Udemy MOOC / Non-credit 0

Theory of Gaussian Process Regression for Machine Learning

About this course

The course walks through the core mathematics behind Gaussian process regression, covering kernels, covariance functions, and Bayesian inference. It then shows how to translate those concepts into working Python code using common libraries. By the end, you’ll be able to build GP models, tune their hyper‑parameters, and interpret the uncertainty estimates they provide for real‑world data.

55/100

CourseAsk score

What the provider tells you: 31/45
Who stands behind it: 8/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Explain the theoretical foundations of Gaussian process regression
Identify and construct appropriate kernel and covariance functions
Implement Gaussian process regression models in Python
Tune hyper‑parameters and assess model uncertainty

Course objectives

Derive GP regression equations from Bayesian principles
Translate mathematical concepts into Python implementations
Apply GP models to datasets from finance, engineering, or geostatistics

Machine Learning Statistics & Probability #python #regression #data science #financial analysis #engineering #bayesian methods #probabilistic modeling #geostatistics #uncertainty estimation #Gaussian processes #gaussian process regression #bayesian inference #probabilistic modelling #uncertainty quantification #kernel functions #covariance functions #hyperparameter tuning #scikit-learn #numpy #engineering applications

Discounted on Udemy

List price $24.99. Udemy runs frequent sales, so the checkout price is usually far lower. We do not receive the live sale price, so we do not guess it — open the course to see today's.

See today's price

You'll be redirected to Udemy to complete enrollment.