📊

PCA

Unsupervised

🗺️

t-SNE & UMAP

Unsupervised

Which should you use?

PCA and t-SNE & UMAP are both unsupervised learning algorithms. PCA is simpler to understand and implement, while t-SNE & UMAP offers more sophisticated capabilities. Choose based on your data characteristics and interpretability requirements.

Key Differences

Factor

PCA

t-SNE & UMAP

Complexity

Intermediate

Advanced

Common Use Case

Data visualization

High-dimensional visualization

Prerequisites

Few

Use PCA when:

Your use case involves: Data visualization

Interpretability is important

You have moderate ML experience

Learn PCA

Use t-SNE & UMAP when:

Your use case involves: High-dimensional visualization

You need advanced modeling power

You have substantial ML experience

Learn t-SNE & UMAP

Real-World Applications

PCA

Data visualization

Feature extraction

Noise reduction

t-SNE & UMAP

High-dimensional visualization

Cluster exploration

Single-cell analysis

Ready to learn both algorithms?

Interactive lessons with visualizations and hands-on practice

Explore ML Algorithms

📊

PCA

Unsupervised

🗺️

t-SNE & UMAP

Unsupervised

Which should you use?

Key Differences

Factor

PCA

t-SNE & UMAP

Complexity

Intermediate

Advanced

Common Use Case

Data visualization

High-dimensional visualization

Prerequisites

Few

Use PCA when:

Your use case involves: Data visualization

Interpretability is important

You have moderate ML experience

Learn PCA

Use t-SNE & UMAP when:

Your use case involves: High-dimensional visualization

You need advanced modeling power

You have substantial ML experience

Learn t-SNE & UMAP

Real-World Applications

PCA

Data visualization

Feature extraction

Noise reduction

t-SNE & UMAP

High-dimensional visualization

Cluster exploration

Single-cell analysis

Ready to learn both algorithms?

Interactive lessons with visualizations and hands-on practice

Explore ML Algorithms