Principal Component Analysis Course
This course equips you with Principal Component Analysis skills for practical statistics. Learn to clean and scale data, pick optimal components, interpret loadings accurately, and contrast PCA against t-SNE and UMAP. Transform complex high-dimensional data, like customer profiles, into insightful segments, compelling visuals, and enhanced predictive models for real business use.

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
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