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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 mastery in Principal Component Analysis to reduce data dimensions effectively. Cover data cleaning, preprocessing, feature selection, efficient PCA on big datasets, interpreting loadings, confident component selection, visualisation for modelling, and comparisons with t-SNE and UMAP for strong pipelines.

Elevify advantages

Develop skills

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

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 where I work.
SilviaNurse
Very great course. Lots of rich information.
WiltonCivil Firefighter

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