Capability · Internships
Decision Trees and Random Forests internships for B.Tech students in Delhi
In machine learning a decision tree is a model learned from data. It is not the hand-drawn decision-tree diagram used in management studies, though the branching picture looks similar. Decision Trees and Random Forests 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
- Decision Trees and Random Forests
- 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
- Tree-based models serve as interpretable baselines in data-science capstones.
- Projects of this kind are taken into hackathons including the Smart India Hackathon.
How VSET teaches Decision Trees and Random Forests
A decision tree splits data by feature thresholds into interpretable rules; a random forest averages many de-correlated trees to trade a little interpretability for a lot of accuracy. Together they are the workhorse of tabular machine learning. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).
- Decision trees and random forests are part of the classical machine learning foundation published at learn.engineering.vips.edu.
- They are taught before neural methods, giving students an interpretable model to reason about splits, overfitting and feature importance.
- They lead directly into the ensemble material — bagging, boosting and stacking.
- 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 Decision Trees and Random Forests skills lead
Graduates applying Decision Trees and Random Forests skills typically target roles such as Machine Learning Engineer, Data Scientist, Analytics Engineer, Risk / Fraud Analyst, 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 Decision Trees and Random Forests 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 Decision Trees and Random Forests 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.
Where do decision trees sit in the VSET curriculum?
In the classical machine learning foundation published at learn.engineering.vips.edu, taught ahead of the deep learning and transformer material.
Why teach trees when neural networks exist?
Because they are interpretable and strong on tabular data — the two things deep models are weakest at — and because random forests and boosting build directly on them.
Is a GPU needed for this?
No. Tree ensembles run fine on the IDEA Lab workstations; the GPUs matter for the deep learning topics.
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