From Scratch, Learn testing types and Strategies involved in all the phases of ML Models (AI) with real time examples
The curriculum structures testing activities around the machine learning lifecycle, covering engineering (overfitting/underfitting), evaluation, and post-deployment stages like data drift.
Includes specific instructions for setting up environments using Postman and the PostBot plugin to test ChatGPT APIs.
Editorial course preview
This view highlights a concrete, legible example from the course presentation.
This agenda slide outlines the course structure, covering topics from an introduction to artificial intelligence and machine learning lifecycles to shift-left testing and API scope.
One signal suggests the content focuses more on high-level phases rather than providing deep dives into specific testing strategies. This review predates the displayed update, and while an update is noted, it does not prove a correction of these concerns.
One signal indicates potential issues with factual accuracy and the depth of understanding presented in the lessons.
The curriculum aligns well with the stated goals of QA professionals entering the AI space.
