Introduction to Computer Fundamentals and Networks
Overview of computer architecture, CPU components, memory types, input/output systems, number systems, logic gates, and basics of computer networks including LAN, WAN, and Internet connectivity.

Software and Programming Concepts
Differentiation between hardware and software, introduction to operating systems, file systems, and programming languages (high-level, assembly, and machine languages). Concepts of algorithms and flowcharts are also introduced. 

Python Programming Essentials
Learning the Python programming environment, syntax, data types, operators, control statements, functions, and commonly used libraries such as NumPy, OpenCV, and Pandas through hands-on coding exercises. 

Foundations of Artificial Intelligence
History of AI, definition and characteristics of AI agents, understanding machine learning and deep learning, and differentiating between traditional programming and ML paradigms. 

Machine Learning Techniques
Study of supervised (regression, classification) and unsupervised (K-means clustering) learning methods, KNN, decision trees, and introduction to model design and evaluation processes. 

Artificial Neural Networks and Deep Learning
Understanding artificial neurons, perceptron learning rule, multilayer networks, and the basics of forward and backward propagation. Comparison between shallow and deep learning. 

AI Applications in Key Domains
Exploring real-world AI implementations in NLP, speech recognition, computer vision, and weather prediction. Application building using Python toolkits like Scikit-learn and NLTK. 

AI for Societal Impact and Industries
Application of AI in healthcare (diagnostic and predictive systems), business (economic forecasting, eCommerce recommendation systems), government policy analytics, and environmental sustainability. 

Chatbots, Large Language Models, and ChatGPT
Structure and working of chatbots, understanding large language models, prompt engineering, and their influence across education, health, and other sectors. 

Ethics, Risks, and Future of AI
Discussion on the ethical implications, risks of AI misuse (job loss, bias, surveillance), and core AI ethics principles focusing on fairness, transparency, and trust.