Explainable AI Development Virtual Internship
In this advanced virtual internship, students will learn to build interpretable machine learning models and develop techniques to explain model decisions. The goal is to improve trust and transparency in AI systems, which is crucial as these technologies become more prevalent. Students will gain hands-on experience with state-of-the-art explainable AI methods and apply them to real-world machine learning problems. Upon completion, students will be equipped with the skills to develop AI systems that are more understandable and trustworthy.
Track Overview
Tasks & Milestones
Understand Explainable AI Techniques
AdvancedIn this task, you will learn about the key explainable AI techniques and their underlying principles.
Implement a Simple Explainable AI Model
AdvancedIn this task, you will implement a simple machine learning model and apply an explainable AI technique to understand its decision-making process.
Implement an Interpretable Decision Tree Model
AdvancedIn this task, you will implement a decision tree model and apply techniques to improve its interpretability.
Implement an Interpretable Linear Model
AdvancedIn this task, you will implement a linear model and apply techniques to improve its interpretability.
Implement SHAP for Interpreting Neural Networks
AdvancedIn this task, you will use the SHAP (Shapley Additive Explanations) technique to interpret the predictions of a neural network model.
Implement LIME for Interpreting Image Classification Models
AdvancedIn this task, you will use the LIME (Local Interpretable Model-Agnostic Explanations) technique to interpret the predictions of an image classification model.
Create Visualizations to Explain Model Decisions
AdvancedIn this task, you will create visualizations to effectively communicate the decisions and explanations of your machine learning models.
Develop a Narrative to Explain Model Decisions
AdvancedIn this task, you will create a narrative to effectively communicate the decisions and explanations of your machine learning models to stakeholders.
Prerequisites
- • Strong programming skills in Python
- • Familiarity with machine learning concepts and algorithms
- • Experience with popular ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
Certificate
Certificate of Completion
Earn a certificate upon successful completion