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Theory of Gaussian Process Regression for Machine Learning

Introduction to a probabilistic modelling tool for Bayesian machine learning, with application in Python
Course from Udemy
 2826 students enrolled
 en
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

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.

Theory of Gaussian Process Regression for Machine Learning
$ 19.99
per course
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