Published 4/2025
Created by Holczer Balazs
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 91 Lectures ( 10h 53m ) | Size: 3.13 GB
Understand the Fundamentals of Large Language Models (LLMs) like BERT, RoBERTa, GPT, LLAMA with Python, Google Colab
What you’ll learn
large language models (LLMs) fundamentals
encoder-only transformer architectures (BERT, RoBERTa etc.)
decoder-only transformer architectures (GPT, LLaMA etc.)
transfer learning and fine-tuning
retrieval-augmented generation (RAG)
Requirements
machine learning fundamentals
Python programming fundamentals
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 LearnSection 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 BERTSection 3 – Decoder-Only ArchitecturesGPT and LLaMA fundamentalsreinforcement learning from human feedback (RLHF)fine-tuning decoder-only architecturesLoRA and QLoRAfine-tuning models on custom datasetSection 4 – Retrieval-Augmented Generation (RAG)what is RAG?semantic search and vector databasesLSH and HNSW algorithmsusing RAG with PDF filesSection 5 – Prompt Engineeringprompt engineering fundamentalszero-shot promptingfew-shot promptingchain of thoughts (CoT)prompt chaining methodsJoin 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!
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