Build production-grade
Agentic AI that ships.

Yes, completely. You can join with zero coding or AI background — we start from absolute scratch, including Python, and build up step by step until you're shipping production-grade agents. Every module is built on the previous one, so you're never dropped into a topic without the foundation for it. After every video there's a quick quiz, so you'll know immediately whether a concept landed or you just nodded along. And you're never stuck alone — post a doubt and we clear it within 24 hours.

My YouTube channel and the free 26-week roadmap will always stay free, and they're great for understanding concepts. But YouTube is one-way — I can't check whether you actually built anything, clear your doubts, or hold you to a sequence, which is why most people jump between videos and get stuck at the "I understand it but can't build it" stage. The course fixes exactly that: one structured path with quizzes, assignments, capstone projects, and one real product you keep upgrading from the first module to the last. Add doubt support within 24 hours, weekly live sessions, and biweekly updates. Simply put, YouTube tells you what to learn; the course makes sure you actually build it.

We haven't announced the price yet — we'll be revealing it in the first week of August. What I can tell you now: we're deliberately keeping it affordable compared to market alternatives, because I want serious students to be able to join, not just those with deep pockets. Whatever the number is, it covers everything — all modules, quizzes, assignments, capstones, doubt support within 24 hours, weekly live sessions, mock interviews, resume prep, and biweekly updates. And if you want to start today without spending anything, the free 26-week roadmap on this site is always open.

None. You don't need Python, an AI background, a CS degree, or work experience — the course starts from absolute scratch and takes you all the way to production level. Python itself is taught inside the course, so "I don't know coding" is not a reason to wait. I've mentored students from every branch, non-IT folks, and working professionals switching tracks, and the path works the same for all of them. The only thing I can't supply is discipline — bring daily consistency, and everything else is covered inside.

The biggest one is the structure: every module is built on top of the previous module, so the course flows as one continuous journey, and you carry one real product through it, upgrading it as your skills grow. After every video there's a quiz to check you actually understood rather than just watched. Every module has multiple assignments designed like real engineering tasks — concrete inputs, a clear deliverable, and "done when" criteria. Across the course you'll complete 3 capstone projects and 2 personal projects, so you finish with working systems you can demo in an interview, not empty repos. Around all of this sits the support layer: doubts cleared within 24 hours, weekly live sessions, mock interviews, resume prep, and biweekly updates.

Very much — a large chunk of my mentees are working professionals, and the course is built with high-demand roles like Forward Deployed Engineer (FDE) in mind. The gap I keep seeing with experienced folks: they can code and they've played with ChatGPT, but they've never taken an agent to production. That last mile — evaluation, observability, guardrails, memory, cost control, deployment — is exactly what companies are hiring for, and it's what most tutorials skip. The course spends serious time there instead of stopping at "build a chatbot in 10 minutes" demos. If you already ship software, this is the fastest route from "I use AI tools" to "I build AI systems."

No — you don't need to have trained a single ML model to become a strong agentic AI engineer. Classical ML, the math-heavy model-training track, is a different lane from what we do here. This course is about engineering with large language models: prompting them properly, giving them tools and memory, grounding them with RAG, and shipping all of it reliably. We build your LLM mental model from scratch in the early modules, so words like "token" and "context window" stop being intimidating very quickly. If you do have ML experience it won't hurt, but curiosity and consistency matter far more here than any background.

Yes — everything we build on screen, you get. Every lesson ships with its complete code, so you're never pausing a video to retype things from my editor. Assignments, notes, and resource guides are collected in one place instead of scattered across video descriptions, and since the course is updated biweekly, the code stays current too. One request though: don't just download and hoard. Run the code, break it, rebuild it — that's where the real learning happens.

Post your doubt and we'll clear it within 24 hours — that's the promise. On top of that, we run weekly live doubt-clearing sessions where we take up the trickier questions, share screens, and debug together. You're also learning alongside a community working through the same modules, so you're never stuck alone at 1 AM wondering if you're the only one confused. One suggestion: spend 15–20 minutes genuinely trying to solve it yourself first, then ask with what you tried — that struggle is where debugging skill gets built.

Yes, you'll receive a certificate once you complete the course. But I tell this to every student honestly: the certificate is not what gets you hired. Recruiters care about what you can build and how you explain it — your capstones, your GitHub, your ability to reason about why an agent failed and how you fixed it. The certificate is proof you finished something end to end, which does say something about your consistency. Just don't join for the certificate — join for the skills, and treat the certificate as the receipt.

Honest answer, not the marketing one: nobody can guarantee you a job, and you should be suspicious of anyone who does. What I can tell you is that the skills in this course — RAG, agents, MCP, evaluation, deployment — are exactly what the market is hiring for right now, and there's a real shortage of people who can do this work at a production level. We also prepare you for the hiring process itself: mock interviews so the real one isn't your first attempt, and resume prep so your projects are presented the way recruiters actually read them. Your capstones and personal projects give you real, demo-able work to talk about instead of just "I completed a course." The rest depends on you — my job is to make sure that when the interview comes, you're ready to crack it.

It has to be — AI moves at a pace where a six-month-old tutorial can already feel dated. So we update the course biweekly: refreshing lessons, swapping in current tools and model versions, and adding new content when something genuinely important lands, not for every shiny launch on social media. There's a balance I'm careful about, though: tools churn, but the underlying concepts — how agents reason, how RAG works, how you evaluate and deploy — transfer across whatever framework is trending. So the course is anchored in fundamentals that last, and the updates keep the tools and code you practice on current. You put in the learning once; the course keeps pace with the ecosystem for you.