Learn Large Language Models (LLMs) with Python and LangChain

Learn Large Language Models (LLMs) with Python and LangChain free download

Understand the Fundamentals of Large Language Models (LLMs) like BERT, RoBERTa, GPT, LLAMA with Python, Google Colab

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 - Transformers

• word embeddings

• positional embeddings and encoding

• self-attention mechanism

• masking

• multi-head architecture

• how to train a transformer architecture

• transformer architectures: GPT, BERT and LLaMA

Section 2 - Encoder-Only Architectures

• BERT fundamentals

• pre-training and fine-tuning the model

• the [CLS] token

• BERT and RoBERTa

• sentiment analysis, text classification and question answering with BERT

Section 3 - Decoder-Only Architectures

• GPT and LLaMA fundamentals

• reinforcement learning from human feedback (RLHF)

• fine-tuning decoder-only architectures

• LoRA and QLoRA

• fine-tuning models on custom dataset

Section 4 - Retrieval-Augmented Generation (RAG)

• what is RAG?

• semantic search and vector databases

• LSH and HNSW algorithms

• using RAG with PDF files

Section 5 - Prompt Engineering

• prompt engineering fundamentals

• zero-shot prompting

• few-shot prompting

• chain of thoughts (CoT)

• prompt chaining methods

Join the course today and start your journey into the world of Large Language Models and Retrieval-Augmented Generation. Whether you're building smarter apps, enhancing your AI knowledge, or simply exploring the future of language technology — this course will give you the tools and confidence to level up.

Enroll now and start building with the AI models shaping the future. Let's get learning!