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naraburns  nihil supernum  1yr ago (text post)   3971 thread views

Small-Scale Question Sunday for March 19, 2023

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Is there a straightforward path for a humble 40yo frontend web dev to learn machine learning and gain a massive career upgrade? Like can I just brush up on my linear algebra, learn some machine learning concepts, and get hired somewhere? Or is it gonna be a bit harder than that?

My opinion? Don’t bother.

There’s going to be a gigantic industry from utilizing ai and learning to use it well. Don’t dive into creating the ai, dive into figuring out the best ways to use the ai to accomplish something else. That’ll probably be more lucrative to you at this point.

Yeah, just like mobile dev exploded and was lucrative over the last decade, the next few years might be the age of the "applied AI engineer".

I suspect that learning linear algebra to get a leg up in the AI revolution would be like learning semiconductor physics to improve your Excel skills. That's not the level of interface where one can simply learn a few techniques and become more useful to an employer.

What are you trying to get into? Data Science in general is very gatekept by formal education and you'll be competing with PhDs for most positions. It doesn't help that the job title is seen as very hot so any opening gets flooded by resumes. That said, it's not very entry level friendly and if you know a particular domain really well and are good at design and communication you can get a leg up that way. Also stats nerds are really bad at programming, so you will likely have an advantage there.

I keep seeing this term "data science" come up lately. What is it and what is the connection to machine learning?

You got a few answers and they are all on point. It’s very broad and under defined and can mean anything from building dashboards to building data pipelines to building ML models to just being an analyst.

The standard definition is that data scientists combine math/statistics, programming, and knowledge of a business domain to use data for solving business problems. One of the key pieces of the tool kit are machine learning models which are at their heart statistical tools.

It's very fuzzy and inconsistently used, but the basic idea is: Suppose you have a huge amount of data. How do you curate the data set, how do you answer question with it, how do you make predictions with it?

Machine Learning can be used for any of those things. For example, you can curate it by first sorting the data set with some clustering algorithm into groups to make it easier to handle (and possibly throwing outliers away). You can answer questions by just running some CNN on the data set. Etc.

I'm not in the field but afaik it's an interdisciplinary field where you use statistics, math, programming, and domain specific expertise to analyze and interpret big or complex sets of data, and build models for predictions.

Machine learning is used to make programs or AIs that can more or less autonomously run and do the work of analysis and model building.

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