PCA: Directions of Largest Variance

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 1 of 3

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Principal Component Analysis: Applications and Limitations

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Principal Component Analysis: Olympic Medals & Image Compression

Explores PCA for predicting medals distribution and compressing face images.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

PCA: Key Concepts

Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.