Learn Large Language Models (LLMs) with Python and LangChain

About this course

Unlock the power of Large Language Models (LLMs) and bring cutting-edge AI to your projects! This beginner-friendly yet comprehensive course takes you deep into the world of transformer-based models — from foundational architectures like BERT and RoBERTa, to generative giants like GPT and Meta’s LLaMA.But we don’t stop there.

You’ll also explore Retrieval-Augmented Generation (RAG) — one of the most powerful methods to enhance LLMs with real-time, context-aware information retrieval. Learn how RAG bridges the gap between static models and dynamic, knowledge-grounded generation — perfect for applications like chatbots, enterprise search, and AI assistants.

Whether you're a beginner Python developer or someone curious about how LLMs really work, this course will give you the theory, hands-on skills, and real-world insights to work confidently with modern AI tools.

What You’ll Learn

Section 1 - Transformersword embeddingspositional embeddings and encodingself-attention mechanismmaskingmulti-head architecturehow to train a transformer architecturetransformer architectures: GPT, BERT and LLaMASection 2 - Encoder-Only ArchitecturesBERT fundamentalspre-training and fine-tuning the modelthe [CLS] tokenBERT and RoBERTasentiment analysis, text classification and question answering with BERT

Section 3 - Decoder-Only ArchitecturesGPT and LLaMA fundamentalsreinforcement learning from human feedback (RLHF)fine-tuning decoder-only architectures

LoRA and QLoRA

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