₹30,000 is real money. You don't pay to apply. Visit, ask hard questions, take the assessment — then decide.
✓
That's the first step done.
Keep your phone handy—our team will call within a day to book your free entry assessment.
Become an Applied AI Engineer in six months — machine learning, deep learning, and the LLM systems companies are actually hiring for: RAG, agents, fine-tuning, and deployment.
Prerequisite: working Python knowledge. This is an advanced track — new to programming? Start with our Full-Stack program.
The single most in-demand applied AI skill — retrieval, chunking, ranking, citations, evaluation.
Gradient boosting, evaluation metrics, and knowing when not to reach for deep learning.
Train networks, fine-tune pretrained models, understand transformers from the inside.
Function calling, multi-step workflows, guardrails — and how they fail.
Deploy, monitor, manage token cost, handle failure — the engineering half of AI.
Top 8 of every batch earn a paid internship on real AI projects. All 8 considered for a role.
Chadura has worked in bioinformatics and healthcare AI — domains where a wrong answer matters. That experience shapes how we teach: rigorous evaluation, honest reporting of limitations, and real attention to privacy.
You can take your capstone in this direction — clinical text, genomic data, or medical imaging — and graduate with a portfolio project almost nobody else in the region will have.
The language of AI.
Applied, not proof-based.
Data pipelines that hold up.
Evaluation done honestly.
Classical ML, end to end.
What still wins on real data.
Build and train networks.
Vision without training from zero.
Build real applications.
Embeddings, retrieval, citation.
Multi-step workflows, guardrails.
LoRA, and when it's worth it.
You finish having deployed a real AI application, evaluated it honestly, and defended its failure modes out loud.
NumPy, linear algebra, gradients, probability — applied, at pace.
Cleaning, leakage, cross-validation, reproducible pipelines.
Regression, trees, gradient boosting, clustering — and evaluating models honestly.
Backprop, training practice, CNNs, transfer learning, transformers intro.
Prompting, embeddings, RAG, vector databases, agents, fine-tuning, and evaluating LLM output properly.
Deployment, monitoring, token cost, ethics and privacy — then your capstone, defended.
We build production systems, including AI work in health and bio data. You'll learn to evaluate models the way people do when a wrong answer has consequences.
Your certificate isn't attendance-based. You earn it by scoring across every domain, verified by proctored exams.
3–4 hrs on campus, including GPU lab time.
Technical writing, explaining AI limits honestly.
Real AI systems, including our bio/health work.
Weekly tests toward your 1200.
Takes 60 seconds. We'll call you to book a free entry assessment — no payment, no pressure. Come see the campus, meet the trainers, and decide for yourself.
₹30,000 is real money. You don't pay to apply. Visit, ask hard questions, take the assessment — then decide.
Keep your phone handy—our team will call within a day to book your free entry assessment.