Jupyter Notebooks projects for B.Tech students — real examples

What students actually build

• Data and machine learning capstone projects at VSET are usually explored in notebooks before being written as application code.
• Notebooks double as a record of what a student tried, which helps at capstone evaluation.

Labs and infrastructure

• Campus computing labs run notebook environments for data and machine learning practicals.
• IDEA Lab GPU workstations are used for notebook work that needs real training compute.

How VSET teaches Jupyter Notebooks

Jupyter notebooks combine code, output, plots and written explanation in one document, which makes them the standard working environment for data exploration, model experiments and teaching. They are used across the data and machine learning coursework of the CSE AI specialisations at VSET. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & Data Science).

• Data and machine learning coursework in the CSE AI and Data Science specialisation at VSET uses Python notebooks as the working environment.
• The CSE AI and ML specialisation uses the same environment for model experiments.
• Python programming coursework across the CSE family is the prerequisite.
• Notebooks are a working tool across that coursework rather than a standalone GGSIPU subject.