Training schedule

4 Oct - 4 Oct, 2023
Amsterdam / English


Contact Giovanni Lanzani, if you want to know more about custom data & AI training for your teams. He’ll be happy to help you!
Check out more

Get a toolbox of interpretability techniques that you can use in your daily work, understanding when and how you can and should use it.

Start by applying techniques based on implementations from popular packages and what their drawbacks are, to prevent investigating blackbox models with blackbox interpretability techniques.


Proficient with scikit-learn: pipelines, column transformers, linear models, more complex models (e.g. random forests and gradient boosting)

Clients we've helped

What you'll learn

  • Understand the use cases for model explainability (debug, right to explanation, etc.)
  • Understand when model explainability is not enough (e.g. causality and fairness)
  • Categorize the methods covered between sensitivity/impact and global/local

  • Apply the methods with the provided packages
  • Explain the inner workings of all methods
  • Articulate the downsides for each method
  • Evaluate whether a method is appropriate for the business use case

The schedule

Using scikit-learn, dalex, and shap, we will look at
  • Addictive Attributions
  • Partial Dependence Plots
  • Individual Conditional Expectation Curves
  • Permutation Importance
  • Shapley Values

learning journey

Data Science Learning Journey

meet your trainer

Rens Dimmendaal

Data Scientist
Flexible delivery

The Right Format For Your Preferred Learning Style

In-Classroom & In-Company Training
Online, Instructor-Led Training
Hybrid and Blended Learning
Self-Paced Training
Get in touch with the experts

Have any questions?

Contact Giovanni Lanzani, our Managing Director of Learning and Development, if you want to know more. He’ll be happy to help you!

Call me back

You can reach him out by phone as well at +31 6 51 20 6163

Course: Machine Learning Explainability Training

Book now