and analysis methods.
  • Interpret PCA loadings and rotations to uncover meaningful business insights.
  • Implement PCA in Python with scikit-learn for efficient large-scale reduction.
  • Evaluate PCA against t-SNE and UMAP to select ideal dimensionality tools.

Suggested summary

Before starting, you can change the chapters and 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. A lot of valuable information.
WiltonCivil Firefighter

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