Theory of Gaussian Process Regression for Machine Learning
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
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 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
Instructor
![Foster Lubbe](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 146 Reviews
- 10,816 Students
- 3 Courses
Foster Lubbe holds undergraduate degrees in Physics and Engineering, as well as a Master's degree in Mechanical Engineering. In his thesis he focused on the application of Gaussian process regression to renewable energy data modelling. He is currently a PhD student in the field of renewable energy. Before commencing with full-time PhD studies, he was a part-time lecturer teaching undergraduate engineering courses.