Model Interpretability Courses and Certifications

Association for Computing Machinery (ACM)

LEMNA - Explaining Deep Learning Based Security Applications

Explore LEMNA, a novel technique for explaining deep learning models in security applications, addressing transparency concerns and improving interpretability for critical decisions.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Simons Institute

Interpretability - Now What?

Explore machine learning interpretability methods, focusing on Testing with Concept Activation Vectors (TCAV) and its applications in image prediction and medical diagnosis.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Alan Turing Institute

Machine Learning - From Black Boxes to White Boxes - Mihaela van der Schaar

Exploring innovative approaches to make complex machine learning models interpretable and trustworthy, especially in medical applications, for diverse users including clinicians and patients.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

Toronto Machine Learning Series (TMLS)

Interpretable Self-Explanatory Machine Learning Models

Explore interpretable machine learning models that are self-explanatory, focusing on making complex neural networks more transparent and understandable for critical applications.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Toronto Machine Learning Series (TMLS)

Sensitivity and Interpretability of AI Models in Regulated Industries

Explore sensitivity analysis for AI model interpretability in regulated industries. Learn design and implementation approaches using case studies from healthcare and human activity data.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Data Science Festival

Where Did My Model Go Wrong? Toolkits of Interpretability, Bias and Fairness to the Rescue

Explore techniques to identify and correct biases in machine learning models, interpret results, and ensure fair outcomes in model development and deployment.

• YouTube
• 38 minutes
• On-Demand
• Free Video

MLOps World: Machine Learning in Production

5 Techniques to Increase Adoption Rates via Interpretability

Discover 5 techniques to boost ML project adoption through enhanced model interpretability, fostering trust and bridging gaps between end-users and data scientists.

• YouTube
• 33 minutes
• On-Demand
• Free Video

UofU Data Science

Evaluating Input Attribution Methods in Machine Learning - Plausibility and Comprehensiveness

Explore advanced concepts in gradient-based input attribution, from plausibility assessment to comprehensive evaluation methods like ROAR and Recursive-ROAR techniques.

• YouTube
• 37 minutes
• On-Demand
• Free Video

UofU Data Science

Gradient-Based Input Attribution Methods and Their Applications

Explore gradient-based input attribution methods, from fundamental concepts to practical applications, limitations, and advanced extensions in machine learning model interpretability.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Simons Institute

Scalably Understanding AI with AI - Using AI Systems for Model Behavior Analysis

Explore advanced techniques for analyzing AI systems, from behavior elicitation to neuron description, focusing on using AI tools to understand and improve AI model behaviors and representations.

• YouTube
• 46 minutes
• On-Demand
• Free Video

MIT Deep Learning in Life Sciences Spring 2020

Explore deep learning applications in genomics, from neural networks to single-cell analysis. Gain insights into regulatory genomics, epigenomics, and genetics through advanced computational techniques.

• YouTube
• 19 hours
• On-Demand
• Free Video

2025 Python for Linear Regression in Machine Learning

287 ratings at Udemy

Linear and Non-Linear Regression, Lasso Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection | Outliers Removal

• Udemy
• 14 hours 36 minutes
• On-Demand
• Paid Course

Explainable AI in Python

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

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Applied Data Science Ethics

Learn practical tools to build ethical AI models, interpret results, audit for bias, and implement fairness in data science projects using real-world datasets and Python code.

• edX
• 4 weeks, 4-5 hours a week
• On-Demand
• Free Online Course (Audit)

Evaluating Model Effectiveness in Microsoft Azure

This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.

• Pluralsight
• On-Demand
• Free Trial Available