By week one, students will grasp AI basics, machine learning terms, and implement linear regression in Python.

Cover logistic regression, KNN, SVM concepts, and implement these models in Python.

By week three, students will understand Naive Bayes, decision trees, and random forests, and implement these models in Python, analyzing their uses.

Cover clustering techniques, dimension reduction methods, and their implementation in Python.

Cover deep learning fundamentals, neural network architecture, and build, train, and evaluate neural networks in Python.

Cover generative AI, GANs, and develop prompt engineering skills for optimizing AI model prompts.

Cover Retrieval-Augmented Generation, emerging AI trends, ethical considerations, and the societal impact of AI.

Every student presents their course project, revises key concepts, and explores pathways for further study.