Principal Component Analysis Course
Dive deep into Principal Component Analysis for practical stats in real scenarios. Cleanse and scale your data properly, pick the best components, understand loadings clearly, and stack it up against t-SNE and UMAP. Transform complex customer data into neat segments, crisp visuals, and powerful predictive models that deliver results.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in dimensionality reduction through this Principal Component Analysis course. Master data cleaning, preprocessing, feature selection, and efficient PCA application on big datasets. Interpret loadings confidently, select components wisely, visualise for segmentation and modelling, and compare PCA to t-SNE and UMAP while building solid pipelines.
Elevify advantages
Develop skills
- Clean and prepare data for PCA with solid scaling, filling missing values, and handling categories.
- Pick the best principal components using scree plots, variance explained, and parallel analysis.
- Break down PCA loadings and rotations to uncover meaningful business insights.
- Implement PCA in Python using scikit-learn for quick and scalable reductions.
- Weigh PCA against t-SNE and UMAP to select the ideal method for your needs.
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 workloadWhat our students say
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