Principal Component Analysis Courses and Certifications

Harvard CMSA

Generalized Frechet Means with Random Minimizing Domains and Its Strong Consistency

Explore generalized Frechet means framework with random minimizing domains, covering strong consistency theorems and applications to non-Euclidean data analysis methods.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

Unsupervised Learning in R

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Dimensionality Reduction in Python

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Linear Algebra for Data Science in R

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Machine Learning for Marketing Analytics in R

In this course you'll learn how to use data science for several common marketing tasks.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Multivariate Probability Distributions in R

Learn to analyze, plot, and model multivariate data.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Dimensionality Reduction in R

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Dimensionality Reduction in R

1 rating

Develop your intuition for when to reduce dimensionality in your data, and master the fundamentals of how to do so in R.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Career Certificate

Implementing AI Algorithms from Scratch

315 ratings at CodeSignal

Master the fundamentals of AI by implementing machine learning algorithms from scratch, covering regression, classification, optimization, ensemble methods, clustering, and neural networks with hands-on coding.

• CodeSignal
• 21 hours
• Self-Paced
• Free Certificate
• 6 courses

Mathematics for Machine Learning: PCA

3186 ratings at Coursera

Explore mathematical foundations of Principal Component Analysis (PCA) for dimensionality reduction. Learn statistics, inner products, orthogonal projections, and derive PCA from a geometric perspective.

• Coursera
• 20 hours 39 minutes
• Self-Paced
• Paid Course

NPTEL-NOC IITM

Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM) Tutorial

Explore practical applications of PCA for dimensional reduction and GMM clustering on public datasets, including Silhouette Score calculation techniques.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

AnalystPrep

Regression Hedging and Principal Component Analysis - FRM Part 2 2025 - Book 1 - Chapter 11

Master regression hedging techniques, DV01-neutral hedge drawbacks, and principal component analysis for constructing effective hedging portfolios in this FRM Part 2 preparation.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Chemometrics & Machine Learning in Copenhagen

All Sparse Models Are Wrong, but Some Are Useful - Sparse Principal Component Analysis

Discover theoretical properties of Sparse PCA that affect variable selection reliability and learn to avoid common misinterpretations in multivariate data analysis.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Executive Briefing - Data Science and Machine Learning

11628 ratings at Udemy

Get a non-technical overview of Data Science and Machine Learning for Executives!

• Udemy
• 1 hour 58 minutes
• Self-Paced
• Paid Course

Intro to Unsupervised Machine Learning

Master unsupervised machine learning through hands-on practice with clustering, dimensionality reduction, and advanced algorithms using the Iris dataset for practical data analysis applications.

• CodeSignal
• 5 hours
• Self-Paced
• Free Certificate