Occlusion-Based Saliency Maps | Explainable AI for Computer Vision

Taxonomy of Explainable AI Methods in Computer Vision | Free XAI Course

Build Class Activation Maps (CAMs) from Scratch with Python & PyTorch Hooks | Free XAI Course

Understanding Class Activation Maps (CAMs) for Deep Learning Interpretability | Free XAI Course

Implementing Guided Backpropagation from Scratch | PyTorch Hooks & Deep Learning Interpretability

Guided Backpropagation theory | FREE Explainable AI (XAI) Course with Python

Grad-CAM with Python | FREE XAI Course | L7 - Gradient-weighted Class Activation Mapping

Grad-CAM Explained | FREE XAI Course | L7 - Gradient-weighted Class Activation Mapping

Debugging a Pot Plant Detector | FREE Python Course | L1 - The Importance of XAI in Computer Vision

Applying Permutation Channel Importance (PCI) to a Remote Sensing Model | Python Tutorial

Explaining Computer Vision Models with PCI

Explaining Anomalies with Isolation Forest and SHAP | Python Tutorial

SHAP with CatBoostClassifier for Categorical Features | Python Tutorial

Applying LIME with Python | Local & Global Interpretations

An introduction to LIME for local interpretations | Intuition and Algorithm |

Friedman's H-statistic Python Tutorial | Artemis Package

Friedman's H-statistic for Analysing Interactions | Maths and Intuition

Accumulated Local Effect Plots (ALEs) | Explanation & Python Code

PDPs and ICE Plots | Python Code | scikit-learn Package

Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math

Permutation Feature Importance from Scratch | Explanation & Python Code

Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models

8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP

Feature Selection using Hierarchical Clustering | Python Tutorial

8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics

Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features

Modelling Non-linear Relationships with Regression

Explaining Machine Learning to a Non-technical Audience

Get more out of Explainable AI (XAI): 10 Tips

The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories

Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals

Data Science vs Science | Differences & Bridging the Gap

About the Channel and my Background | ML, XAI and Remote Sensing

SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems

Introduction to Algorithm Fairness | Causes, Measuring & Preventing Unfairness in Machine Learning

SHAP Violin and Heatmap Plots | Interpretations and New Insights

Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing

Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact

Exploratory Fairness Analysis | Quantifying Unfairness in Data

5 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops

Feature Engineering with Image Data | Aims, Techniques & Limitations

Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices

Interpretable vs Explainable Machine Learning

4 Significant Limitations of SHAP

Shapley Values for Machine Learning

The mathematics behind Shapley Values

SHAP with Python (Code and Explanations)

SHAP values for beginners | What they mean and their applications

5 ways to use a Seaborn Heatmap

Data Exploration with PCA