This repository contains slides, notebooks, and data for the Machine Learning University (MLU) Responsible AI class. Our mission is to make Machine Learning accessible to everyone. We have courses available across many topics of machine learning and believe knowledge of ML can be a key enabler for success. This class is designed to help you get started with Responsible AI, learn about widely used Machine Learning techniques, and apply them to real-world problems.
Watch all Responsible AI video recordings in this YouTube playlist from our YouTube channel.
There are three lectures and one final project for this class.
Lecture 1
| Title | Studio lab |
|---|---|
| Exploratory Data Analysis |  |
| Final Challenge Day 1 | |
| Completed Final Challenge Day 1 | |
Lecture 2
| Title | Studio lab |
|---|---|
| Data Preparation | |
| Disparate Impact | |
| Logistic Regression | |
| Final Challenge Day 2 | |
| Completed Final Challenge Day 2 | |
Lecture 3
| Title | Studio lab |
|---|---|
| Equalized Odds | |
| SHAP | |
| Clarify and Model Monitor | |
| Final Challenge Day 3 | |
| Completed Final Challenge Day 3 | |
Final Project: Practice working with a "real-world" dataset for the final project. Final project dataset is in the data/final_project folder. For more details on the final project, check out this notebook.
Interested in visual, interactive explanations of core machine learning concepts? Check out our MLU-Explain articles to learn at your own pace! Relevant for this class is this article on Equality of Odds.
If you would like to contribute to the project, see CONTRIBUTING for more information.
The license for this repository depends on the section. Data set for the course is being provided to you by permission of Amazon and is subject to the terms of the Amazon License and Access. You are expressly prohibited from copying, modifying, selling, exporting or using this data set in any way other than for the purpose of completing this course. The lecture slides are released under the CC-BY-SA-4.0 License. This project is licensed under the Apache-2.0 License. See each section's LICENSE file for details.