Dimensionality Reduction Courses and Certifications

Mathematics for Machine Learning: PCA

3184 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

Shaw Talebi

Dimensionality Reduction and Segmentation with Decision Trees - Python Code

Explore advanced decision tree applications for dimensionality reduction and predictor segmentation, with Python code examples for breast cancer detection and sepsis risk analysis.

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

UofU Data Science

Dimensionality Reduction: Principal Component Analysis and Multidimensional Scaling - Lecture 21

Master dimensionality reduction techniques through Principal Component Analysis (PCA) and Multidimensional Scaling (MDS), exploring their applications in data analysis and visualization.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

DigitalSreeni

Principal Component Analysis (PCA) - Mastering Dimensionality Reduction and Visualization

Master PCA for dimensionality reduction with complete Python implementation, from mathematical foundations to 3D visualization using breast cancer classification case study.

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

UofU Data Science

Principal Component Analysis - Dimensionality Reduction and SVD - L15

Master dimensionality reduction through dot-products, orthogonal projections, SVD, and Principal Component Analysis techniques for optimal data representation.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

BIMSA

Fast Algorithms for Fusing Phylogenetic Trees into Networks via Dimensionality Reduction

Discover fast algorithms for fusing phylogenetic trees into networks using dimensionality reduction techniques in computational biology.

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

International Centre for Theoretical Sciences

Unsupervised ML - Clustering, Dimensionality Reduction, Applications by Rahul Siddharthan

Explore unsupervised machine learning techniques for clustering and dimensionality reduction, with practical applications in data analysis and pattern discovery.

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

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Learning Manifolds and Dimensionality Reduction in Deep and Geometric Learning

Explore geometric principles and manifold structures in data spaces, examining dimensionality reduction techniques for deep learning applications through mathematical and physical perspectives.

• YouTube
• 52 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

Unsupervised Learning and Clustering

Master unsupervised learning algorithms from scratch, including k-Means, PCA, and DBSCAN. Build clustering implementations, visualize results, and evaluate performance using essential metrics.

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

Navigating Data Simplification with PCA

Master dimensionality reduction techniques using Principal Component Analysis (PCA) with Python's Scikit-learn, from basic concepts to practical implementation in machine learning projects.

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

Feature Selection, Reduction and Streamlining

Master techniques to identify impactful features, reduce dimensionality, and streamline datasets for enhanced model efficiency and performance by focusing on the most relevant data attributes.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Career Certificate

Feature Engineering for Beginners

Explore foundational to advanced feature engineering techniques, selection methods, and transformations to enhance data quality and optimize model performance.

• CodeSignal
• 4 hours
• Self-Paced
• Free Certificate
• 3 courses

Unsupervised Learning and Clustering

Master unsupervised learning by building k-Means, mini-batch k-Means, PCA, and DBSCAN from scratch. Assess cluster quality using key metrics like homogeneity and completeness.

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