AI Explainability 360 for Explainable AI and ML - eLearning (W7117G) » zur vollständigen Seminarliste
The Big Picture of Trustworthy AI and AI Explainability
- Recognize the need for Trustworthy AI
- Describe and differentiate various factors that can build trust in AI
- Appraise situations that require a focus on AI explainability
- Recognize different methods of achieving explainability
Methods for Explainable AI & Overview of AI Explainability 360 Toolkit
- Identify different methods of achieving Explainable AI
- Recognize the role of open-source AI Explainability 360 toolkit in supporting explainability
- Describe various features and capabilities of the open-source AI Explainability 360 toolkit
Hands-on with AI Explainability
- Conduct an end-to-end explainability exercise, including model-building, evaluation, and other considerations pertaining to explainability
- Apply explainability algorithms to create interpretable models
This course is intended primarily for Analytics Leaders, Data Science Leaders and Practicing Data Scientists, Machine Learning Engineers and AI specialists. Anyone with interest in Explainable AI and AI Trust having the prerequisite knowledge required.
In order to be successful, you should have a basic understanding of Data Science workflow, Data Preprocessing, Feature Engineering, Machine Learning Models, Hyperparameter Optimization, Evaluation measures for models, Python Helpful but not necessary. Two helpful, but not necessary, courses to consider: Reducing Unfair Bias in Machine Learning and AI FactSheets for Transparency and Governance
Das Training findet auf Deutsch statt.