Theory of Gaussian Process Regression for Machine Learning

Theory of Gaussian Process Regression for Machine Learning

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.

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

Rating: 4.15

Level: All Levels

Duration: 1 hour

Instructor: Foster Lubbe