From zero to hero with LLMs

From zero to hero with LLMs

Start with Large Language Models (LLMs) in 2024

A complete guide to start and improve your LLM skills in 2024 without an advanced background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

First, if you have 0 programming or AI knowledge, please follow this guide I made for this exact purpose and come back here!

This guide is intended for anyone with a small background in programming and machine learning. There is no specific order to follow, but a classic path would be from top to bottom. If you don’t like reading books, skip them. If you don’t want to follow an online course, you can also skip it. There is not a single way to become a machine learning expert, and with motivation, you can absolutely achieve it.

All resources listed here are free, except some online courses and books, which are certainly recommended for a better understanding, but it is definitely possible to become an expert without them, with a little more time spent on online readings, videos, and practice. When it comes to paying courses, the links in this guide are affiliated links. Please use them if you feel like following a course, as it will support me. Thank you, and have fun learning! Remember, this is completely up to you and not necessary. I felt like it was useful to me and maybe useful to others as well.

Don’t be afraid to repeat videos or learn from multiple sources. Repetition is the key of success to learning!

Maintainer: louisfb01, also active on YouTube and as a Podcaster if you want to see/hear more about AI & LLMs! You can also learn more twice a week in my personal newsletter!

Feel free to submit an issue for any great resources to add to this repository.

Tag me on Twitter @Whats_AI or LinkedIn @Louis Bouchard if you share the list!

Want to know what this guide is about? Watch this video:

Table of Contents

  • Prerequesites
  • Start with short YouTube video introductions as a first step
  • LLM Books and articles (for readers)
  • Follow online courses
  • Practice, practice, and practice!
  • Prompting
  • Retrieval Augmented Generation (RAG)
  • More resources (Communities, cheat sheets, news, and more!)
  • How to find a machine learning job
  • AI Ethics
  • Learn more and do more… with LLMs


If you have 0 programming or AI knowledge, please follow this guide I made for this exact purpose. Check out the python section mostly and then you will have a strong enough background to come back here!

If you are somewhat familiar with Python and AI, then I wish you happy learning!

Start with short YouTube video introductions as a first step

Start with short YouTube videos introductions

This is the best way to start from nothing. Here, I list a few of the best videos I found that will give you a great first introduction to the terms you need to know to get started in the LLM field.

Understanding the terminology

Understanding Transformers and LLMs (i.e. models behind ChatGPT)!

Another easy way to get started and keep learning is by listening to podcasts in your spare time. Driving to work, on the bus, or having trouble falling asleep? Listen to some AI podcasts to get used to the terms and patterns, and learn about the field through inspiring stories! I invite you to follow a few of the best I personally prefer, like Lex Fridman, Machine Learning Street Talk, and obviously, my podcast: Louis Bouchard Podcast, where you will learn about incredibly talented people in the field with inspiring stories sharing the knowledge they worked so hard to gather. A new one I really enjoy listening to that keeps me up to date is the ThursdAI podcast by my friend Alex Volkov.

Here is a list of awesome courses available on YouTube that you should definitely follow and are 100% free.

LLM Books and articles (for readers)

If you prefer the article and reading path, here are some suggestions:

Follow online courses

If you like some more guidance, I can advise checking out (optional) online courses, such as…

You can easily google for more, but after reading and watching those, I believe you already have a good enough understanding of LLMs to get into the real deal: practice.

Practice, practice, and practice!

Practice is key

The most important thing in programming is practice. This applies to machine learning too. It can be hard to find a personal project to practice. I strongly advise you to try to build something by yourself, but I understand it may be intimidating. What I would then suggest is to follow one or two extremely applied courses and use the resource to build your own project based on the code examples they provide you, and ChatGPT or GitHub Copilot to work for you as a code assistant for the rest of the work.

Here are a few of the most applied courses I could find for LLMs:

  • Looking to build a quick text classification model or word vectorizer, fasttext is a good library to quickly train up a model.
  • Huggingface is THE place to get modern day NLP models, and they also include a whole course about it.
  • LangChain & Vector Databases in Production — An amazing free resource we built at Towards AI in partnership with Activeloop and the Intel Disruptor Initiative to learn about LangChain & Vector Databases in Production. “Whether you are an experienced developer who’s a newcomer to the AI realm or an experienced machine learning enthusiast, this course is designed for you. Our goal is to make AI accessible and practical, transforming how you approach your daily tasks and the overall impact of your work.”
  • Training & Fine-Tuning LLMs for Production — An amazing free resource we built at Towards AI in partnership with Activeloop and the Intel Disruptor Initiative to learn about Training & Fine-Tuning LLMs for Production. “If you want to learn how to train and fine-tune LLMs from scratch and have intermediate Python knowledge as well as access to moderate compute resources (for some cases, just a Google Colab will suffice!), you should be all set to take and complete the course. This course is designed with a wide audience in mind, including beginners in AI, current machine learning engineers, students, and professionals considering a career transition to AI. We aim to provide you with the necessary tools to apply and tailor Large Language Models across a wide range of industries to make AI more accessible and practical.”
  • The Real-World ML Tutorial & Community — Paid

A reminder. The best way to learn is to build something! I really prone to learn by doing. Those courses are all great but optional. You can do it on your own, and most companies providing resources for working with LLMs (OpenAI, LangChain, Activeloop, Cohere, W&B…) have great tutorials to get you started and build something. Then, you can ask ChatGPT to help you finish it!


Prompting is an important new skill to learn for both using the models and building NLP-related apps.

More on Retrieval Augmented Generation (RAG)

Most people build RAG-based apps currently. Here are a few resources that I loved to get you started and have a good understanding of it…

More Resources

Join communities!

Follow reddit communities — Ask questions, share your projects, follow news, and more.

Follow the news in the field!

Subscribe to YouTube channels that share new papers — Stay up to date with the news in the field!

LinkedIn Groups

Facebook Groups

  • Artificial Intelligence & Deep Learning — The definitive and most active FB Group on A.I., Neural Networks and Deep Learning. All things new and interesting on the frontier of A.I. and Deep Learning. Neural networks will redefine what it means to be a smart machine in the years to come.
  • Deep learning — Nowadays society tends to be soft and automated evolving into the 4th industrial revolution, which consequently drives the constituents into the swirl of societal upheaval. To survive or take a lead one is supposed to be equipped with associated tools. Machine is becoming smarter and more intelligent. Machine learning is inescapable skill and it requires people to be familiar with. This group is for these people who are interest in the development of their talents to fit in.


  • Synced AI TECHNOLOGY & INDUSTRY REVIEW — China’s leading media & information provider for AI & Machine Learning.
  • Inside AI — A daily roundup of stories and commentary on Artificial Intelligence, Robotics, and Neurotechnology.
  • AI Weekly — A weekly collection of AI News and resources on Artificial Intelligence and Machine Learning.
  • AI Ethics Weekly — The latest updates in AI Ethics delivered to your inbox every week.
  • Louis Bouchard Weekly  — One and only one paper clearly explained weekly with an article, video demo, demo, code, etc.
  • ThursdAI — Recaps of the most high-signal AI weekly spaces!
  • Toward’s AI newsletter — Summarizing the most interesting news and learning resources weekly as well as community updates from the Learn AI Together Discord community. Perfect for ML professionals and enthusiasts.
  • The Batch - Andrew Ng /

Follow Medium publications

  • Towards Data Science — “Sharing concepts, ideas, and codes”
  • Towards AI — “The Best of Tech, Science, and Engineering.”
  • OneZero — “The undercurrents of the future. A Medium publication about tech and science.”

Find a machine learning job

  • Read this section from the article full of interview tips and how to prepare for them.
  • Learn how the interview process goes and getting better at preparing for them by watching how others did it, like the interview series I ran with experts from NVIDIA, Zoox (Self-driving company), D-ID (Generative AI Startup), etc.

AI Ethics

Learn more and do more… with LLMs

ChatGPT, Bing, Claude… are incredible. Of course, they have limitations. Yet, you can leverage those to learn anything you want. I use it for coding or asking lots of questions in general. You need to double-check when you ask for important questions. Still, it is a powerful tool. Yes, it is a tool, not a human replacement. Use it as a dumb assistant that knows about pretty much everything.

Here’s a clear example of how I used it for a project to better understand a function from a project I was not familiar with. This is for python, but those models are extremely powerful for coding in general, understanding new platforms (like AWS, GCP, working with a virtual machine, a server, SSH connections, etc…. anything you are not familiar with that is useful in the LLM space).

p.s. I didn’t mention Bing and Claude for fun. Don’t be overly dependent on a single company like OpenAI. There are (and will always be) other companies in the fight for the best LLM. I wanted to create an example for the guide this morning when…

Tag me on Twitter @Whats_AI or LinkedIn @Louis Bouchard if you share the list!

👀 If you’d like to support my work, you can check to Sponsor this repository or support me on Patreon.

This guide is still regularly updated.