This course introduces the concept of machine learning architecture frameworks, providing solutions for common challenges in machine learning. The primary aim is to help learners understand and apply design frameworks in various aspects of machine learning, from data representation to model deployment. The course covers key topics like data preprocessing, model optimization, and ethical AI practices.

The course is self-paced, meaning learners can progress through the material at their own convenience. There will be no interactive discussions or live sessions. Instead, the course will consist entirely of video lectures, reading materials, and quizzes. The technologies used include an online platform for streaming videos, accessing reading resources, and completing quizzes.