Introduction to Hyperparameter Optimization

Summary

Learn how to optimize model hyperparameters with Databricks.

Description

In this course, you’ll learn how to apply hyperparameter tuning strategies to optimize machine learning models for unseen data. First, you’ll work within a balanced binary classification problem setting where you’ll use random forest to predict the correct class. You’ll learn to tune the hyperparameters of a random forest to improve a model. Then, you’ll again work with a binary classification problem using random forest and a technique known as cross-validation to generalize a model.

Learning objectives

  • Explain common machine learning techniques that are used to optimize machine learning models for unseen data.

  • Apply machine learning techniques to improve the fit of machine learning models.

  • Apply machine learning techniques to improve the generalization of machine learning models.

Prerequisites

  • Intermediate level experience with machine learning (ex. feature engineering, feature selection, applying-tree-based models)

  • We recommend taking the following courses prior to taking this course: Fundamentals of Machine Learning, Introduction to Feature Engineering and Selection with Databricks, Introduction to Applied Tree-based Models with Databricks.

Learning path

  • This course is part of the data scientist learning path.

Proof of completion

  • Upon 80% completion of this course, you will receive a proof of completion.