How to learn Support Vector Machines after 12th in Delhi

The degree route

The degree route is a B.Tech with genuine Support Vector Machines depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means documented coursework depth inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.

How VSET teaches Support Vector Machines

A support vector machine finds the decision boundary with the widest margin between classes, and uses kernels to draw non-linear boundaries without explicitly building high-dimensional features. It is the classical benchmark for small, clean datasets. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• SVMs are part of the classical machine learning foundation published at learn.engineering.vips.edu.
• The kernel trick taught here is a first, concrete encounter with implicit high-dimensional feature spaces — useful background for the embedding material later in the curriculum.
• They are taught alongside the optimisation content, since training an SVM is a constrained optimisation problem.
• Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

Where Support Vector Machines skills lead

Graduates applying Support Vector Machines skills typically target roles such as Machine Learning Engineer, Data Scientist, Applied ML Researcher, AI Engineer, Research Associate. 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.