Real-World Projects.

The Kidney Tumor Identification System is a state-of-the-art Deep Learning application designed to assist medical professionals in the early detection and classification of kidney tumors from CT scan images. By leveraging Convolutional Neural Networks (CNNs) and MLOps best practices, this system provides accurate, automated diagnostics to reduce the workload on radiologists and improve patient outcomes.

This project guides you through end-to-end AI agent development using Google's Agent Development Kit (ADK). You'll learn to build agents, expose them via REST APIs, create a Streamlit frontend, and deploy them to Google Cloud Run, resulting in a fully functional and production-ready AI agent application.

This project demonstrates how to leverage the Model Context Protocol (MCP) to connect AutoGen agents with Notion, enabling natural language control of Notion workspace operations. You'll build a Flask API, expose it via ngrok, and create an end-to-end workflow for AI agents to create pages, search content, and manage data within Notion.

Build an automated data analysis system leveraging Microsoft's AutoGen framework to create a team of specialized AI agents. This project guides you through processing CSV data, generating visualizations, and producing comprehensive reports using natural language queries.

Develop a sophisticated AI-powered personal assistant using a multi-agent architecture, where specialized agents collaborate to handle tasks like email management, calendar scheduling, research, and travel planning. This project integrates with real-world APIs and is accessible through web, Telegram, and voice interfaces, built on the n8n visual workflow automation platform.

Poultry diseases pose a significant threat to both the health of birds and the overall production efficiency of poultry farms. Early identification of these diseases through fecal analysis is essential for implementing timely interventions. This process not only helps in maintaining flock welfare but also enhances productivity, ensuring a healthier farm ecosystem.

This project establishes an automated Video Compliance QA Pipeline orchestrated by LangGraph, designed to audit content against regulatory standards using a RAG architecture. We leverage Azure Video Indexer for multimodal ingestion (transcripts/OCR) and Azure AI Search to retrieve relevant compliance rules via Azure OpenAI Embeddings. The core reasoning engine, Azure OpenAI (GPT-4o), synthesizes this data to deterministically detect violations, while LangSmith provides granular tracing for LLM workflow optimization. Additionally, Azure Application Insights is integrated for production-grade telemetry, logging, and real-time performance monitoring. This end-to-end system transforms unstructured video into structured, actionable JSON compliance reports with deep full-stack observability.

This project evaluates the security of an MLOps-based machine learning application deployed on a Minikube Kubernetes cluster inside a VM. Intentional vulnerabilities were added to the Deployment and RBAC configurations to simulate real-world weaknesses. KubeHunter was used to detect these security issues, while KubeBench measured the cluster’s compliance against CIS Kubernetes security benchmarks. Together, the tools provided insight into vulnerabilities and hardening requirements for secure Kubernetes deployments.

The AI-Powered Travel Itinerary Planner is a web application that utilizes Large Language Models and real-time search tools to automatically generate personalized travel itineraries. It offers a streamlined and efficient way for travelers to create detailed, day-by-day plans tailored to their specific preferences, destinations, and travel styles, showcasing a complete LLMOps pipeline.

Develop a Flipkart Product Recommender Chatbot using Retrieval-Augmented Generation (RAG) to provide accurate, context-aware product recommendations based on a specific dataset. The system leverages LangChain and LangGraph within a containerized microservices architecture for scalability and observability.

AniBaba is an AI-powered anime recommendation system that provides personalized suggestions based on natural language queries. Leveraging Retrieval-Augmented Generation (RAG), it understands user preferences and retrieves relevant anime from a semantic knowledge base to offer a conversational and discovery-rich experience.

A Neural-Semantic Matching Protocol for Real-Time Job Market Interoperability is a high-tech framework designed to connect the right people to the right jobs instantly and accurately. It moves away from old-school "keyword matching" (where a computer just looks for the word "Python" on a resume) and instead uses artificial intelligence to understand the actual meaning and context of a person's career and a company's needs. Neural-semantic matching protocols aim to enhance real-time interoperability in the job market, connecting job seekers with suitable opportunities more efficiently and effectively. By leveraging the combined power of Large Language Models (LLMs) and the Model Context Protocol (MCP), neural-semantic matching protocols revolutionize job market interoperability.

In this comprehensive project, you will step into the shoes of a Senior ML Engineer. You won't just train a model; you will architect a robust, scalable system to detect network security threats. You will abandon ad-hoc scripting and adopt professional workflows, starting with a structured development environment and rigorous Data Engineering (ETL) pipelines. You will implement a complete MLOps workflow. This includes tracking experiments with MLflow and Dagshub to ensure reproducibility. You will effectively manage data using MongoDB Atlas and automate the flow of data using ingestion and transformation pipelines.

NexusViewPro is a lightweight Python library designed for Data Scientists and Jupyter Notebook users. It addresses the need to view external documentation, tutorials, or live websites without leaving the coding environment. By seamlessly rendering HTTPS websites and embedding YouTube videos directly within .ipynb cells (Jupyter Notebook, JupyterLab, Google Colab), it streamlines the workflow and keeps the focus on code.

Develop a real-time AI-powered interview practice coach using voice interaction. This project involves building a full-stack system where users can practice interviews with an AI interviewer, receive intelligent feedback, and interact with an animated avatar.

Build a robust content generation engine that automates blog creation from topics or video transcripts. This project focuses on creating a Directed Acyclic Graph (DAG) architecture using LangGraph, where specialized agents handle specific tasks such as title brainstorming and content generation, and a router manages translation workflows.

Develop a complete text summarization system from scratch, focusing on summarizing complex dialogues using the SAMSum dataset. This project emphasizes professional NLP pipelines, fine-tuning state-of-the-art models like Google Pegasus, and implementing modular Python code for maintainability and scalability.

Develop an end-to-end Retrieval-Augmented Generation (RAG) system to create a 'Document Search' application, enabling users to chat with their own data from PDFs and text files. This project emphasizes modular coding for production-ready AI applications, utilizing UV for environment setup and comprehensive data lifecycle management.

Develop a production-ready Stateful Agentic AI System that remembers, reasons, and deploys tools using LangGraph and Groq. This project involves building a modular application with a Basic Chatbot, a Tool-Augmented Agent, and an AI News Aggregator, emphasizing end-to-end implementation and professional deployment.

Build a production-ready Multi-Agent Quantitative Analyst system where autonomous AI agents collaborate to scrape market data, analyze trends, and generate professional investment reports. This full-stack application utilizes FastAPI for backend orchestration, Streamlit for the user interface, and integrates Azure PostgreSQL for transaction metadata logging and Azure Blob Storage for report archiving.

This project implements an end-to-end object detection workflow using Faster R-CNN, leveraging DVC for reproducible data versioning and automated pipeline orchestration. Training progress and model metrics are visualized through TensorBoard to ensure optimal performance, while the final model is deployed via FastAPI for high-performance inference.

This project delivers an end-to-end data analytics solution focused on regional sales performance. It encompasses data cleaning, exploratory data analysis (EDA), and interactive dashboard creation using Power BI to provide actionable insights for stakeholders and empower data-driven decision-making.

Develop an intelligent chatbot that translates natural language queries into SQL statements, leveraging Generative AI and Retrieval-Augmented Generation (RAG). This chatbot empowers non-technical users to interact with SQL databases, streamlining data access and reducing query response times.

This project develops a machine learning model to predict cancer risk levels (High, Medium, Low) based on demographic, behavioral, and health data. It addresses class imbalance using techniques like SMOTE and optimizes model performance with hyperparameter tuning, providing crucial insights for early detection and intervention.

Develop an AI-powered chatbot tailored for Air India, leveraging AWS Bedrock APIs for natural language processing. This chatbot will provide real-time support by answering queries about flight schedules, bookings, and various airline services, utilizing a knowledge base of Air India-related documents.

This project focuses on developing a machine learning system to predict customer churn in the telecommunications industry. It covers the entire data science lifecycle, from exploratory data analysis to model deployment, enabling proactive intervention and customer retention.

This project provides a comprehensive end-to-end analysis of customer churn in the telecom industry. You'll perform exploratory data analysis, build interactive dashboards, and generate actionable insights to help telecom companies reduce churn and improve customer retention.

This project focuses on building and deploying image classification models using various architectures. Students will gain hands-on experience with model training, hyperparameter optimization, and deployment on AWS EC2, culminating in a functional image classification service.

This project focuses on developing an automated elephant species classification system using deep learning techniques, specifically convolutional neural networks (CNNs) and transfer learning. By accurately identifying elephants from photographic data, the system aims to enhance wildlife conservation efforts through improved monitoring and data analysis.

This project guides learners through building a comprehensive Power BI report using real-world solar energy data stored in a MySQL database. It covers the entire process, from data integration and transformation to in-depth analysis and visualization, culminating in actionable insights for sustainable energy investment.

This project provides a hands-on experience in creating a comprehensive Power BI report using the Diamonds dataset. Learners will progress from setting up their Power BI environment and understanding the dataset to building interactive visualizations and effectively presenting data-driven insights.

This course empowers learners to conduct comprehensive sales data analysis using SQL Server and Power BI. Participants will learn to clean, transform, model, and visualize real-world sales data, culminating in the creation of interactive and insightful Power BI reports.

This project focuses on predicting student academic outcomes based on demographic, academic, and behavioral data. It demonstrates how educational institutions can use Machine Learning to identify students at risk, understand performance drivers, and make data-driven decisions to improve learning outcomes.The project emphasizes classification and probabilistic modeling, proper data preprocessing, feature importance analysis, and model evaluation.

This project is a real-world, end-to-end Machine Learning application that simulates how financial institutions evaluate loan applications. It uses a two-stage modeling approach where the first model decides whether a loan should be approved or rejected, and the second model predicts the optimal loan amount for approved applicants. The project covers the full ML lifecycle from dataset understanding and experimentation in Jupyter Notebook to modular Python code, configuration management using YAML, and deployment on Streamlit Cloud.

This project is an end-to-end Machine Learning application designed to classify chest diseases from CT scan images. It demonstrates a complete MLOps lifecycle, incorporating experiment tracking, pipeline orchestration, model versioning, and automated deployment using CI/CD pipelines. The solution leverages Deep Learning (CNNs) for classification and provides a web interface for user interaction.

The AI Powered Job Analyzer is a cloud-native application that leverages GPT-4 to automatically screen resumes against job descriptions for hiring accuracy. Deployed on a Kubernetes cluster, it integrates a full ELK stack (Filebeat, Logstash, Elasticsearch, Kibana) to provide robust, real-time logging and system observability.

The Notion ReAct Planner Agent is a smart, reasoning-driven daily planning system powered by a ReAct (Reason + Act) AI agent, deeply integrated with Notion. Unlike traditional task automation tools that follow fixed rules, this system uses structured reasoning to understand user intent, plan actions, and intelligently interact with tools to manage daily workflows. At its core, the project implements the ReAct agent architecture, where the AI alternates between reasoning steps (thinking about what to do next) and actions (calling tools such as Notion APIs or external services). This enables the agent to break down natural language instructions—like planning a day, adding notes, or managing schedules—into logical steps and execute them reliably.

This project is an end-to-end Machine Learning solution designed to detect and classify waste objects in images/live video. It leverages the state-of-the-art object detection model to achieve high accuracy and real-time performance. The system is built with a production-ready mindset, featuring a modular architecture, automated pipelines, and CI/CD integration for cloud deployment.

An end-to-end AI application that automates YouTube SEO analysis using GPT-4 and a professional GitOps pipeline. This project demonstrates how to build a web app and integrate OpenAI, and deploy it onto a Kubernetes cluster using Jenkins, Docker, and Argo CD.

Develop an AI-powered heart murmur detection system using audio signal processing and deep learning techniques. The system will enable users to analyze heart sound recordings, extract relevant features, and classify audio using a trained LSTM model, providing a valuable tool for preliminary cardiac assessment.

Develop a robust thunderstorm forecasting system leveraging machine learning models and MLflow for tracking experiments. This project integrates data preparation, model training, hyperparameter tuning, and deployment to predict thunderstorm occurrences, enhancing weather prediction accuracy and enabling proactive safety measures.

Build a complete system for automating YouTube mixtape creation, transforming raw audio into publish-ready video content. This project teaches students to merge audio tracks, generate descriptions with timestamps, and convert audio into video using Python, FastAPI, and Streamlit.

Develop a modular, web-based attendance management system using Streamlit and Supabase, enabling secure student attendance marking and real-time administrative control over classes, attendance codes, and analytics. The system features roll-number locking for enhanced security and automated CSV exports for comprehensive analytical reporting, providing a robust and scalable solution for educational institutions.

This project aims to build a robust, end-to-end Machine Learning pipeline for predicting the quality of Drinks based on physicochemical tests. It demonstrates a complete ML workflow, emphasizing modularity, reproducibility, and automation.

The End-to-End Recommender System is a machine learning-based application designed to recommend books to users based on collaborative filtering. The project encompasses a complete MLOps pipeline, including data ingestion, validation, transformation, model training, and a web-based user interface for interaction.

Develop an intelligent system to analyze audience sentiment from social video comments. The project focuses on collecting, preprocessing, and classifying user comments to understand overall sentiment trends and provide actionable insights for content creators.

Develop an automated system for analyzing and processing global mobility applications, streamlining the immigration and relocation process. This project focuses on building a complete ML pipeline to automate key aspects of the application review, leveraging feature engineering, model training, and deployment strategies for efficiency and accuracy.