Ementorhub

What you'll learn

Explain and implement model-agnostic explainability methods.

Visualize and explain neural network models using SOTA techniques.

Describe emerging approaches to explainability in large language models (LLMs) and generative computer vision.

Skills you'll gain

Generative AI

Large Language Modeling

Artificial Neural Networks

Image Analysis

Visualization (Computer Graphics)

Data Ethics

Predictive Analytics

Machine Learning

Artificial Intelligence and Machine Learning (AI/ML)

Deep Learning

Python Programming

There are 3 modules in this course

Through discussions, case studies, programming labs, and real-world examples, you will gain the following skills: 1. Implement local explainable techniques like LIME, SHAP, and ICE plots using Python. 2. Implement global explainable techniques such as Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots in Python. 3. Apply example-based explanation techniques to explain machine learning models using Python. 4. Visualize and explain neural network models using SOTA techniques in Python. 5. Critically evaluate interpretable attention and saliency methods for transformer model explanations. 6. Explore emerging approaches to explainability for large language models (LLMs) and generative computer vision models. This course is ideal for data scientists or machine learning engineers who have a firm grasp of machine learning but have had little exposure to XAI concepts. By mastering XAI approaches, you'll be equipped to create AI solutions that are not only powerful but also interpretable, ethical, and trustworthy, solving critical challenges in domains like healthcare, finance, and criminal justice. To succeed in this course, you should have an intermediate understanding of machine learning concepts like supervised learning and neural networks.

Explainable Deep Learning

Explainable Generative AI