Comprehensive Curriculum to Master Large Language Models

A Structured Path with Theoretical Insights and Practical Labs

Key Takeaways

• Structured Learning Phases: The curriculum is divided into foundational, intermediate, and advanced phases to ensure progressive mastery.
• Hands-On Experience: Each phase incorporates practical labs and projects using industry-standard tools and platforms.
• Continuous Learning: Emphasis on staying updated with the latest trends and engaging with the LLM community.

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Introduction to Large Language Models

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by enabling machines to understand and generate human-like text. Becoming an expert in LLMs requires a solid foundation in NLP principles, understanding of model architectures, hands-on experience with model training and deployment, and continuous engagement with the evolving landscape of AI technologies.

Curriculum Overview

This curriculum is meticulously designed to guide you from the basics to advanced applications of LLMs over the course of approximately one year, dedicating 5 hours per week. It integrates theoretical knowledge with practical labs, ensuring a balanced and comprehensive learning experience.

Phase 1: Foundations of LLMs (Weeks 1–12)

Objective: Build a strong theoretical and practical foundation in LLMs, NLP, and AI.

Weeks 1-4: Introduction to LLMs and NLP

Topics to Study:

• Basics of Natural Language Processing (NLP)
• Evolution from traditional language models to Transformers
• Fundamentals of Large Language Model architectures
• Introduction to transformer architecture, attention mechanisms, and tokenization

Hands-On Labs/Tools:

• Complete introductory modules from courses such as Generative AI with Large Language Models - Coursera.
• Experiment with simple text generation using OpenAI's GPT or Hugging Face's Transformers library.
• Practice tokenization and word embeddings using Python libraries like spaCy and NLTK.
• Build a simple language model using Python and Google Colab.

Recommended Resources:

• Getting Started with Large Language Models (LLMs) - Skillsoft
• H2O AI Large Language Models (LLMs) - Level 1 - Coursera

Weeks 5-8: LLM Architecture and Training

Topics to Study:

• Deep dive into transformer architecture and attention mechanisms
• Understanding tokenization and embeddings in detail
• Basics of training and fine-tuning LLMs
• Introduction to model quantization and optimization techniques

Hands-On Labs/Tools:

• Use Hugging Face to load and fine-tune a pre-trained model like GPT-2 or BERT on a small dataset.
• Build a simple user interface for your model using Gradio.
• Experiment with prompt engineering using OpenAI's Playground or Hugging Face's Spaces.
• Quantize a pre-trained model using Hugging Face or PyTorch and measure performance trade-offs.

Recommended Resources:

• Gradio Course by freeCodeCamp
• Large Language Model Operations (LLMOps) Specialization - Coursera

Weeks 9-12: Fine-Tuning and Evaluation

Topics to Study:

• Advanced fine-tuning techniques including transfer learning and domain adaptation
• Evaluation metrics for LLMs: BLEU, ROUGE, perplexity
• Understanding LLM security risks and ethical considerations

Hands-On Labs/Tools:

• Complete the fine-tuning project from the Mastering LLMs for Developers & Data Scientists course.
• Evaluate your fine-tuned model using Hugging Face's evaluation tools.
• Secure your LLM by completing the SEC545: GenAI and LLM Application Security course.

Recommended Resources:

• SEC545: GenAI and LLM Application Security - SANS Institute

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Phase 2: Advanced Topics and Applications (Weeks 13–30)

Objective: Dive deeper into advanced LLM concepts and real-world applications.

Weeks 13-16: Intermediate Development

Topics to Study:

• Fine-tuning LLMs on domain-specific data
• Few-shot learning and advanced prompt engineering
• Ethical concerns and bias in LLMs
• Deployment strategies: cloud vs. on-premises

Hands-On Labs/Tools:

• Fine-tune open-source models like GPT-Neo or Bloom using Hugging Face.
• Build a chatbot with open-source LLMs and deploy it using AWS Sagemaker or Google Cloud AI.
• Create applications using OpenAI’s API or other interfaces for prompt engineering.

Recommended Resources:

• Databricks Expert-Led LLM Courses on edX
• Large Language Model Operations Specialization - Coursera

Weeks 17-22: Advanced Fine-Tuning and Custom Datasets

Topics to Study:

• Advanced fine-tuning techniques: LoRA, RLHF (Reinforcement Learning with Human Feedback)
• Data engineering for LLMs: dataset curation and preprocessing pipelines
• Exploring Retrieval-Augmented Generation (RAG)
• Integrating LLMs with tools like LangChain and LlamaIndex

Hands-On Labs/Tools:

• Fine-tune models using custom datasets such as legal documents or medical texts.
• Implement RAG applications to enhance model performance.
• Build and integrate LLM-powered agents with external APIs or databases.

Recommended Resources:

• Large Language Model Operations Specialization - Coursera
• LLM Courses - Hamel.dev

Weeks 23-30: LLM Security and Ethical Considerations

Topics to Study:

• Security risks in LLMs: prompt injection, data leakage
• Ethical deployment of LLMs
• Advanced model optimization: quantization and pruning techniques
• Scalable systems for serving LLMs to millions of users

Hands-On Labs/Tools:

• Secure your LLM implementations by applying best practices learned in SEC545.
• Optimize models using DeepSpeed or TensorFlow for distributed training.
• Deploy scalable LLM solutions on cloud platforms like AWS, Azure, or Google Cloud.

Recommended Resources:

• Training & Fine-Tuning LLMs for Production - Activeloop
• Free Resources to Learn LLMs - Roadmap.sh

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Phase 3: Building and Deploying Real-World Applications (Weeks 31–52)

Objective: Develop and deploy comprehensive LLM-based applications, solidifying expertise through practical projects.

Weeks 31-40: Advanced Applications and Deployment

Topics to Study:

• Designing and deploying LLM-based applications
• Model evaluation and testing in production environments
• Best practices for LLMOps and continuous integration/continuous deployment (CI/CD) for AI models

Hands-On Labs/Tools:

• Build a chatbot or document summarization tool using LangChain and Gradio.
• Deploy applications on cloud platforms like AWS, GCP, or Hugging Face Spaces.
• Implement CI/CD pipelines for continuous model updates and deployments.

Recommended Resources:

• LLM Bootcamp - Data Science Dojo
• The Large Language Model Course - Towards Data Science

Weeks 41-52: Capstone Projects and Community Engagement

Topics to Study:

• Building end-to-end LLM solutions for specific domains
• Engaging with the LLM community through forums, hackathons, and open-source projects
• Staying updated with the latest LLM trends and research developments

Hands-On Labs/Tools:

• Develop a comprehensive LLM-based project, such as a legal document analyzer or a customer support chatbot.
• Present your project to peers, share it on GitHub, and seek feedback.
• Participate in hackathons or contribute to open-source LLM projects to enhance collaborative skills.

Recommended Resources:

• Large Language Model Operations Specialization - Coursera
• Awesome LLM Courses - GitHub

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Daily/Weekly Schedule Plan

Weekly Breakdown

Weeks	Theory (Hours)	Hands-On Labs (Hours)	Activities
1-12	2	3	Foundation learning and initial projects
13-30	1.5	3.5	Advanced topics, fine-tuning, and optimization
31-52	1	4	Capstone projects and deployment

Useful Tools and Libraries

• Libraries: Hugging Face Transformers, PyTorch, TensorFlow, DeepSpeed, spaCy, NLTK
• Cloud Platforms: AWS Sagemaker, Microsoft Azure, Google Cloud AI, Hugging Face Spaces
• Visualization Tools: TensorBoard, Weights & Biases
• Deployment Tools: Gradio, LangChain, LlamaIndex
• Optimization Frameworks: DeepSpeed, TensorFlow Optimization Toolkit

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Staying Updated and Community Engagement

LLM technology is rapidly evolving. To maintain expertise, it's essential to stay informed about the latest research, tools, and best practices. Engaging with the community through forums, blogs, and collaborative projects enhances learning and keeps you connected with industry developments.

Recommended Practices

• Join LLM-focused communities such as Reddit's LocalLLaMA and Hugging Face forums.
• Subscribe to newsletters like Alpha Signal and ThursdAI to receive updates on LLM advancements.
• Follow influential AI researchers and practitioners on platforms like LinkedIn and Twitter.
• Participate in hackathons, webinars, and online workshops to collaborate and learn from peers.

Key Resources for Continuous Learning

• Startup Guides and Tutorials
• Awesome LLM Courses - GitHub
• Free Resources to Learn LLMs - Roadmap.sh

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Recap and Conclusion

Mastering Large Language Models requires a blend of theoretical understanding and practical application. This comprehensive curriculum offers a structured path, balancing foundational learning with advanced topics and hands-on projects. By dedicating consistent effort and engaging with the LLM community, you can achieve expertise and contribute meaningfully to the field of Natural Language Processing.

References

• Generative AI with Large Language Models - Coursera
• Start with Large Language Models (LLMs) - GitHub Guide
• Large Language Model Operations Specialization - Coursera
• LLM Courses - Various Providers
• SEC545: GenAI and LLM Application Security - SANS Institute
• Databricks Expert-Led LLM Courses on edX
• LLM Bootcamp - Data Science Dojo
• The Large Language Model Course - Towards Data Science
• Maxime Labonne's LLM Course 2025 Edition - LinkedIn