Bayesian Optimization Tutorial with Python

Offered By
Coursera Project Network
In this Guided Tutorial, you will:

Define objective function of Bayesian optimization

Implement Bayesian Optimization

Use Bayesian Optimization and GPyOpt in your projects

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this guided tutorial you will get familiar with the basics of Bayesian optimization and Implement Bayesian optimization algorithm process and use it in a machine learning project, We will consider function optimization tasks and also Hyperparameters tuning using Bayesian optimization and GPyOpt library. Bayesian optimization is a nice topic, whether you want to do a high dimensional or a computationally expensive optimization it's efficient. By the end of this tutorial you will be able to understand and start applying Bayesian optimization in your machine learning projects.

Skills you will develop

Bayesian OptimizationPython ProgrammingMachine LearningGpyOpt

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Define Objective function - One Dimensional Case

  2. Optimize 1-D Objective function using GPyopt

  3. Define Objective function - Two Dimensional Case

  4. Optimize 2-D Objective function using GPyopt

  5. Using Bayesian Optimization in Machine Learning

How Guided Tutorials work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.