Principal Component Analysis Courses and Certifications

Ideas for a Complex World - Anna Seigal

Explore mathematical tools for understanding complex data, their applications in disease treatment, exam predictions, and advertising, and the limitations of these approaches in real-world scenarios.

• YouTube
• 48 minutes
• On-Demand
• Free Online Course

StatQuest with Josh Starmer

Principal Component Analysis - PCA, Step-by-Step

Step-by-step guide to Principal Component Analysis (PCA), explaining its importance in data analysis and machine learning. Covers 2D and 3D data examples, PC calculation, and interpretation of results.

• YouTube
• 22 minutes
• On-Demand
• Free Online Course

StatQuest with Josh Starmer

Principal Component Analysis - PCA Clearly Explained - 2015

Clear explanation of Principal Component Analysis, covering dimensions, variance, loading scores, and key concepts for interpreting PCA plots in RNA-seq data analysis.

• YouTube
• 20 minutes
• On-Demand
• Free Online Course

MITCBMM

Wobbling, Drooping, Bouncing - Visual Perception of Materials and Their Properties

Explore visual perception of material properties through experiments on elastic bouncing objects, examining human estimation accuracy, motion features, and internal models for predicting object behavior.

• YouTube
• 41 minutes
• On-Demand
• Free Online Course

Julia Silge

Principal Component Analysis for Hip Hop Songs With Tidymodels

Implement PCA using recipes to analyze the best hip hop songs of all time, exploring Spotify data and visualizing results with tidymodels.

• YouTube
• 1 hour
• On-Demand
• Free Online Course

Alan Turing Institute

Parsimonious Representations in Data Science - Dr. Armin Eftekhari, University of Edinburgh

Explore geometric structures in data, from linear subspace models to nonlinear approaches, for efficient data analysis and representation in the era of big data.

• YouTube
• 1 hour 53 minutes
• On-Demand
• Free Online Course

Applied Algebraic Topology Network

Katharine Turner - PCA of Persistent Homology Rank Functions with Case Studies

Explore persistent homology rank functions through PCA, featuring case studies and insights into topological data analysis techniques.

• YouTube
• 57 minutes
• On-Demand
• Free Online Course

Fields Institute

PCA for High-Dimensional Heteroscedastic Data

Explore optimal weighting for PCA in high-dimensional heteroscedastic data, improving recovery of underlying components and addressing challenges in modern applications with heterogeneous datasets.

• YouTube
• 40 minutes
• On-Demand
• Free Online Course

Data Science: Machine Learning

85 ratings at edX

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

• edX
• 8 weeks, 2-4 hours a week
• On-Demand
• Free Online Course (Audit)

Tiling the Genome - Naming the Parts of Your Genome That Make You You

Explore genomic tiling, precision medicine, and the future of DNA-based healthcare. Learn about organizing computational resources, big data platforms, and the Personal Genome Project's impact on clinical genetics.

• YouTube
• 42 minutes
• On-Demand
• Free Online Course

Statistics for Applications

1 review

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications.

• MIT OpenCourseWare
• 28 hours
• On-Demand
• Free Online Course

Mathematics for Machine Learning: PCA

3082 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
• On-Demand
• Free Online Course (Audit)

Matrix Methods

238 ratings at Coursera

Explore fundamental matrix methods essential for machine learning and data analysis, including multiplication, linear equations, orthogonality, and SVD for dimensionality reduction and PCA.

• Coursera
• 6 hours 35 minutes
• On-Demand
• Free Online Course (Audit)

Microcredential

Mathematics for Machine Learning

1 review

Gain essential mathematical foundations for machine learning, covering linear algebra, multivariate calculus, and dimensionality reduction with PCA. Build intuitive understanding and practical skills for advanced ML and data science.

• Coursera
• 17 weeks, 4 hours a week
• On-Demand
• Paid Course
• 3 courses

Clustering Analysis

Explore unsupervised learning techniques, including clustering methods and dimension reduction, to uncover patterns and insights in diverse datasets through hands-on tutorials and case studies.

• Coursera
• 1 day 13 hours 1 minute
• On-Demand
• Free Online Course (Audit)