Free Download AI Engineering & LLM MasterClass
Published 5/2026
Created by Ganesh Ravikumar
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 73 Lectures ( 3h 56m ) | Size: 1.6 GB
LangChain • RAG • AI Agents • Fine-Tuning • Vector DBs • LLMOps • OpenAI • HuggingFace - Real Projects, Real Deployment
What you'll learn
Every section has a real deployable project Goes beyond ChatGPT wrappers into actual engineering Covers what companies actually hire for - RAG, Agents, Fine-tuning, LLMOps Includes evaluation, cost optimisation, and responsible AI 2026-current tools and frameworks onlyRequirements
No AI/ML background needed - we build from the ground upDescription
This course contains the use of artificial intelligence.
AI tools were used to assist with script drafting, slide structure, and voiceover narration. Every lesson was reviewed, edited, and validated by the instructor based on real production experience in enterprise AI and data engineering systems.
AI Engineering & LLM Masterclass (Pro)
I built this course because I couldn't find the one I actually needed when I started working with LLMs in production.
Most courses show you how to call the OpenAI API and stop there. They don't tell you what happens when your JSON parsing breaks at 2am because the model decided to add a friendly introduction before the output. They don't show you how to build a RAG system that actually retrieves the right document - not just a semantically close one. They don't explain why your agent works perfectly in testing and fails silently in production.
This course covers all of that. Every section is built around real engineering problems from supply chain and warehouse operations - environments where getting the answer wrong has real consequences. The code is production-grade. The explanations are honest. The war stories are real.
You will learn prompt engineering properly - not just zero-shot and few-shot, but structured output, injection defences, versioning, and testing. You will build complete RAG pipelines from scratch, implement vector databases, and deploy AI agents that can reason, use tools, and recover from failures. You will understand fine-tuning well enough to know when it is the right decision and when it is not.
By the end, you will have built systems - not just completed exercises. That is the difference between a course and a masterclass.
If you are a developer who wants to do serious AI engineering work, this is where you start.
Who this course is for
Tech leads and architects evaluating AI system design ML engineers levelling up from classical ML to LLMs Python developers who want to enter AI Engineering Software engineers adding AI to their existing stackHomepage
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