Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Theory of Gaussian Process Regression for Machine Learning
Rating: 4.2 out of 5(133 ratings)
3,152 students

Theory of Gaussian Process Regression for Machine Learning

Introduction to a probabilistic modelling tool for Bayesian machine learning, with application in Python
Created byFoster Lubbe
Last updated 8/2021
English

What you'll learn

  • The mathematics behind an algorithm such as the scikit-learn GaussianProcessRegressor algorithm
  • The benefits of Gaussian process regression
  • Examples of Gaussian process regression in action
  • The most important kernels needed for Gaussian process regression
  • How to apply Gaussian process regression in Python using scikit-learn

Course content

3 sections • 14 lectures • 1h 8m total length

  • Introduction3:11
  • Reading 1
  • The conditional of a Gaussian8:25
  • Reading 2
  • An example of finding the conditional3:21
  • Reading 3
  • Supervised learning with the Gaussian process9:11
  • Reading 4
  • An example of supervised learning3:55
  • Reading 5

Requirements

  • A basic understanding of linear algebra
  • Basic experience with coding

Description

Probabilistic modelling, which falls under the Bayesian paradigm, is gaining popularity world-wide. Its powerful capabilities, such as giving a reliable estimation of its own uncertainty, makes Gaussian process regression a must-have skill for any data scientist. Gaussian process regression is especially powerful when applied in the fields of data science, financial analysis, engineering and geostatistics.

This course covers the fundamental mathematical concepts needed by the modern data scientist to confidently apply Gaussian process regression. The course also covers the implementation of Gaussian process regression in Python.

Who this course is for:

  • Data scientists, engineers and financial analysts looking to up their data analysis game
  • Anybody interested in probabilistic modelling and Bayesian statistics

Report abuse