Principal Component Analysis Course
Dive deep into Principal Component Analysis for practical stats in Nigeria's data scene. Cleanse and scale datasets, pick key components, decode loadings, and stack up against t-SNE and UMAP. Transform complex customer data into neat segments, crisp visuals, and powerful predictive models ready 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 wisely, visualise for segmentation and modelling, and compare with t-SNE and UMAP for solid pipelines.
Elevify advantages
Develop skills
- Clean and encode data robustly for PCA with scaling, imputation, and category management.
- Pick best principal components via scree plots, variance checks, and parallel analysis.
- Decode PCA loadings and rotations for straightforward business insights.
- Implement PCA in Python using scikit-learn for quick, scalable reduction.
- Evaluate PCA against t-SNE and UMAP to select ideal dimensionality technique.
Suggested summary
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