Ten months of live Applied AI curriculum covering Machine Learning, Data Science and Generative AI, delivered completely remote, followed by a 2-month guaranteed industry internship with a host company. Designed for second and third year students in four-year programmes — BCA, BTech and equivalent.
The programme runs for twelve months in total. The first ten months are structured Applied AI courseware delivered live and remotely. The final two months are an industry internship at a host company, with a company-issued internship certificate on completion.
Modules are sequenced so each builds on the previous. Python and mathematics precede ML; ML and deep learning precede GenAI. The capstone in month ten integrates two or more modules into a single deployed project.
Proficiency in Python and the core data manipulation libraries is a prerequisite for every subsequent module. This module covers both at working level. Students who already have Python experience move through this module faster; sessions are structured to accommodate varied starting points.
Covers the linear algebra, probability, statistics and calculus concepts that underpin ML algorithms. Treatment is applied rather than proof-based — the goal is working understanding of why models behave as they do, not mathematical derivation. Students completing this module can interpret loss functions, read gradient descent diagrams and reason about model optimisation.
Classical supervised and unsupervised ML at working depth — covering algorithm selection, implementation, evaluation and diagnosis. Students work through each algorithm with explicit attention to failure modes, not only successful training runs. scikit-learn Pipelines are introduced as the standard for reproducible, production-oriented ML code.
Covers neural network architecture, training dynamics, and the transformer model — the architecture underlying all current large language models. Transfer learning is covered at applied level: students fine-tune pre-trained models from Hugging Face on custom datasets. Production-level training infrastructure (distributed training, MLOps tooling) is out of scope at this level and covered in the IIP.
Covers statistical inference, experimental design, SQL at working level, and business intelligence tooling. Students completing this module can frame a business question as an analytical problem, query a relational database, run and interpret an A/B test, and produce a Power BI dashboard that a non-technical stakeholder can use.
Covers LLM architecture, prompt engineering, and GenAI application development. Students work through commercial LLM APIs, build a functional RAG pipeline, and implement a basic AI agent using the ReAct pattern. Fine-tuning is covered at awareness level — the decision framework for when to fine-tune versus prompt-engineer versus RAG — with hands-on fine-tuning reserved for the IIP. Students complete this module able to build and deploy a functional LLM-powered application using current tooling.
On completing the ten-month courseware and the capstone project, students are placed with a Genvarsity host company for two months of project work. The internship certificate is issued by the host company, not by Genvarsity, and constitutes a verifiable record of employment-equivalent engagement.
Students who complete the full twelve months — ten months courseware and two months internship — receive two separate documents, issued independently by Genvarsity and the host company respectively.
Speak with a Genvarsity counsellor to discuss programme fit, academic scheduling and the admission process.
Talk to a Counsellor