Capability · Internships
Transfer Learning internships for B.Tech students in Delhi
In machine learning, transfer learning means reusing a trained model on a new task. It is not credit transfer or migration between colleges, which at GGSIPU is a separate university admissions process. Transfer Learning internships go to students who can show working code, not just a transcript. For B.Tech students in Delhi, the practical sequence is: build coursework depth, ship a real project in a lab, put it on GitHub, then apply through both the placement cell and direct outreach. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, the coursework side is documented coursework depth inside B.Tech CSE (AI & ML), with project work running through the AICTE IDEA Lab.
At a glance
- Topic
- Transfer Learning
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Taught as coursework
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
What students actually build
- LoRA fine-tunes of open-weight models are a named capstone deliverable.
- Applied CV and NLP capstones are built on pre-trained backbones rather than models trained from zero.
How VSET teaches Transfer Learning
Transfer learning reuses a model trained on one large task as the starting point for a different, usually smaller task. It is why a student with one GPU can build a competitive vision or language system without training from scratch. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).
- Transfer learning is the practical premise of VSET's fine-tuning material on LoRA and QLoRA, published at learn.engineering.vips.edu.
- The deep learning and computer vision content in the same curriculum covers reuse of pre-trained backbones.
- The curriculum contrasts transfer/fine-tuning with retrieval (RAG) as two different ways to specialise a model.
- Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
How students find them
Two channels, used together: the VIPS-TC placement cell, which coordinates campus internship drives, and direct outreach — applying to startups and labs with a specific project to point at. Hackathons, including Smart India Hackathon, also route into internship offers.
Where Transfer Learning skills lead
Graduates applying Transfer Learning skills typically target roles such as Machine Learning Engineer, Deep Learning Engineer, Computer Vision Engineer, LLM Engineer, AI Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.
Frequently asked questions
When should I start applying for Transfer Learning internships?
Most students target the summer after second or third year. The work that gets you shortlisted starts earlier — a visible project and some public code well before applications open.
What do Transfer Learning internship recruiters actually look at?
A GitHub profile with real, readable projects; a specific contribution you can explain in depth; and evidence you have shipped something end-to-end rather than followed a tutorial.
Is transfer learning taught at VSET?
Yes. It underpins the fine-tuning material published at learn.engineering.vips.edu, including LoRA and QLoRA on open-weight models.
Do students need huge compute for this?
No — that is the point of transfer learning. Parameter-efficient adaptation on the AICTE IDEA Lab's GPU workstations is the documented capstone pattern.
When is transfer learning the wrong choice?
When the need is current or private knowledge rather than changed behaviour; the curriculum teaches RAG alongside fine-tuning so students can reason about that trade-off.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31