🔥 What You'll Achieve

Build production-grade language models and NLP systems

🧠 Master Modern LLMs

Understand GPT, BERT, T5, and other state-of-the-art language models. Learn how they work and their differences.

⚙️ Fine-tune Models

Learn to fine-tune LLMs for your specific tasks. Work with HuggingFace, optimize efficiently, and deploy models.

🚀 Build NLP Applications

Create chatbots, text classifiers, summarization systems, and more. Integrate with APIs and build production systems.

📈 LLM/NLP Engineer Career Outlook

Hottest AI specialization with explosive growth

$155k+
Average LLM/NLP Engineer salary
(Levels.fyi, 2024)
629%
Growth in LLM job postings
(LinkedIn, 2022-2024)
$200k+
Senior LLM Engineer at top companies
(OpenAI, Anthropic, Google)

Prerequisites

🧠
Deep Learning Fundamentals

Neural networks, backprop, RNNs (we recommend our Deep Learning course)

📝
NLP Concepts

Tokenization, embeddings, attention (basics covered, prior knowledge helpful)

🐍
Python & Libraries

Python proficiency, PyTorch/TensorFlow experience

LLM Fundamentals

Understanding large language models and transformer architecture

Beginner

1. What Are LLMs?

Understand what large language models are, how they're trained, and why they're revolutionary.

⏱️ 20 min read
Start Learning
Beginner

2. Tokenization & Embeddings

Learn how LLMs convert text to numbers. Master tokenizers and word embeddings.

⏱️ 18 min read
Start Learning
Intermediate

3. Prompt Engineering

Master the art of prompting. Learn techniques to get better outputs from LLMs.

⏱️ 25 min read
Start Learning
Intermediate

4. In-Context Learning & RAG

Learn few-shot learning, in-context learning, and Retrieval Augmented Generation (RAG) systems.

⏱️ 28 min read
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Advanced LLM Techniques

Fine-tuning, optimization, and production deployment

Advanced

5. Fine-tuning LLMs

Learn different fine-tuning approaches: full fine-tune, LoRA, QLoRA. Optimize for speed and memory.

⏱️ 32 min read
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Advanced

6. Inference Optimization

Master quantization, distillation, and efficient inference. Deploy models at scale.

⏱️ 30 min read
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Advanced

7. Building LLM Applications

Build production LLM apps: chatbots, agents, pipelines. Integrate APIs, handle errors, monitor systems.

⏱️ 35 min read
Start Learning

Hands-on Projects

Build real-world LLM applications

Intermediate

Project 1: Fine-tune BERT for Classification

Complete end-to-end project: Fine-tune BERT for sentiment analysis on IMDb dataset. Includes training, evaluation, and FastAPI deployment.

⏱️ 2-3 hours
Start Project
Advanced

Project 2: Build a RAG Chatbot

Build a production-ready RAG system with LangChain, ChromaDB, and Gradio. Document Q&A with conversational memory and source citations.

⏱️ 2-3 hours
Start Project
Advanced

Project 3: Deploy a Fine-tuned LLM

Deploy LLMs at scale with vLLM, quantization, Docker, and Kubernetes. Includes monitoring, cost optimization, and production best practices.

⏱️ 2-3 hours
Start Project

💡 Continue Your Learning Journey

Explore more courses to expand your AI and programming skills

🧠

Deep Learning

Master neural networks, CNNs, RNNs, and Transformers

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🤖

Machine Learning

Learn classification, regression, and advanced ML algorithms

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🔬

AI Agents

Build autonomous AI systems and intelligent agents

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