| Month 1 |
Introduction to AI, AI tools, prompt engineering, responsible AI |
12 hrs |
Personal AI Productivity Portfolio |
|
| Month 2 |
Python, Pandas, NumPy, data cleaning, visualization, statistics |
12 hrs |
Student Performance Analytics Dashboard |
|
| Month 3 |
ML concepts, supervised learning, unsupervised learning, model evaluation |
12 hrs |
One ML prediction or classification model |
|
| Month 4 |
Deep learning basics, NLP, computer vision and domain applications |
8 hrs |
NLP/CV prototype such as sentiment analyzer or image classifier |
|
| Month 5 |
LLMs, APIs, embeddings, RAG, AI agents and automation |
8 hrs |
University AI Assistant Prototype |
|
| Month 6 |
Capstone development, product thinking, career preparation and demo day |
8 hrs |
Final capstone, GitHub/portfolio, presentation and demo |
|