Dimensionality Reduction Courses and Certifications

Dimensionality Reduction in Python

919 ratings at DataCamp

High-dimensional datasets can be overwhelming and leave you not knowing where to start.

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

Dimensionality Reduction in R

97 ratings at DataCamp

Do you ever work with datasets with an overwhelming number of features?

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

Advanced Dimensionality Reduction in R

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

• 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

Career Certificate

Mastering Dimensionality Reduction with Python

Learn to simplify high-dimensional data in Python using linear, nonlinear, autoencoder, and feature-selection techniques to improve model efficiency.

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

Non-linear Dimensionality Reduction Techniques

Explore non-linear dimensionality reduction in R by visualizing datasets and tuning t-SNE, Locally Linear Embedding, and Kernel PCA.

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

Career Certificate

Mastering Dimensionality Reduction with R

Learn to use PCA, nonlinear methods, autoencoders, and feature selection in R to simplify complex data for efficient, interpretable machine learning.

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

Non-linear Dimensionality Reduction Techniques

1 rating

Learn to use t-SNE, LLE, and kernel PCA to visualize hidden patterns, tune parameters, and reduce complex datasets to lower dimensions.

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

Machine Learning - Dimensionality Reduction

Learn the theory behind dimension reduction and apply Principal Component Analysis and Exploratory Factor Analysis to survey data using prepared R code.

• Cognitive Class
• 6 hours
• Self-Paced
• Free Online Course

DigitalSreeni

Advanced Dimensionality Reduction - t-SNE vs UMAP vs PCA Deep Dive

Master non-linear dimensionality reduction with t-SNE and UMAP for complex data visualization, including parameter tuning and Python implementation using breast cancer classification.

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

UofU Data Science

Dimensionality Reduction: Rank-k Approximation and Eigen-decomposition - Lecture 20

Dive into advanced dimensionality reduction techniques, exploring rank-k approximation, eigen-decomposition, and power method applications in data analysis.

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

UofU Data Science

Dimensionality Reduction: Data Matrices and Projection Methods - Lecture 18

Explore dimensionality reduction techniques through matrix operations, focusing on dot product projections and orthogonal basis sets for effective data transformation.

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

Mining Massive Datasets

24 ratings

The course is based on the text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course.

• edX
• 7 weeks, 5-10 hours a week
• Self-Paced
• Free Online Course (Audit)

• Best of All Time

Linear Landscapes of Dimensionality Reduction

Learn Linear Discriminant Analysis in Python by projecting the Iris dataset, comparing LDA with PCA, and evaluating classification accuracy.

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

Enigmatic Autoencoders for Dimensionality Reduction

Build and tune Keras autoencoders to compress and reconstruct data for unsupervised dimensionality reduction.

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