Optimize Model Training with Hyperparameter Tuning
Machine learning models are of critical business importance, and hyperparameters allow us to converge on more accurate models faster. This course will teach you how to understand and optimize hyperparameters by tuning them.
What you'll learn
Machine learning (ML) models are extremely powerful, and many enterprise companies use them extensively. But how can you get the most performance out of your models without investing excessive time or resources in the training process?
In this course, Optimize Model Training with Hyperparameter Tuning, you'll learn to optimize your model performance by tuning hyperparameters.
First, you’ll explore the basics of hyperparameters - what they are, how they are used, the different categories of hyperparameter, and to which model types they apply.
Next, you’ll discover how to tune hyperparameters - including different techniques, softwares, automated and manual tuning, and the outcomes of tuning hyperparameters on ML business goals.
Finally, you'll learn the difference between manual and automated tuning.
When you’re finished with this course, you’ll have the skills and knowledge of tuning hyperparameters needed to effectively optimize ML models.
Table of contents
About the author
Daniel Stern is a coder, web developer and programming enthusiast from Toronto, Ontario, where he works as a freelance developer and designer. Daniel has been working with web technologies since the days of the dial-up, and is especially keen on JavaScript, CSS, Angular, React and TypeScript. Over the course of his work as an open-source developer, he's created many community-standards web tools including Angular Audio and Range.CSS. His tool, Range.CSS, was featured in a guest article on CSS-T... more