Principal Component Analysis Course
Dive into Principal Component Analysis to handle high-dimensional data effectively. Learn to clean and scale datasets, select optimal components, interpret results for insights, and compare with advanced techniques like t-SNE and UMAP. Build practical pipelines to segment customers, visualise patterns, and enhance machine learning models with reduced dimensions.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Gain expertise in Principal Component Analysis for dimensionality reduction. Master data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Learn to interpret loadings, select components confidently, visualise results for segmentation and modelling, and compare PCA with t-SNE and UMAP while developing production-ready pipelines.
Elevify advantages
Develop skills
- Clean and preprocess data for PCA using scaling, imputation, and encoding.
- Select key principal components via scree plots, variance explained, and parallel analysis.
- Interpret PCA loadings and rotations to uncover meaningful business factors.
- Implement PCA in Python with scikit-learn for efficient dimensionality reduction.
- Compare PCA against t-SNE and UMAP to pick the best method.
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