Learn Large Language Models (LLMs) with Python and LangChain

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

Preview this Course

GET COUPON CODE

Description

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!

Who this course is for:

• Beginner Python developers who are curious about generative AI and large language models (LLMs)