Principal Component Analysis Course
This course teaches Principal Component Analysis (PCA) for effective dimensionality reduction in real-world data analysis. Participants will clean and preprocess data, select key components using scree plots and variance analysis, interpret loadings for business insights, implement PCA using Python's scikit-learn, and compare it with t-SNE and UMAP. Transform high-dimensional datasets into clear segments, visualizations, and robust models for segmentation and prediction.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in Principal Component Analysis for dimensionality reduction. Master data cleaning, preprocessing, feature selection, efficient PCA on large datasets, interpreting loadings, confident component selection, visualizations for modeling, and comparisons with t-SNE and UMAP to build production-ready 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
FAQs
Who is Elevify? How does it work?
Do the courses have certificates?
Are the courses free?
What is the course workload?
What are the courses like?
How do the courses work?
What is the duration of the courses?
What is the cost or price of the courses?
What is an EAD or online course and how does it work?
PDF Course