Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of large datasets while preserving the maximum amount of information. It involves transforming the data into a new coordinate system where the variation in the data can be described with fewer dimensions. PCA is commonly used for exploratory data analysis, making predictive models, and dimensionality reduction.

Stanford University

Winter 2023

This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.

No concepts data

+ 32 more conceptsStanford University

Spring 2022

CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.

No concepts data

+ 57 more conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

No concepts data

+ 55 more concepts