Course Objective:
To develop understanding, appreciation, and readiness for Artificial Intelligence (AI) and its real-life applications through interactive and hands-on learning.

Core Focus Areas:
Learners are introduced to the three major AI domains — Data Science, Computer Vision, and Natural Language Processing (NLP) — along with basic Python programming.

Learning Outcomes:
Students learn to identify AI applications, understand human–machine interactions, appreciate AI ethics, explore job opportunities, and connect AI with Sustainable Development Goals (SDGs).

Assessment Scheme:
Total marks: 100 (Theory 50 + Practical 50). The practical part includes project work, viva, and AI programming exercises.

Course Structure:

Part A: Employability Skills (Communication, ICT, Entrepreneurial, Green Skills).

Part B: Subject-Specific Skills (AI Introduction, AI Project Cycle, Advanced Python, Data Science, Computer Vision, NLP, Evaluation).

Part C: Practicals.

Part D: Project/Field Visit/Portfolio.

Key AI Units:

Introduction to AI – Basics, applications, ethics.

AI Project Cycle – Problem scoping, data acquisition, modelling, evaluation.

Data Science – Using NumPy, Pandas, Matplotlib.

Computer Vision – Image processing, OpenCV basics.

NLP – Text processing, chatbots, bag-of-words model.

Evaluation – Accuracy, precision, recall, F1-score.

Practical Component:
Minimum 15 Python programs covering list operations, data visualization, CSV handling, image reading, and statistical analysis.

Project Work / Portfolio:
Students must complete one project related to AI and SDGs (e.g., Student Marks Prediction Model, Fire Detection using CNN) or participate in AI exhibitions and hackathons.