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I'm so happy you're joining me on this path. We'll be building immensely satisfying projects in the coming weeks. Some will be easy, some will be challenging, many will ASTOUND you! The projects build on each other so you develop deeper and deeper expertise each week. One thing's for sure: you're going to have a lot of fun along the way.
I am phasing in new, updated versions of all the course videos, with new videos and new code. I realize this can be jarring for people already on the course, and I will do my best to make this as smooth as possible.
- Both video series will be available in Udemy and you can watch either. A new week should become available each week as we roll this out.
- You can follow the original videos or the new videos - either should work great. Switch between them at any time.
- The latest code is pushed to the repo. You can follow along with new code, or revert to original code.
Full details of this upgrade is on the course resources in Purple at the top:
https://edwarddonner.com/2024/11/13/llm-engineering-resources/
The most significant change is that the new version uses the fabulous uv, instead of Anaconda. But there's also tons of new content, including new models, tools and techniques. Prompt caching, LiteLLM, inference techniques and so much more.
We are rolling out the new weeks, but keeping the original content in an appendix:
In Udemy:
Section 1 = NEW WEEK 1
Section 2 = NEW WEEK 2
Section 3 = NEW WEEK 3
Section 4 = Original Week 4
Section 5 = Original Week 5
Section 6 = Original Week 6
Section 7 = Original Week 7
Section 8 = Original Week 8
Then as an appendix / archive:
Section 9 = Original Week 1
Section 10 = Original Week 2
Section 11 = Original Week 3
To revert to the original version of code, consistent with the original videos (Anaconda + virtualenv)
If you'd prefer to stick with the code for the original videos, simply do this from your Anaconda Prompt or Terminal:
git fetch
git checkout original
And that's it! Any questions, please ask me on Udemy or at [email protected]. More details at the top of the course resources here.
I'm here to help you be most successful with your learning. If you hit any snafus, or if you have any ideas on how I can improve the course, please do reach out in the platform or by emailing me direct ([email protected]). It's always great to connect with people on LinkedIn to build up the community - you'll find me here:
https://www.linkedin.com/in/eddonner/
And this is new to me, but I'm also trying out X/Twitter at @edwarddonner - if you're on X, please show me how it's done 😂
Resources to accompany the course, including the slides and useful links, are here:
https://edwarddonner.com/2024/11/13/llm-engineering-resources/
And a useful FAQ with common questions is here:
https://edwarddonner.com/faq/
Important note: see my warning about Llama3.3 below - it's too large for home computers! Stick with llama3.2 - several students have missed this warning...
We will start the course by installing Ollama so you can see results immediately!
- Download and install Ollama from https://ollama.com noting that on a PC you might need to have administrator permissions for the install to work properly
- On a PC, start a Command prompt / Powershell (Press Win + R, type
cmd, and press Enter). On a Mac, start a Terminal (Applications > Utilities > Terminal). - Run
ollama run llama3.2or for smaller machines tryollama run llama3.2:1b- please note steer clear of Meta's latest model llama3.3 because at 70B parameters that's way too large for most home computers! - If this doesn't work: you may need to run
ollama servein another Powershell (Windows) or Terminal (Mac), and try step 3 again. On a PC, you may need to be running in an Admin instance of Powershell. - And if that doesn't work on your box, I've set up this on the cloud. This is on Google Colab, which will need you to have a Google account to sign in, but is free: https://colab.research.google.com/drive/1-_f5XZPsChvfU1sJ0QqCePtIuc55LSdu?usp=sharing
Any problems, please contact me!
After we do the Ollama quick project, and after I introduce myself and the course, we get to work with the full environment setup.
Hopefully I've done a decent job of making these guides bulletproof - but please contact me right away if you hit roadblocks:
NEW INSTRUCTIONS for new version of the course (rolled out October 2025): New Setup Instructions All Platforms
ORIGINAL INSTRUCTIONS for people on the version prior to October 2025:
- PC people please follow the instructions here: Original PC instructions
- Mac people please follow the instructions here: Original Mac instructions
- Linux people please follow the instructions here: Original Linux instructions
During the course, I'll suggest you try out the leading models at the forefront of progress, known as the Frontier models. I'll also suggest you run open-source models using Google Colab. These services have some charges, but I'll keep cost minimal - like, a few cents at a time. And I'll provide alternatives if you'd prefer not to use them.
Please do monitor your API usage to ensure you're comfortable with spend; I've included links below. There's no need to spend anything more than a couple of dollars for the entire course. Some AI providers such as OpenAI require a minimum credit like $5 or local equivalent; we should only spend a fraction of it, and you'll have plenty of opportunity to put it to good use in your own projects. During Week 7 you have an option to spend a bit more if you're enjoying the process - I spend about $10 myself and the results make me very happy indeed! But it's not necessary in the least; the important part is that you focus on learning.
See Guide 9 in the guides directory for the detailed approach with exact code for Ollama, Gemini, OpenRouter and more!
There are folders for each of the "weeks", representing modules of the class, culminating in a powerful autonomous Agentic AI solution in Week 8 that draws on many of the prior weeks.
Follow the setup instructions above, then open the Week 1 folder and prepare for joy.
The mantra of the course is: the best way to learn is by DOING. I don't type all the code during the course; I execute it for you to see the results. You should work along with me or after each lecture, running each cell, inspecting the objects to get a detailed understanding of what's happening. Then tweak the code and make it your own. There are juicy challenges for you throughout the course. I'd love it if you wanted to submit a Pull Request for your code (see the Github guide in the guides folder) and I can make your solutions available to others so we share in your progress; as an added benefit, you'll be recognized in GitHub for your contribution to the repo. While the projects are enjoyable, they are first and foremost designed to be educational, teaching you business skills that can be put into practice in your work.
You should be able to use the free tier or minimal spend to complete all the projects in the class. I personally signed up for Colab Pro+ and I'm loving it - but it's not required.
Learn about Google Colab and set up a Google account (if you don't already have one) here
The colab links are in the Week folders and also here:
- For week 3 day 1, this Google Colab shows what colab can do
- For week 3 day 2, here is a colab for the HuggingFace pipelines API
- For week 3 day 3, here's the colab on Tokenizers
- For week 3 day 4, we go to a colab with HuggingFace models
- For week 3 day 5, we return to colab to make our Meeting Minutes product
- For week 7, we will use these Colab books: Day 1 | Day 2 | Days 3 and 4 | Day 5
You can keep your API spend very low throughout this course; you can monitor spend at the dashboards: here for OpenAI, here for Anthropic.
The charges for the exercsies in this course should always be quite low, but if you'd prefer to keep them minimal, then be sure to always choose the cheapest versions of models:
- For OpenAI: Always use model
gpt-4.1-nanoin the code - For Anthropic: Always use model
claude-3-haiku-20240307in the code instead of the other Claude models - During week 7, look out for my instructions for using the cheaper dataset
Please do message me or email me at [email protected] if this doesn't work or if I can help with anything. I can't wait to hear how you get on.
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I've put together this webpage with useful resources for the course. This includes links to all the slides. https://edwarddonner.com/2024/11/13/llm-engineering-resources/ Please keep this bookmarked, and I'll continue to add more useful links there over time. |