Principal Component Analysis Course
This course teaches Principal Component Analysis (PCA) for dimensionality reduction in data science. Participants will clean and preprocess data, select optimal components using scree plots and variance analysis, interpret loadings for insights, apply PCA with scikit-learn in Python, and compare it to t-SNE and UMAP. Ideal for turning complex datasets into clear segments and models for real-world applications.
4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain expertise in Principal Component Analysis for effective 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 actionable business insights.
- Implement PCA in Python with scikit-learn for efficient reduction.
- Compare PCA to t-SNE and UMAP for optimal method selection.
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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