Principal Component Analysis Course
Dive into Principal Component Analysis to simplify complex datasets. Learn data cleaning, component selection, loading interpretation, and comparisons with t-SNE and UMAP. Transform high-dimensional data into insightful segments, visuals and robust models for real-world applications.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Gain expertise in dimensionality reduction through Principal Component Analysis. Master data cleaning, feature selection, efficient PCA on big data, interpreting loadings, visualising results, and comparing with t-SNE and UMAP for 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 and parallel analysis.
- Interpret PCA loadings and rotations for actionable business insights.
- Implement PCA in Python with scikit-learn for scalable analysis.
- Compare PCA against t-SNE and UMAP to pick 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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