Principal Component Analysis Course
This course teaches Principal Component Analysis for real-world data analysis. Participants learn to clean and scale high-dimensional data, select key components using scree plots and variance analysis, interpret loadings for business insights, implement PCA efficiently in Python with scikit-learn, and compare it to t-SNE and UMAP. Transform complex customer datasets into clear segments, visualizations, and robust predictive models for production use.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in Principal Component Analysis for effective dimensionality reduction. Master data cleaning, preprocessing, feature selection, and efficient PCA application on large datasets. Learn to interpret loadings, select components confidently, visualize results for segmentation and modeling, and compare PCA with t-SNE and UMAP to build reliable pipelines.
Elevify advantages
Develop skills
- Clean and encode data for PCA using robust scaling, imputation, and category handling.
- Select optimal principal components with scree plots, variance, and parallel analysis.
- Interpret PCA loadings and rotations to reveal business-ready factors.
- Apply PCA in Python with scikit-learn for scalable dimensionality reduction.
- Compare PCA with t-SNE and UMAP to select the best method.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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