# Gaussian Process Regression for Bayesian Machine Learning

Udemy Course Gaussian Process Regression for Bayesian Machine Learning | NED

New

by Foster Lubbe

**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**.New

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

- 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.

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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