How to learn Recommendation Systems after 12th in Delhi

The degree route

The degree route is a B.Tech with genuine Recommendation Systems depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means elective-level coverage 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 Recommendation Systems

Recommendation systems predict which items a user is likely to want, using collaborative filtering, content features or learned embeddings. They power feeds, search ranking and e-commerce personalisation. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & DS).

• The AI & Data Science track at VSET is the natural home for recommendation work, being one of the seven GGSIPU-affiliated B.Tech programmes.
• Embeddings and vector similarity — the core mechanism of modern recommenders — are documented in the curriculum at learn.engineering.vips.edu through the vector database and RAG material.
• Machine learning and deep learning foundations in the same curriculum supply the modelling methods.
• Recommenders are not published as a separate library topic, so coverage is best described as elective.

Where Recommendation Systems skills lead

Graduates applying Recommendation Systems skills typically target roles such as Machine Learning Engineer, Data Scientist, Search / Ranking Engineer, Personalisation Engineer, Analytics Engineer. 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.