Best Resources to Learn LLMs

Nov 05, 2025 • 5 min read
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Large Language Models (LLMs) are transforming industries with their ability to generate text, answer questions, assist in coding, and power AI-driven applications. If you are aiming for a career in LLMs and looking for resources to learn LLMs, this article is for you. In this article, I’ll take you through a list of 4 best resources to learn LLMs you can follow.

Best Resources to Learn LLMs

Here are four best resources to help you learn LLMs, from fundamental NLP concepts to building your own LLMs from scratch.

Natural Language Processing Specialization

Understanding Natural Language Processing (NLP) is crucial before diving into LLMs. NLP forms the foundation of LLMs, covering topics like tokenization, word embeddings, sequence modelling, and transformers.

The Natural Language Processing Specialization by DeepLearning.AI on Coursera is an excellent starting point. This specialization covers Natural Language Processing with:

  1. Classification and Vector Spaces
  2. Probabilistic Models
  3. Sequence Models
  4. Attention Models

By completing this specialization, you’ll build strong NLP fundamentals and gain hands-on experience with real-world projects, which will prepare you for more advanced LLM concepts.

Generative AI Engineering with LLMs Specialization

After mastering NLP, the next step is understanding how LLMs work, their architecture, and how to build applications using them.

The Generative AI Engineering with LLMs Specialization on Coursera provides a structured way to learn about:

  1. Architecture and Data Preparation
  2. Gen AI Foundational Models for NLP & Language Understanding
  3. Generative AI Language Modeling with Transformers
  4. Generative AI Engineering and Fine-Tuning Transformers
  5. Advanced Fine-Tuning for LLMs
  6. Fundamentals of AI Agents Using RAG and LangChain
  7. And a project with RAG and LangChain

This specialization focuses on both theoretical and practical aspects of LLMs.

Building LLMs from Scratch (only for experienced professionals)

While many developers work with pre-trained LLMs like GPT-4 and LLaMA, understanding how to build an LLM from scratch gives you deeper insights into the model’s architecture and training process.

The Building LLMs from Scratch book provides a step-by-step guide on:

  1. How transformers process text
  2. Training and optimizing your own LLM
  3. Implementing LLMs using deep learning frameworks like PyTorch and TensorFlow

This book is only recommended if you are well-versed in Machine Learning.

If you are new to Machine Learning, I’ll recommend you to follow my book instead, which will help you learn ML Algorithms first and then will introduce you to Generative AI and LLMs as well. Here are the paperback and ebook versions of my book:

  1. Paperback on Amazon
  2. Affordable Ebook on Google Play

Hands-On LLM Projects

The best way to learn LLMs is through practical projects that solve real-world problems. Working on projects helps solidify your understanding and gives you a portfolio to showcase your skills.

Here are five hands-on projects to build your expertise in LLMs:

  1. Building a RAG Pipeline for LLMs: Implement a Retrieval-Augmented Generation (RAG) system to improve LLM responses with external knowledge.
  2. AI Image Caption Recommendation System: Use LLMs to recommend captions for images.
  3. Analyzing Large Text Documents using LLMs: Develop an AI tool to process and summarize large text datasets efficiently.
  4. Code Generation Model with LLMs: Train a model to generate code snippets based on natural language descriptions.
  5. Text Completion using Fine-Tuning LLMs: Fine-tune an existing LLM to predict and complete text sequences.

By working on these projects, you’ll gain hands-on experience in LLM development, fine-tuning, and making you job-ready for AI roles.

Summary

So, here are four best resources to help you learn LLMs, from fundamental NLP concepts to building your own LLMs from scratch:

  1. Natural Language Processing Specialization
  2. Generative AI Engineering with LLMs Specialization
  3. Building LLMs from Scratch
  4. Hands-On LLM Projects