Theory of Gaussian Process Regression for Machine Learning
Introduction to a probabilistic modelling tool for Bayesian machine learning, with application in Python
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What You Will 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
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 Should Attend!
- Data scientists, engineers and financial analysts looking to up their data analysis game
- Anybody interested in probabilistic modelling and Bayesian statistics
