House of Data/Generative AI Flagship
Flagship · 6 months
The full build, foundation to roof.
Six months, starting from the beginning. No assumed coding background, deeper specialisation, production LLMOps, more capstone projects and longer placement support. This is the one to take if you want the whole thing rather than the quickest route through it.
Fast Track or flagship
The honest comparison.
Same subject, same trainers, same placement cell. What changes is how much ground gets covered and how much room there is to absorb it.
| Fast Track | Flagship | |
|---|---|---|
| Length | 3 months (2 training + 1 placement) | 6 months |
| Course fee | ₹59,999 + post-placement fee | ₹74,999 + post-placement fee |
| Coding background | Some coding assumed | None needed — starts at the beginning |
| Python & SQL | 2 weeks, revision pace | 5 weeks, from scratch with practice time |
| Maths & statistics | Folded into the ML week | Its own module — probability, statistics, linear algebra |
| Machine learning | Foundations | Supervised, unsupervised, recommenders, deep learning |
| Gen AI, RAG, agents | Full coverage | Full coverage, with more build time |
| Multi-agent systems | Introduced | Built and deployed |
| Fine-tuning | — | Included |
| MLOps & LLMOps | Deployment basics | Full module — monitoring, evaluation, guardrails, cost |
| Capstone projects | 1 deployed | 4 deployed — 2 ML, 2 Gen AI |
| Placement support | Dedicated month, continues until placed | Extended, continues until placed |
| Best for | Career switchers on a deadline | Freshers, career gaps, non-IT backgrounds |
The syllabus
Six modules, in the order they are useful.
No module starts before the one under it is solid. That is the whole reason this version takes six months.
- Module 01 · 2 weeksFundamentals of data analysis Excel, beginner Python, Tableau. Where people with no technical background start, and where they stop being afraid of a dataset.
- Module 02 · 2 weeksAnalytical proficiency and business insight SQL properly, and product analytics — framing a business question so the query answers something a manager actually asked.
- Module 03 · 3 weeksFoundations of ML and deep learning Advanced Python and the libraries; probability, statistics, calculus and linear algebra; then neural networks from first principles rather than as a black box.
- Module 04 · 4 weeksSpecialisations Machine learning — supervised, unsupervised, recommenders. And deep learning — computer vision, NLP, Generative AI, RAG and AI agents. Two of your four capstones come out of this module.
- Module 05Pipeline development and deployment MLOps and LLMOps: containers, CI, monitoring, evaluation harnesses, guardrails and what it costs to run. Advanced data structures and algorithms are available as an optional track.
- Module 06 · until placedGet placed at product companies Profile building, applying the right way, and interview preparation that continues past the end of the syllabus. How this works →
What you leave with
Four deployed projects and a profile that reads like an engineer's.
4 real projects
Two machine learning, two Generative AI, all deployed to Azure, AWS or GCP on public URLs. Not notebooks — running systems you can hand an interviewer.
Certification and lifetime access
Certification on completion, lifetime access to House of Data, free webinars, and an internship opportunity while you study.
The full placement programme
Resume rewrite, Naukri and LinkedIn rebuilt, mock interviews and hiring-partner submissions — extended, and running until you are placed.
The course fee is ₹74,999 plus a separate post-placement fee payable once you are earning. Instalments are available. For the post-placement figure and any offer running when you join, talk to the team — we would rather quote you something current than publish a number that goes stale.
Not sure which one you need?
Come to a free demo class and ask. Fifteen minutes with a trainer is worth more than an hour of reading comparison tables, and nobody will push you toward the more expensive one.