Brewing ideas with coffee, coding AI with passion — my world pretty much revolves around data, algorithms, and endless curiosity.
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I am a final year Computer Science undergraduate student from Gautam Buddha University, specialising in Machine Learning. I am an ardent AI/ML Enthusiast and Data Scientist, relentlessly working on ML models in the domains of computer vision, natural language processing and geospatial analysis. In addition, I am an enthusiastic practitioner of Cloud Technology, deeply immersed in hands-on exploration of Microsoft Azure, AWS and Google Cloud Platform.
During my spare moments, I channel my passion into writing and publishing blogs dedicated to these intricate cloud platforms. My hobbies include playing chess, reading books and articles, playing badminton, singing and swimming.
Read MoreWorking on developing and managing both theoretical content and projects in the fields of Artificial Intelligence and Data Science for skill development courses on PhysicsWallah website and PW Institute of Innovation.
Analyzed and visualized the financial and sales data for business clients using advanced Microsoft Excel, Power Query, and DAX programming for designing and developing interactive dashboards in Power BI and Tableau, by integrating and transforming data from multiple sources, including Excel, SQL databases, and dynamic feeds from QuickBooks.
Worked on multispectral data from satellites like Sentinel-2 and LandSAT for the estimation of the local climate, vegetation indices and soil types in different farms to derive patterns for the types of crops grown due to the given factors for developing a recommendation engine to assist farmers in selecting the crops to grow in specific regions.
Worked on developing a software for serving the purpose of object detection and recognition for an Indoor Autonomous Warehouse Drone using Computer Vision, Deep Reinforcement Learning and IoT technology for drone navigation, perception and control.
Organizing Technical Events and conducting workshops for educating participants about the various intricacies and key aspects of Cloud Computing.
Managed and orchestrated several Technical Events and Hackathons, designed Event Brochures and Posters, while driving participation in Nation-Wide Hackathons.
Managed and organized technical events and hackathons and made event brochures and posters
Coordinated multiple Placement Drives, developed Placement Brochures and actively promoted applications for Internships and Off-Campus opportunities.
Python
Django
Flask
HTML
CSS
JavaScript
C
SQL
Java
GitHub
Power BI
VS Code
PyCharm
Microsoft Excel
Google Colab
Google Cloud
Microsoft Azure
AWS
This Reinforcement Learning application, built from scratch using Pygame framework of Python, is a Chess Engine trained using advanced algorithms like Minimax with Alpha-Beta Pruning for strategic decision-making. Its key features include the basic rules of chess, and a display of Move logs using chess notations. Currently working on improving the user interface by adding a menu for flipping the board, choosing the piece for pawn promotion, mouse drag and the engine by adding a 50 move and 3-time repetition rule for Stalemate and adding a database for openings and endgames.
This robust voice assistant is designed to run on the local machine, inspired by the JARVIS voice assistant from the Marvel Universe. It utilizes facial authentication for secure access to the application and listens for specific trigger phrases (hot words) to activate or turn off the voice assistant. Its core features include providing intelligent responses to user inquiries through a chatbot interface, opening websites seamlessly in the default browser, initiating applications installed on the local machine, playing videos directly from YouTube and making phone calls or sending messages through WhatsApp or from the mobile device.
This MLOps Project is for Semantic Segmentation on BDD100K dataset using Weights and Biases dashboard. The dataset contains thousands of images of self-driving cars. The aim of this project is to perceive the object in front of the car, be it traffic light, road, vehicle or a person. The model is then optimized through hyperparameter tuning and evaluated using individual IOU (Intersection over Union) scores. The results of the model training, optimization and evaluation through individual reports, along with the details of job and sweep runs are given at Weights and Biases Workspace.
This is a deep learning app for real-time Sign Language Detection and Translation, built using the YOLOv5 model and implemented using PyTorch and OpenCV libraries. It can translate common phrases in sign language such as 'hello', 'sorry', 'thank you', 'I love you', etc. The app features buttons for starting and stopping video capture and a dictionary of the Common Sign Language phrases in the side bar.
