NLP & LLM Engineering with RAG
কোর্স সম্পর্কে
এই কোর্সে আপনি যা শিখবেন
- Modern NLP workflow ও LLM-based text processing- Large Language Models কীভাবে কাজ করে এবং কোথায় ব্যবহার হয়
- Embeddings, Vector Search এবং Retrieval-Augmented Generation (RAG)
- LLM + External Data ব্যবহার করে intelligent Q&A system তৈরি
- RAG pipeline design, testing এবং improvement techniques
এই কোর্স করে যেভাবে উপকৃত হবেন -
- NLP থেকে LLM Engineering-এ transition করার clear understanding পাবেন- নিজের ডেটার উপর LLM ব্যবহার করে practical AI solution বানাতে পারবেন
- Real-world RAG use-case implement করার confidence তৈরি হবে
- AI Engineer / LLM Engineer role-এর জন্য প্রয়োজনীয় applied skills অর্জন করবেন
Prerequisite:
- Python জানা থাকতে হবে
- Machine Learning-এর ধারণা থাকতে হবে
কোর্স কনটেন্ট
ইন্সট্রাক্টর
Tahmid Rahman
Tahmid Rahman is a highly respected AI practitioner and mentor, known for building production-grade machine learning systems and solving complex real-world problems across multiple industries. As the AI Team Lead at iBusinessFormula and former AI Engineer at Brain Station 23, he brings a rare blend of technical depth, leadership, and practical field experience—making him uniquely qualified to guide students through modern AI engineering.
Professional Experience
AI Team Lead at iBusinessFormula
Tahmid leads end-to-end AI solution development, from problem discovery to deployment. He oversees model architecture design, optimization, prompt engineering workflows, LLM integration, and scalable MLOps pipelines. His leadership ensures that AI systems not only work in theory but operate reliably in production environments. He regularly mentors junior engineers, conducts code reviews, and drives innovation within the AI team.
Software Engineer at Brain Station 23
At Brain Station 23, Tahmid plays a central role in developing AI-powered systems with a sharp focus on practical impact. His work revolves around designing AI agents that simulate human-level reasoning, solve user-specific tasks, and automate client workflows using advanced techniques.
His core responsibilities include:
Designing and deploying AI agents that handle dynamic user interactions, adapt over time, and intelligently make decisions based on context and intent.
Leading LLM integration pipelines (like OpenAI, Cohere, or custom-trained models) with LangChain, RAG (Retrieval-Augmented Generation), and memory-enabled agents.
Building tools for autonomous agents that carry out sequential tasks, connect to APIs, retrieve documents, extract structured data, and provide accurate, context-aware responses.
Collaborating with cross-functional teams to translate client needs into intelligent assistant systems that improve support, analytics, and automation.
He consistently solves enterprise-grade AI challenges—from prompt-tuning for reliability to implementing long-term memory in conversational agents.
Tahmid’s AI journey is not confined to labs or theory—he builds real-world, production-ready agents that are being used by companies to automate processes, enhance customer service, and extract insights from unstructured data.
Whether it's an LLM-powered customer support bot, a document retrieval and summarization assistant, or a task automation agent with multi-step reasoning, Tahmid has solved these problems hands-on.
He doesn't just teach how agents work—he teaches how to make them useful, efficient, and scalable.
Students under his guidance will learn:
How to go from zero to building deployable AI agents
How to architect systems with tools like LangChain, vector databases, and RAG
How to ensure reliability, reduce hallucination, and scale agent performance
With a problem-solving mindset, solid teamwork, and deep AI experience, Tahmid is not just a teacher—he’s a technical leader who will help students confidently build the future of autonomous AI systems.
NLP & LLM Engineering with RAG
আপনি যদি অলরেডি পাইথন প্রোগ্রামিং জানেন এবং মেশিন লার্নিং এর কনসেপ্টগুলো বুঝেন, তাহলে NLP ও LLM শেখার জার্নি স্টার্ট করুন এই প্রিরেকর্ডেড কোর্সের সাথে।
কোর্স সম্পর্কে
এই কোর্সে আপনি যা শিখবেন
- Modern NLP workflow ও LLM-based text processing- Large Language Models কীভাবে কাজ করে এবং কোথায় ব্যবহার হয়
- Embeddings, Vector Search এবং Retrieval-Augmented Generation (RAG)
- LLM + External Data ব্যবহার করে intelligent Q&A system তৈরি
- RAG pipeline design, testing এবং improvement techniques
এই কোর্স করে যেভাবে উপকৃত হবেন -
- NLP থেকে LLM Engineering-এ transition করার clear understanding পাবেন- নিজের ডেটার উপর LLM ব্যবহার করে practical AI solution বানাতে পারবেন
- Real-world RAG use-case implement করার confidence তৈরি হবে
- AI Engineer / LLM Engineer role-এর জন্য প্রয়োজনীয় applied skills অর্জন করবেন
Prerequisite:
- Python জানা থাকতে হবে
- Machine Learning-এর ধারণা থাকতে হবে
কোর্স কনটেন্ট
ইন্সট্রাক্টর
প্রোমো কোড EIDERKHUSHI ৩৬% ডিসকাউন্ট, আর বাকি ২ দিন !