Dimensionality Reduction Courses and Certifications

Advanced Dimensionality Reduction in R

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

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Dimensionality Reduction in R

1 review

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
• On-Demand
• Free Trial Available

Linear Landscapes of Dimensionality Reduction

Master dimensionality reduction techniques through Linear Discriminant Analysis (LDA), comparing it with PCA while implementing practical Python solutions for improved feature selection and model performance.

• CodeSignal
• 2 hours
• On-Demand
• Free Certificate

Enigmatic Autoencoders for Dimensionality Reduction

Dive into the world of autoencoders, mastering neural network architecture, forward propagation, and hyperparameter optimization for effective data compression and reconstruction techniques.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Dimensionality Reduction with Feature Selection

Master advanced feature selection techniques to optimize machine learning models through variance analysis, univariate selection, mutual information, and recursive elimination methods.

• CodeSignal
• 4 hours
• On-Demand
• Free Certificate

Linear Landscapes of Dimensionality Reduction

Discover Linear Discriminant Analysis techniques for dimensionality reduction and feature selection through hands-on R programming exercises with real datasets.

• CodeSignal
• 2 hours
• On-Demand
• Free Certificate

Enigmatic Autoencoders for Dimensionality Reduction

Discover how autoencoders compress and reconstruct data through neural networks in R, mastering unsupervised learning techniques for effective dimensionality reduction.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Dimensionality Reduction with Feature Selection

Master specialized feature selection techniques to optimize machine learning models by removing low-variance features, applying correlation analysis, and using advanced methods like RFE.

• CodeSignal
• 4 hours
• On-Demand
• Free Certificate

Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.

• LinkedIn Learning
• 3 hours 33 minutes
• On-Demand
• Free Trial Available

Machine Learning with Data Reduction in Excel, R, and Power BI

19 ratings at LinkedIn Learning

Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.

• LinkedIn Learning
• 3-4 hours
• On-Demand
• Free Trial Available

Machine Learning - Dimensionality Reduction

Learn techniques to reduce dataset features using PCA and EFA, with hands-on practice in R. Explore the theory and application of dimensionality reduction in machine learning.

• Cognitive Class
• 6 hours
• On-Demand
• Free Online Course

Alan Turing Institute

Approximate Cross Validation for Large Data and High Dimensions - Tamara Broderick, MIT

Explore efficient cross-validation techniques for large datasets using linear approximation and dimensionality reduction. Learn about error bounds, high-dimensional challenges, and practical applications in machine learning.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Microcredential

Mathematics for Machine Learning

15024 ratings at Coursera

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
• 4 weeks, 10 hours a week
• On-Demand
• Paid Course
• 3 courses

Blessings of Dimensionality for Gaussian Latent Factor Models

Explore how high-dimensional data analysis can benefit from restricted latent factor overlaps, with applications in brain fMRI and financial markets.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

The Julia Programming Language

Randomization for Solving High-Dimensional Problems - Algorithms and Software

Explore randomization techniques for high-dimensional linear systems and eigenvalue problems, featuring sketching methods, randomized orthogonalization, and Krylov subspace algorithms in Julia.

• YouTube
• 53 minutes
• On-Demand
• Free Video