A course that teaches you responsible ML methods with each new core concept.
Most modern ML courses walk learners from data cleaning through ML development without deeply discussing the issues surrounding language models or systems that cause harm.
This isn't the way to create Data Scientists who prioritize the needs of the most vulnerable users.
This course is drastically unlike other Data Science and ML courses you've seen before.
Machine Learning core concepts alongside responsible ML principles
How to identify societal biases while analyzing datasets
How to have nuanced conversations around uncertainty in ML
Common pitfalls and how to avoid/mitigate them
Why MLOps and observability tools are crucial
How to decide when to use an interpretable vs black-box model
How to create models that increase equity
How to document models for transparency and accountability
No Glossing Over Racial Features
No Using Racist and Sexist Data Examples
No Ignoring Historical Context