Academic Background
Engineering graduate with a foundation in Computer Science — built on four years of rigorous coursework, hands-on projects, and applied AI/ML work.
Bachelor of Technology
Computer Science & Engineering
Core Coursework
Key Highlights
- Built 3+ production AI/ML systems during degree — RAG pipelines, CNN classifiers, and MLOps workflows
- Applied deep learning research on facial micro-expression recognition achieving 87% accuracy
- Completed industry internship at REGex Software Services alongside final year studies
- Reached national top 10 at Smart India Hackathon with AI document intelligence system
- Runner-up at LPU TechFest for real-time emotion-adaptive learning platform
Year-by-Year Progression
Foundations
- Mathematics I & II
- Programming in C
- Digital Electronics
- Engineering Physics
- Data Structures
Core Engineering
- OOP with Java & Python
- DBMS & SQL
- Computer Networks
- Operating Systems
- Algorithms Analysis
AI / ML Depth
- Machine Learning
- Deep Learning & CNNs
- Natural Language Processing
- Computer Vision
- Cloud Computing
Specialisation & Industry
- MLOps & Deployment
- Software Engineering
- AI Ethics & Safety
- Final Year Project
- Industry Internship
Online Accreditations
Machine Learning Specialization
3-course specialization covering supervised learning, unsupervised learning, and recommender systems by Andrew Ng.
Introduction to Deep Learning
IIT-Kharagpur NPTEL course on deep learning fundamentals, CNNs, RNNs, and advanced architectures.
Azure AI Fundamentals (AI-900)
Foundational understanding of AI/ML concepts and Azure cognitive services — vision, language, and decision capabilities.
Kaggle — 5 Micro-Courses
Completed Pandas, Feature Engineering, Intro to ML, Intermediate ML, and Data Visualization tracks on Kaggle Learn.