Developers choosing an LLM course
Is the FSDL LLM Bootcamp still worth taking?
The short answer
Use the FSDL Spring 2023 LLM Bootcamp for product decisions: evaluation, deployment, and user experience. Do not treat its code as a current SDK reference. If your immediate problem is understanding model internals or learning to train models, start with one of the alternatives below.
Before you start
Our suggested starting point: you can run a small program, call an API, and describe one task your application should perform. If installation and Python syntax are the main obstacles, address those before committing to a whole bootcamp.
The Spring 2023 lectures are publicly available for free. Running a project can incur model or hosting costs; check current pricing before enabling paid services.
Leave with something you can check
This is our suggested exercise, not an official assignment or a claim of course completion.
- Write one acceptance test before watching: for a document assistant, require an answer that points to the supporting passage and declines questions the document cannot answer.
- Choose the lecture that addresses your bottleneck: LLMOps for evaluation, UX for the interaction, or augmented language models for connecting information. Take notes against your own task, not a completion checklist.
- Rebuild the smallest example with current official SDK documentation. Use public sample text, set a spending limit, and keep the tool read-only.
- Try three answerable questions and two unanswerable ones. Record failures. Continue the course only if the next lesson addresses a failure you can name.
Choose the route that fits your task
Free learning material
FSDL LLM Bootcamp
You already code and need to turn an LLM demo into a usable application.
Next step: Read the review, then choose one product or evaluation lecture from the Spring 2023 syllabus.
Read the FSDL LLM Bootcamp review →Free learning material
Hugging Face LLM Course
You want the model, tokenization, and Transformers foundation behind an application.
Next step: Check the Python prerequisites in the review before working through the introductory chapter.
Read the Hugging Face LLM Course review →Free learning material
fast.ai Practical Deep Learning
Your goal is to train and apply models, not primarily to wrap a hosted LLM API.
Next step: Read the coding prerequisites, then use the first lesson to build and inspect a small model.
Read the fast.ai Practical Deep Learning review →Routes are matched to different needs, not ranked by commission. Some links elsewhere on this site may earn us a commission. Free course material does not guarantee free tools or certificates.
Questions before you commit
Is an older LLM course automatically useless?
No. Separate decisions that persist from implementations that change. Evaluation design and a clear user task can remain useful; package versions, model names, and API calls need a fresh check. The exercises above are our suggested way to test the fit, not a claim that we reran every course notebook.
Should I finish the entire course before building?
Our recommendation is to make one small artifact first. If you cannot explain what the next lecture will help you change, pause and choose a narrower lesson. A finished playlist is not evidence that an application works, and this guide does not promise employment or income.
Still not a fit?
Choose the obstacle for a next step. This is optional; no written response or account is needed.
For a different starting point, browse the course catalog.
Sources and scope
Official information checked 2026-09-19. Fit judgments and exercises are our editorial suggestions. Availability may change; follow the provider’s current terms.