AI Interview Bot (Qwen AI + Lip Sync)
Conversational AI interview assistant powered by Qwen, with realistic lip-sync for a natural interview experience.
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Technically proficient and highly driven B.Tech student specializing in Artificial Intelligence and Data Science. Adept at building and deploying machine learning models, with growing expertise in deep learning, data processing, and intelligent systems.
An Obsidian-style map of my stack — drag nodes, click to explore connections.
Professional internships and roles in AI and ML.
Working on AI-driven solutions and software development in a healthcare setting. Deploying intelligent systems for hospital workflows and patient care.
Trained and evaluated ML models for classification tasks using Scikit-learn and Pandas. Implemented data preprocessing pipelines and feature engineering techniques.
CertificateContributed to ML models for predictive analytics. Worked on data preprocessing, feature selection, and model evaluation to optimize performance.
CertificateDeveloped AI-based solutions for internal automation. Worked on NLP modules and integrated them with existing workflow systems.
CertificateStandout AI applications and platforms I've built.
Conversational AI interview assistant powered by Qwen, with realistic lip-sync for a natural interview experience.
View on LinkedIn
Learning Management System designed to guide learners through structured career paths with progress tracking and path-based learning.
Watch on YouTubeA selection of AI, ML and computer vision projects.
Real-time vehicle movement detection and object tracking for traffic surveillance using YOLOv5/YOLOv8 and DeepSORT.
Automated attendance system using facial recognition, deployed on Raspberry Pi with real-time detection.
ML-based early detection of multiple diseases using symptom analysis and patient data.
Calculator with NLP capabilities to understand and solve complex mathematical expressions.
Control your computer mouse using hand gestures via webcam with OpenCV and MediaPipe.
Generative Adversarial Networks to create synthetic medical images for training with limited data.
Streamlit app to train, fine-tune and deploy YOLO object detection models.
NLP app that analyzes movie reviews to determine sentiment and audience reception.
Text sentiment classification and analysis using Python and ML models.
Deep learning model for brain tumor detection from medical imaging data.
Automated image model training and dataset preparation for computer vision tasks.
Deep learning model trained on MNIST with TensorFlow for digit recognition.
For more projects, view on GitHub
Professional certifications and course completions.
Have a project idea or want to collaborate? Reach out.