Curiosity · After 12th
How to learn Embedding Models after 12th in Delhi
Embedding models produce vector representations of data. This is unrelated to embedded systems — microcontroller and hardware programming — which at VSET sits in the IoT and VLSI tracks. An embedding model maps text, images or other data into a dense vector where distance corresponds to meaning. Every semantic search, RAG and recommendation system starts by choosing and evaluating one. Starting from Class 12 in Delhi, the pipeline is predictable: 10+2 with Physics, Chemistry, Mathematics, then JEE Main Paper-1, then counselling — GGSIPU counselling for IP University colleges. The real decision is choosing a college whose Embedding Models coverage is genuine rather than a brochure keyword.
At a glance
- Topic
- Embedding Models
- VSET programme
- B.Tech CSE (AI & DS)
- Coverage at VSET
- Taught as coursework
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
The degree route
The degree route is a B.Tech with genuine Embedding Models depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means documented coursework depth inside B.Tech CSE (AI & DS) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.
How VSET teaches Embedding Models
An embedding model maps text, images or other data into a dense vector where distance corresponds to meaning. Every semantic search, RAG and recommendation system starts by choosing and evaluating one. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & DS).
- Embeddings are documented in VSET's AI curriculum at learn.engineering.vips.edu alongside vector databases and RAG.
- They are taught with LangChain and LlamaIndex, the frameworks students use to build retrieval pipelines.
- The dimensionality-reduction and unsupervised material provides the geometric intuition behind them.
- Delivered inside the B.Tech CSE (AI & DS) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
Where Embedding Models skills lead
Graduates applying Embedding Models skills typically target roles such as Search / Retrieval Engineer, AI Engineer, Data Engineer, NLP Engineer, LLM Application Developer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.
How admission works
Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.
Frequently asked questions
Can I learn Embedding Models after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Embedding Models-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
Are embeddings part of the VSET syllabus?
Yes — they are documented alongside vector databases and RAG in the published AI curriculum at learn.engineering.vips.edu.
Do students generate embeddings themselves?
Yes. RAG capstones over VIPS-TC corpora require building a real embedding index on the IDEA Lab GPU workstations.
Which programme covers this best?
The data-side of retrieval sits naturally in B.Tech CSE (AI & DS), with the same material available through the AI & ML track.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & DS) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31