Large Language Models Course: Learn LLMs from Basics to Practical Applications
Learn how Large Language Models work from first principles. Understand tokens, tokenization, embeddings, transformers, attention, training, fine-tuning, and how to build applications using LLM APIs. Complete beginner to intermediate course with practical projects.
What's Inside Every Lesson?
Every topic is designed so complete beginners can follow along step by step
Interactive Code Editor
Write & run real code in the browser - no setup needed
Practice After Every Topic
Hands-on exercises reinforce what you just learned
Q&A Flip Cards
Common questions answered with simple explanations
Module Quizzes
Test your knowledge before moving to the next module
Mini Projects
Build real apps - a calculator, to-do list, and more
Visual Diagrams
Concepts explained with clear, annotated diagrams
Who This Course Is For
Designed for students, developers, and working professionals who want to understand how Large Language Models work and how to build applications around them. Covers LLMs from fundamentals to practical API use.
Prerequisites
No prior LLM knowledge required. Completing the Artificial Intelligence or Generative AI course first is recommended but not required. Basic Python familiarity is helpful for the API sections.
First published September 2024 · Updated 2026
What You'll Learn
- What a Large Language Model is and how it differs from other AI systems
- How LLMs represent text as tokens and numbers
- What a context window is and why it matters
- How next-token prediction works during training and inference
- What embeddings are and how they represent meaning
- How the transformer architecture and attention mechanism work
- How LLMs generate text token by token using temperature and sampling
- The difference between pretraining, fine-tuning, and instruction tuning
- How to integrate LLM capabilities using APIs in Python
- What Retrieval-Augmented Generation is and how it works
- What AI agents are and how they use tools
- How to evaluate LLM responses for accuracy, relevance, and consistency
- Why hallucinations happen and how to reduce their impact
- How to design secure and responsible LLM applications
- How to build a complete simple LLM application from planning to deployment
Career Opportunities
Course Modules Overview
Introduction to Large Language Models
12 topics
Language, Tokens and Text
12 topics
How Language Models Learn
12 topics
Embeddings and Representations
12 topics
Transformers and Attention
12 topics
How LLMs Generate Text
12 topics
Training, Fine-Tuning and Alignment
13 topics
Using LLM APIs
13 topics
LLM Applications: RAG, Search and Agents
13 topics
LLM Evaluation, Hallucinations and Limitations
13 topics
LLM Security, Privacy and Responsible AI
13 topics
Practical LLM Projects and Career Path
13 topics
Complete all 12 modules to unlock your course completion certificate
Course Curriculum
12 comprehensive modules covering everything from basics to advanced topics
Introduction to Large Language Models
Understand what a Large Language Model is, why LLMs became important, how they differ from earlier AI systems, and where they are used in everyday life.
Language, Tokens and Text
Learn how language models represent text as tokens and numbers, what a vocabulary is, and how context windows work.
How Language Models Learn
Discover how language models learn from large amounts of text, what next-token prediction means, and how training improves model predictions.
Embeddings and Representations
Understand embeddings: how text is converted to numbers that capture meaning, and how semantic similarity makes search and AI applications smarter.
Transformers and Attention
Explore the transformer architecture that powers modern LLMs. Learn what attention is, how self-attention works, and why transformers became the foundation of today's AI.
How LLMs Generate Text
Learn exactly how an LLM turns your prompt into a response. Understand token probabilities, temperature, and how the model generates one token at a time.
Training, Fine-Tuning and Alignment
Understand the full lifecycle from pretraining to fine-tuning. Learn about instruction tuning, RLHF, and LoRA without needing to train a model yourself.
Using LLM APIs
Learn how to integrate LLM capabilities into applications using APIs. Write your first API request in Python and learn API security best practices.
LLM Applications: RAG, Search and Agents
Explore Retrieval-Augmented Generation and AI agents. Understand how to give LLMs access to external information and how agents use tools to complete tasks.
LLM Evaluation, Hallucinations and Limitations
Learn to evaluate LLM outputs for accuracy, relevance, and consistency. Understand why hallucinations happen, what bias means in AI, and how to apply human review.
LLM Security, Privacy and Responsible AI
Understand LLM security risks including prompt injection and data leakage. Learn responsible AI principles and how to design safer LLM applications.
Practical LLM Projects and Career Path
Apply everything you have learned to plan, build, test, and evaluate a simple LLM application. Explore LLM career skills and the path forward.
Your Learning Roadmap
Follow this structured path - from first concepts to production-ready mastery
Understand what LLMs are, how they represent text as tokens, and how they learn from training data
Explore transformers, attention, text generation, fine-tuning, and how to use LLM APIs
Build LLM applications with RAG, evaluate model outputs, and apply responsible AI principles
Understand what LLMs are, how they represent text as tokens, and how they learn from training data
Explore transformers, attention, text generation, fine-tuning, and how to use LLM APIs
Build LLM applications with RAG, evaluate model outputs, and apply responsible AI principles
Tools & Technologies
Essential tools you'll master during this course
Python
Used for LLM API integration and practical code examples
OpenAI API
API access to GPT models for building LLM applications
Google Gemini
Google AI models accessible via API for text generation
ChatGPT
Conversational interface for exploring LLM capabilities
Hugging Face
Repository of open-source models and tools for NLP
LangChain
Framework for building LLM-powered applications and agents
Ready to Start Learning?
Begin your journey with Module 1 and build your skills step by step. Completely free, no registration required.
Start Learning Large Language Models Free