AI and ML Track: Observation on Curriculum

[Ayokolapo]

In the AI and ML curriculum, there are critical topics which are missing such as :

• Computer vision and practical use cases such as Object detection, object recognition, pose estimation etc.
• Natural Language Processing and practical use cases such as Speech recognition, Speech synthesis, text analytics etc.
• Unsupervised machine learning e.g. anomaly detection, clustering etc.

Also given the current focus on Generative AI across the world, the intermediate and advanced track should have the following:

• Intro to LLMs
• LLM Fine tuning
• Retrieval Augmented Generation (RAG)

[Ayokolapo]

Still on the above curriculum, there seem to be too many tools taught during the course. This may end up confusing participants. Python is the defacto standard these days for industry grade AI projects. I will suggest the curriculum focuses on Python and various libraries for delivering traditional machine learning, deep learning (Computer vision, NLP etc) and LLMs.

Participants can always learn no-code tools after their program easily if they have to.

[EmmanuelTheCoder]

Thank you @Ayokolapo for your observation. We have it noted. We will soon open up our curriculum for contributions and amendment from experts like you.