I'd recommend starting with the [Andrew Ng Coursera specialization](https://www.coursera.org/specializations/machine-learning-introduction#courses). It's free and will give you a good base to build upon. I feel like he explains concepts very well and is good about explaining terminology.

> What would it take to be able to learn to train a model and deploy in-house solutions? For my company, I'd like to take our knowledge base and SOP and turn it into an interactive guide you can ask questions.

If the SOP is fairly short you can add it to your ChatGPT prompt and it can do Q&A from that. I found [Learn Prompting](https://learnprompting.org/docs/intro) helpful to understand how to do this.

I'm not sure about the knowledge base but it might be possible to inject that as knowledge too. The challenge is that there's a max input length.

But let's take a step back for a moment -- in general it's not too hard to learn ML basics and be able to build some model. Like it might take a few weekends depending on your schedule and previous experience with programming and math. If you want to solve a question answering problem, how much you need to learn will depend a great deal on how well you need it to work. For instance, you could probably get by with a simple search system for many things but it might not meet your bar for quality.

> I apologize if machine learning isn't the same thing as A.I.

I think of AI as the broader term and generally I think about the [AIMA table of contents](http://aima.cs.berkeley.edu/contents.html) for the general scope of AI -- machine learning is in there but there's a lot of other stuff too like logic, planning, ontologies, and optimization problems. That said, in the news AI is often used to mean "any technology that seems magical" and that's problematic because things like chess bots seemed magical in the past but no longer seem magical. So the scope of the term has shifted over time.