Python Programming
Syntax · OOP · Libraries
Course · BeginnerEngineer. Educator. Always a student.
I’m Aayush Raj Regmi. I build intelligent systems and teach the ideas behind them — connecting AI, data, and software with the people who use them.
Based in Nepal. Connected by curiosity.
01 / Learn with me
Open courses and session materials for curious minds. Explore programming, data, AI, and software engineering, then put the ideas into practice.
All teaching resourcesBuilt for the classroom.
Open for everyone.
Syntax · OOP · Libraries
Course · Beginnerpandas · Visualisation · EDA
Course · Beginner → IntermediateSupervised · Evaluation · scikit-learn
Course · IntermediateInference · Testing · Regression
Course · IntermediateMachine learning · Artificial intelligence · Applications
Course · Intermediate12-Factor · Application design · Software engineering
Session · Developer sessionICAN · Data analytics · Artificial intelligence
Course · Professional learning02 / Principles in practice
Three principles that guide how I teach, build, and explore.
Start with a clear mental model. Give the formalism something to attach to.
03 / Projects & research
A project lab spanning agentic systems, Nepali language models, voice AI, and the data behind better decisions.
An orchestration layer for specialist agents that plan, use tools, recover from failures, and hand work back to people.
Explore architecture & design
A reproducible Nepali language-model program spanning corpus curation, Devanagari tokenization, continued pretraining, and instruction tuning.
Explore architecture & design
A realtime voice-agent architecture for Nepali–English conversations, interruptions, tool calls, and human handoff.
Explore architecture & design
04 / Articles & field notes
Detailed guides and small observations on AI, mathematics, engineering, and the systems behind useful software.
Build an evidence-driven retrieval loop with query planning, hybrid search, bounded tool use, grounded answers, and evaluation that exposes failures.
Derive next-token prediction and causal attention, then build a small decoder-only Transformer with a complete PyTorch training and sampling script.
Understand exponential smoothing, ARIMA, supervised forecasting, rolling backtests, and prediction intervals through equations and practical examples.
A common thread
Engineering and teaching inform each other. Building reveals the questions worth asking. Explaining makes the answers clearer. This is a home for both.
A little more about me