Applied Machine Learning - Stacking Ensemble Models

Resources

• Slides

• Student Notebook

• Solution Notebook

Join us for this hands-on training where you will learn how to greatly enhance the predictive performance of your machine learning models. Using Jupyter Notebooks you'll learn how to create a layer of baseline models, and using packages designed for model stacking, another layer to produce a final model with much better-than-baseline performance. Fill out the form to access the recording, accompanying slides, student notebook, and solution notebook.

What will I learn?

You will learn how to:

• The Data Scientist mindset and keys to success in transitioning from baseline models to stacking models.

• How to select a baseline Machine Learning algorithm

• Discuss alternative stacking methods

• Create simple, two-layer regressor and classifier stacked models

• How to tune hyperparameters using K-fold cross-validation

What should I prepare?

Please note, a Gmail account is required in order to use Colaboratory, a free Jupyter notebook environment. You can join the webinar from your web browser following the instructions you receive in your registration email. All required data/resources will be provided in the training.

Who should attend?

This course is open to all DataCamp Premium learners looking to enhance the performance of their predictive models using the model stacking technique. We recommend that you have taken the following courses before attending:

• Supervised Learning with scikit-learn

• Ensemble Methods in Python

• Practicing Machine Learning Interview Questions in Python

Lisa Stuart

Data Scientist at Amazon