I strongly believe that teaching is one of the best ways to learn.
Over the years, I’ve created structured resources and mentored peers to make AI/ML concepts more accessible.
📘 Teaching Repositories
Deep Learning Complete Guide
A complete set of notebooks covering neural networks, CNNs, RNNs, transfer learning, and optimization techniques.
Designed to be used as self-study material or workshop notes.Machine Learning Complete Guide
An end-to-end ML curriculum with examples on data preprocessing, model training, evaluation, and deployment.
Built as a reference for beginners and intermediate learners.CS02: Introduction to Python Programming
A beginner-friendly Python course covering programming fundamentals, data structures, and problem solving.
Used in peer teaching and coding bootcamps.
🧑🤝🧑 Mentorship
CDAC PGDAI Course (5 mentees): Guided a group of postgraduate students in their project on Signature Verification using Deep Learning.
- Helped them with project ideation, model selection (CNN, RNN, hybrid approaches), and evaluation.
- Provided continuous feedback and support on code, research methodology, and presentation.
Peer mentoring: Assisted juniors in understanding advanced concepts like spiking neural networks, transformers, and reinforcement learning, and guided them on project structure and publication processes.
✨ I see teaching not just as instruction, but as collaborative learning, where both mentor and student grow together.