Python library for adversarial machine learning, attacks and defenses for neural networks, logistic regression, decision trees, SVM, gradient boosted trees, and more with multiple framework support

Interpretability and explainability of data and machine learning models

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

You were probably looking for our website... this is it. We moved our website here, so you can see the insides of how we work.

Jupyter notebooks, Python and MATLAB code examples, and demos from the textbook "Machine Learning Refined" (Cambridge University Press). See our blog https://jermwatt.github.io/mlrefined/index.html…

Lecture on Developing a (Basic) Machine Learning Workflow

Content for Udacity's Machine Learning curriculum

Starter project code for students taking Udacity ud120

Implementation of the models from "Monetary Economics 2e" by Godley and Lavoie, 2012