Applied Machine Learning: Ensemble Learning

This is the repository for the LinkedIn Learning course Applied Machine Learning: Ensemble Learning. The full course is available from LinkedIn Learning.

Course Description

Do you want to grow your skills as a machine learning practitioner, but don’t know where to begin? You don’t need any formal training in data science to start working toward your goal. In this course, instructor Matt Harrison guides you through the key concepts of ensemble learning. Explore different ensemble methods like bagging, boosting, and stacking and learn to implement them using popular Python libraries such as scikit-learn and XGBoost. By the end of this course, you’ll be equipped with the skills you need to implement and optimize ensemble models in real-world machine learning tasks.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.

See the readme file in the main branch for updated instructions and information.

Installation

There are two options for installation:

1. Use Codespaces (the video walks through this):

• Click on the green "Code" button. Then click on the "Codespaces" tab. Click the "+" button to create a codespace.
• Wait a minute or two for the Codespace to provision
• Open the ensembles.ipynb notebook

2. Install with uv:

• Install uv as per https://docs.astral.sh/uv/getting-started/installation/

• Check out this project:

  git clone git@github.com:LinkedInLearning/applied-machine-learning-ensemble-learning-3959211.git

• Change into the directory of the project:

  cd applied-machine-learning-ensemble-learning-3959211.git

• Run uv sync to build the environment.

• Launch Jupyter with:

uv run jupyter lab

• Open ensembles.ipynb notebook

Instructor

Matt Harrison

Python and Data Science Corporate Trainer, Author, Speaker, Consultant

Check out my other courses on LinkedIn Learning.