Principal Component Analysis Course
This course teaches Principal Component Analysis for effective dimensionality reduction in data science. Participants will clean and preprocess data, select optimal components using scree plots and variance metrics, interpret loadings for business insights, apply PCA with scikit-learn in Python, and compare it to t-SNE and UMAP. Transform complex datasets into actionable segments, visuals, and models for real-world applications.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in Principal Component Analysis for dimensionality reduction. Cover data cleaning, preprocessing, feature selection, efficient PCA on large datasets, interpreting loadings, component selection, visualizations, and comparisons with t-SNE and UMAP to build production 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 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.
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