I’ve recently been asked a lot about what education should one take to get better at understanding artificial intelligence / machine learning as its the hot thing now and for the foreseeable future. I’m also a father and have a child interesting in technology. So, I’m also figuring out how to put together a track that is aimed at understanding AI/ML. I found this blog post HERE that breaks down an education path into four stages. Those stages are the following:
- Stage 1: Foundational Math. All the high school and university-level math that underpins machine learning. All of algebra, a lot of single-variable calculus / linear algebra / probability / statistics, and a bit of multivariable calculus.
- Stage 2: Classical Machine Learning. Coding up streamlined versions of basic regression and classification models, all the way from linear regression to small multi-layer neural networks.
- Stage 3: Deep Learning. Multi-layer neural networks with many parameters, where the architecture of the network is tailored to the specific kind of task you’re trying to get the model to perform.
- Stage 4: Cutting-Edge Machine Learning. Transformers, LLMs, diffusion models, and all the crazy stuff that’s coming out now, that captured your interest to begin with.
This path may not be for everybody, but I do like the details and throughs why each stage helps one prepare for a career with AI. It’s a good post to check out.