CTECH466 - Improving Performance With Ensemble Methods

Course Description

Ensemble modeling is a helpful and important technique used in machine learning. It's a powerful approach to train multiple models and quantify them into a single prediction. There are three commonly used ensemble techniques: stacking, bagging, and boosting. So how do you know which ensemble method to use and when to use it?

In this course, you will explore stacking, bagging, and boosting techniques, including the motivation behind using each and understanding their optimal scenarios as well as their tradeoffs. By the end of this course, you will have observed a number of robust algorithm case studies, such as random forests and gradient boosted decision trees, that employ these methods. You will also have the opportunity to put this new knowledge into action by practicing building and optimizing various ensemble models.

You are required to have completed the following courses or have equivalent experience before taking this course:

Machine Learning Foundations
Managing Data in Machine Learning
Training Common Machine Learning Models
Training Linear Models
Evaluating and Improving Your Model

Faculty Author

Brian D'Alessandro

Benefits to the Learner

Explore the bias-variance tradeoff
Improve model performance with ensemble methods
Understand the mechanics of random forests and gradient boosted decision trees
Identify differences among ensemble methods and when to use them
Navigate design decisions and constraints to perform agile model development
Build and tune different models to see how various methods can improve models

Target Audience

Data scientists and data analysts
Programmers, developers, and software engineers
Statisticians
Product managers
Entrepreneurs
Working professionals seeking to upskill or career change

Applies Towards the Following Certificates

Applied Machine Learning and AI : Core Courses

$999.00