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Pluralsight

Optimize Model Training with Hyperparameter Tuning

via Pluralsight

Overview

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.

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.

Syllabus

  • Course Overview 2mins
  • Understanding Hyperparameters 12mins
  • Tuning Hyperparameters 13mins

Taught by

Daniel Stern

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