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Gaussian Process Regression for Bayesian Machine Learning

 Gaussian Process Regression for Bayesian Machine Learning
Udemy Course Gaussian Process Regression for Bayesian Machine Learning | NED
Gaussian Processes has opened the possibility of flexible models which are practical to work with. In this short tutorial we present the basic idea on how Gaussian Process models can be used to formulate a Bayesian framework for regression. We will focus on understanding the stochastic process and how it is used in supervised learning.
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by Foster Lubbe

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
 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
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