I get asked what to read to learn AI quite often. Like every other week.
But I've just been too busy to sit down and filter out what's good. There are thousands of courses now. I have a long list that I collected for another project. Prompt engineering for lawyers. ChatGPT for accountants. Copilot for HR. AI for finance. The overwhelming proportion teach you the tool but not how it works. And I think sharing them all will overwhelm without helping. Also, I am fearful that I will share one that harms more than it helps. For example, I have seen courses that treat teaching AI as guiding people blindly through tools - prompt and awe, prompt and awe. With zero understanding. I detest such courses and training.
And so I am giving up sharing that long list for now. Until I find a better way to frame and filter them.
Also, that's not how I learned AI. I started a PhD in it at 42, with no computer science background. Before that, for around 5 years, I taught myself the basics the slow way, from a handful of resources. That period + my PhD studies spanned 2015 to 2023. Way before the hype sparked by ChatGPT. Most of these learning resources are still up. Many are free. So I shall start by sharing a reading list of those resources.
The reading list covers most of what I remember (some have been updated). Grouped by what they are good for. The on-ramps, like Andrew Ng. The books to keep on the desk. The explainers for when a concept will not click. And so on. Each with a note on why it is worth your time, and which to do first. Almost all of these were started before LLMs, but still relevant. Some more recent additions that fit. A course from 2017 will teach you more about how AI works than a viral post on ten things a chatbot can do.
I called it The Boring Reading List for Learning AI. Because that is what it is.
Get the ebook here.
#AI #MachineLearning #DeepLearning #LearningAI #SimplyBoringAI