Live training

Applied Machine Learning - Stacking Ensemble Models

Join us for this live, 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. This session will run for approximately three hours, allowing you time to really immerse yourself in the subject, and includes short breaks and opportunities to ask the expert questions throughout the training.

Thursday 6 August, 1 PM EDT, 6 PM BST
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Python

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 course before attending:

Lisa Stuart Headshot

Lisa Stuart

Data Scientist

Lisa Stuart is a Data Scientist with a wealth of industry experience. She is currently on the DSP Big Data Analytics Team at Amazon where she and her team use statistical analysis and machine learning to improve processes around successful and on-time delivery for each and every Amazon order. Prior to that, she built predictive models for targeted marketing at Costco and Expedia and managed dashboards for process automation. At Starbucks, she managed a team of data scientists to build a predictive model on geopolitical stability of countries around the world to make informed decisions on expansion and supply routes. In her free time, you'll find her hanging out with her beloved German Shepherd/Husky Blaze.
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