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

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, eeering 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, eeers and financial analysts looking to up their data analysis game

Anybody interested in probabilistic modelling and Bayesian statistics



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