Intro to Large Language Models (LLMs) Course Syllabus

An essential course for understanding how Large Language Models work and their growing impact in the AI ecosystem. This beginner-friendly program spans approximately 5 hours of on-demand video content, structured across six focused modules. You'll gain a solid foundation in LLM concepts, architecture, training methods, real-world applications, and ethical considerations—ideal for tech professionals looking to build foundational AI knowledge. No prior experience with deep learning is required, making it accessible to a broad audience.

Module 1: Introduction to LLMs

Estimated time: 0.5 hours

• What are Large Language Models and why they matter
• Evolution from NLP to modern LLMs
• Key milestones in LLM development
• Overview of popular LLMs like GPT

Module 2: Architecture and Core Concepts

Estimated time: 0.75 hours

• Transformer architecture fundamentals
• Attention mechanisms explained
• Tokenization and text processing
• Understanding embeddings and model sizes

Module 3: Training and Fine-Tuning LLMs

Estimated time: 1 hour

• Pre-training vs. fine-tuning explained
• Role of large datasets in training
• Hyperparameters and model performance
• Challenges in training large models

Module 4: Using LLMs in Real-World Applications

Estimated time: 1 hour

• Content generation and summarization
• Language translation using LLMs
• Code generation and programming assistance
• Integrating LLMs into business workflows

Module 5: Limitations, Ethics & Safety

Estimated time: 0.75 hours

• Bias in language models
• Mitigation strategies for fairness
• Safety and misuse concerns
• Responsible deployment practices

Module 6: Future Trends in LLMs

Estimated time: 0.5 hours

• Open-source vs. proprietary models
• Emergence of multimodal LLMs
• Career paths and skills for LLM work
• Next wave of LLM development

Prerequisites

• Familiarity with basic AI concepts
• No coding experience required
• Interest in artificial intelligence and technology trends

What You'll Be Able to Do After

• Explain how Large Language Models work
• Describe the transformer architecture and attention mechanisms
• Understand the training and fine-tuning process of LLMs
• Identify real-world applications of LLMs across industries
• Recognize ethical issues and best practices in LLM deployment