Principal Component Analysis: Dimensionality Reduction

Related lectures (32)

Page 3 of 4

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Explores PCA for predicting medals distribution and compressing face images.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.