• Introduction to AI
• Applications of AI
• AI Terminologies
• AI Project Life Cycle Stages
• Datasets
• Mathematical Foundations for AI and ML
• Research Fields Associated with AI
• Statistics Foundations
• Advanced Python for AI and Machine Learning
• Hands-on with NumPy and Pandas
• Exploratory Data Analysis (EDA)
• Hands-on with Matplotlib and Seaborn
• Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• Data Pre-processing
• Regression Algorithms
• Regression Model Performance Metrics
• Classification Algorithms
• Classification Model Performance Metrics
• XGBoost / LightGBM
• Model Tuning
• Model Explainability and Interpretability
• Clustering Algorithms
• Clustering Model Performance Metrics

• Dimensionality Reduction
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Foundations of Neural Networks and Deep Learning
• Hands-on with TensorFlow and Keras
• Artificial Neural Networks (ANN)
• Deep Neural Networks (DNN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transfer Learning
• Working with Pre-trained Models
• VGG16 / VGG19
• GoogLeNet / InceptionV3
• ResNet50
• Attention Mechanism
• Vision Transformers (ViT)
• Time Series Forecasting

Natural Language Processing

• NLP Foundations
• Hands-on with NLTK
• Hands-on with spaCy
• NLP with Deep Learning
• Transformer Models
• BERT
• GPT

Computer Vision

• Computer Vision Foundations
• CNN-based Computer Vision
• GANs (Generative Adversarial Networks)
• Transfer Learning for Computer Vision
• Pre-trained Vision Models
• Hands-on Computer Vision Projects

Practical Projects

• NLP-based Project
• Computer Vision-based Project

• Foundation of Generative AI
• Foundation of ChatGPT
• Generative AI & LLM Engineering
• Transformer Architecture
• LLM Ecosystem
• Hugging Face
• Prompt Engineering
• Embeddings
• Vector Databases
• Retrieval-Augmented Generation (RAG)
• Advanced RAG
• Fine-tuning
• Parameter-Efficient Fine-Tuning (PEFT)
• LoRA
• LLM Evaluation
• Large Language Model APIs
• Open-source LLMs
• Model Selection
• Chunking Strategies
• Semantic Search
• Hybrid Search
• Reranking
• Hallucination and Grounding
• LLM Application Architecture

• Introduction to Agentic AI
• AI Agents
• Agent Architecture
• ReAct
• Tool / Function Calling
• Planning and Reasoning
• Agent Memory
• Agent Workflows
• Agent Orchestration
• Multi-Agent Systems
• Agent Collaboration
• Role-based Agents
• Multi-Agent Workflows
• Human-in-the-Loop Agents
• Agent Evaluation
• Real-world Business Automation

• AI Application Architecture
• Building AI Applications with Python
• Streamlit for AI Application Development
• Building Interactive AI Dashboards and Applications
• Flask for AI/ML API Development
• REST API Development with Flask
• Integrating ML/DL Models with APIs
• Integrating LLMs with Web Applications
• Integrating RAG Applications with Web Interfaces
• AI API Development
• Model Serving and Inference
• Docker for AI Applications
• End-to-End AI Application Development

Industry-oriented AI Projects

The programme includes hands-on implementation using 100+ real-world projects and datasets across diverse AI and Machine Learning use cases. Selected projects and use cases are listed below. Projects may be adapted based on the learners’ background, business domain, and programme requirements.

• Customer Churn Prediction – Develop a classification model to identify customers likely to discontinue a service.
• House Price Prediction – Build an end-to-end regression solution for property price prediction.
• Customer Segmentation – Apply clustering techniques to identify customer segments for targeted business strategies.
• Loan Default / Credit Risk Prediction – Develop an explainable ML model for credit risk assessment.
• Sales / Demand Forecasting – Develop a time-series forecasting solution for business demand prediction.

• Image Classification System – Develop an image classification solution using CNN and transfer learning.
• Cat vs. Dog Image Classification – Develop a CNN-based image classification model to distinguish between cats and dogs.
• Handwritten Digit Recognition – Develop a deep learning model to recognize handwritten digits using image datasets.
• Waste Classification – Build a computer vision model to classify waste into different categories for automated waste management.
• Brain Tumor Detection / Classification – Develop a deep learning-based system for detecting or classifying brain tumors from medical images.
• Transfer Learning-based Image Classification – Develop an image classification solution using pre-trained models such as VGG, Inception, or ResNet.
• Vision Transformer-based Image Classification – Develop an image classification solution using Vision Transformer (ViT) models.

• Sentiment Analysis System – Develop an NLP solution for analysing customer reviews or feedback.

• Text Classification System – Automatically classify emails, documents, tickets, or customer queries.
• Document Information Extraction – Extract important entities and information from unstructured documents.

• Semantic Search System – Develop a semantic search application using embeddings and transformer models.

• AI-powered Document Question Answering – Build an LLM-based application that answers questions from uploaded documents.
• Enterprise RAG Chatbot – Develop a domain-specific chatbot using RAG and a vector database.
• AI-powered Meeting / Document Summarizer – Build an application for summarizing long documents or meeting transcripts.
• Domain-specific LLM Application – Build an LLM application for a selected business domain.

• AI Research Assistant – Develop an AI agent that searches, processes, and summarizes information.
• Business Process Automation Agent – Build an AI agent that performs a multi-step business workflow using external tools.
• AI Data Analysis Agent – Build an agent capable of analysing datasets and generating insights and reports.
• Agentic RAG System – Develop an intelligent system that combines RAG, reasoning, tools, and agent workflows.

• AI Dashboard using Streamlit – Develop an interactive AI/ML application with data visualization and model predictions.
• AI REST API using Flask – Develop and expose an AI/ML model through REST APIs.
• End-to-End AI Application – Build and integrate a complete AI solution covering data processing, model/LLM inference, API development, user interface, and deployment.