Machine Learning Explainability

Explain the use cases for model explainability

Evaluate when model explainability is not enough (correlation vs. causality, fairness)

Categorize the used methods into sensitivity vs. impact as well as explaining single predictions (local explainability) vs. multiple predictions (global explainability)

Apply the explainability methods with the provided Python packages

Summarize the advantages and disadvantages for each method

Evaluate whether a method is appropriate for the business use case

You are familiar with Data Science and Machine Learning concepts and can comfortably program in Python

You can already build and train machine learning models using Scikit-Learn

You want to better understand how your Machine Learning models make their predictions

You understand that your use case could fall under the “high risk AI applications” category of the EU AI act and explaining your model will be a must-have in the future.

You have never programmed in Python before

You are unfamiliar with core Machine Learning concepts, such as train-test splits, model training and measuring model performance with metrics

You are only interested in achieving the best performance with your models and not how they achieved it

All literature and course materials are included in the price

Information on the software and tooling will be shared before the start date

This course requires a laptop

Online courses are delivered via Zoom or Microsoft Teams