Understanding Large Language Models: A Practical Learning Path

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Understanding Large Language Models: A Practical Learning Path

Recently, I have been spending time exploring Artificial Intelligence and Large Language Models (LLMs).

There are countless videos, tutorials, and courses available today. While this abundance of material is exciting, it can also be overwhelming when trying to understand where to begin and how to learn systematically.

Among the many resources I came across, the following video resonated with me the most because it provides a clear and structured roadmap for learning LLMs properly.

🎥 Video: Learning Path for Large Language Models

🔗 Video Link:
https://www.youtube.com/watch?v=U07MHi4Suj8

Why This Video Stood Out

Many people today are using tools like ChatGPT, Claude, Gemini, and other AI systems, but understanding how these systems actually work requires a deeper learning path.

This video explains how to truly understand LLMs, not just how to use them.

It answers questions many learners struggle with:

  • Should you start with prompt engineering?

  • Do you need to understand transformers first?

  • When should you learn fine-tuning or RAG?

  • What about AI agents?

Instead of random tutorials, the video proposes a structured 4-step roadmap that builds real understanding.

The 4-Step Learning Path

The recommended approach is based on progressive learning, where each stage builds on the previous one.

Skipping foundational knowledge often leads to confusion later.

Step 1: Fundamentals of Machine Learning & Deep Learning

Before learning LLMs, it is essential to understand the fundamentals of:

  • Machine Learning

  • Neural Networks

  • Deep Learning concepts

  • Model training and evaluation

These fundamentals help you understand how models learn from data.

Recommended resources:

Machine Learning Specialization – Andrew Ng
https://www.deeplearning.ai/courses/machine-learning-specialization/

MIT Introduction to Deep Learning
https://introtodeeplearning.com/

Step 2: Transformers & the Attention Mechanism

Large Language Models are built on the Transformer architecture, which relies on the attention mechanism.

Understanding transformers explains how models process text and capture relationships between words.

Recommended resources:

The Illustrated Transformer – Jay Alammar
https://jalammar.github.io/illustrated-transformer/

Hugging Face NLP / LLM Course
https://huggingface.co/learn

Step 3: LLM Pre-training, Fine-tuning & RAG

Once the transformer architecture is clear, the next step is understanding how LLMs are trained and improved.

Key concepts include:

  • Pre-training large language models

  • Fine-tuning models for specific tasks

  • Retrieval Augmented Generation (RAG)

Recommended resources:

Cohere LLM University
https://cohere.com/llmu

DeepLearning.AI – Pretraining LLMs
https://www.deeplearning.ai/short-courses/pretraining-llms/

DeepLearning.AI – Fine-tuning LLMs
https://www.deeplearning.ai/short-courses/fine-tuning-llms/

Step 4: Applications & AI Agents

The final stage focuses on building real-world applications using LLMs.

This includes:

  • AI assistants

  • LLM-powered applications

  • Multi-agent systems

  • Autonomous AI workflows

Recommended resources:

Hugging Face Agents Course
https://huggingface.co/learn/agents-course

Berkeley LLM Agents Course
https://llmagents-learning.org/f24

Arize AI – AI Agents Mastery
https://arize.com/llm-course/

DeepLearning.AI – Multi-AI Agent Systems with CrewAI
https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/

A Key Insight

One important takeaway from this roadmap is that true understanding requires building from the ground up.

Many people jump directly into prompt engineering or AI tools, but without understanding:

  • Machine learning basics

  • Neural networks

  • Transformer architecture

it becomes difficult to truly grasp how these systems function.

Final Thoughts

The field of Artificial Intelligence is evolving extremely quickly, and large language models are becoming a central technology in modern software systems.

For anyone serious about understanding this space, following a structured learning path like the one described in the video can make a huge difference.

Rather than chasing every new tool or tutorial, focusing on strong fundamentals and progressive learning will lead to deeper understanding and long-term relevance.

✍️ These are my personal learning notes as I continue exploring Artificial Intelligence and Large Language Models.

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