AI System Development

Perception of the AI Experience Centre

To create a world-class AI Experience Centre building that empowers students, faculty, researchers, and industry professionals with practical expertise in Large Language Models (LLMs), Generative AI, AI Agents, Multi-Agent Systems, Enterprise AI Automation, Intelligent Decision Systems, AI Product Engineering and Responsible AI.

Course Overview

The course is designed in multiple progressive levels starting from AI foundations, Deep Learning, Large Language Models, Generative AI, Agentic AI, Enterprise AI, Responsible AI, and AI Product Engineering. The initiate focuses on both theoretical understanding and hands-on practical implementation using industry-standard tools and frameworks.

Objectives of the Course

• Build a sustainable AI knowledge ecosystem that continuously supports advanced learning, product innovation and global AI competency development.
• Prepare professionals to lead next-generation AI transformation initiatives by combining technical expertise, domain intelligence, and responsible innovation practices.
• Promote practical implementation of AI Automation, Intelligent Decision Systems, predictive analytics, conversational AI and AI-driven digital transformation strategies.

Layout of Course

Level 1 – AI & Machine Learning Foundation

Artificial Neural Networks (ANN), Single-layer and Multi-layer Perceptron (MLP), Backpropagation, Linear Regression, Logistic Regression, Bayesian Classifier, Decision Trees, Naïve Bayes Classifier, SVM, Random Forest, K-Nearest Neighbors, PCA for dimensionality, Visualize clusters in 2D using T-SNE.

Level 2 – Neural Networks & Deep Learning

Basics of Deep Learning: Importance, Large Datasets, Architectures, End-to-end Model Design, RNNs, CNNs, LSTMs, Transformers, Train a model for handwritten digit recognition (MNIST), speech recognition. Computer Vision: Industry Use Cases, Case Studies, Latest Trends, Natural Language Processing (NLP): Industry Use Cases, Case Studies, Latest Trends and applications.

Level 3 - Large Language Models (LLMs)

LLM architecture, pre-training, fine-tuning, Retrieval-Augmented Generation (RAG), vector databases, hallucination handling and open-source LLM deployment. Chatbots, PDF knowledge assistant and institutional AI assistants using LangChain, Ollama, Llama Index, Chroma DB and FAISS.

Level 4 - Generative AI Specialization

Generative AI architectures, GPT models, prompt engineering, embeddings, tokenization, and multimodal AI systems. AI-assisted coding, content generation, AI image generation, and intelligent AI assistants using ChatGPT, Claude, Gemini, Huggingface, and Perplexity.

Level 5 - Agentic AI & Autonomous Systems

Autonomous AI agents, multi-agent collaboration, planning and reasoning, memory systems, workflow automation, and human-in-the-loop systems. AI scheduling assistants, research agents, and enterprise automation systems using CrewAI, AutoGen, LangGraph, OpenAI Agents SDK and Microsoft Copilot Studio.

Level 6 - Capstone Projects

• AI-powered chatbot with voice interface.
• Recommendation system for movies or products.
• Fraud detection system using supervised ML.
• Face recognition attendance system.
• AI assistant using NLP + voice commands.
• Conventional AI for Customers
• Environment risk tracker for safety and security of VIPs
• Fake news detection by Explainable AI
• Sentiment analysis for product review

Recommended AI Tools & Frameworks