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Comment by qorrect | Hacker News Reader

I am a long time developer who is trying to move into data science / ML.

What I've found after taking off for 12 months of self-study, is that it quickly dissolves into you must know math. As far as I can tell, its take a problem, map it into a vector space, then use the full power of mathematical analysis on it .

There is a huge push by large companies to make AI as a service though, and for that, you only really need to know how to use the APIs.

Which makes me wonder, why in hell have I done burnt through all my savings for this. Sure I have a new found love for math, but I'm not going to be accepted as a mathematician ever without the rigor of a formal education, and if I just wanted to use APIs ... I could have continued to do what I was doing.

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> Sure I have a new found love for math, but I'm not going to be accepted as a mathematician ever without the rigor of a formal education

I think there are two separate things here:

1. Being a research mathematician requires a degree of expertise which is easier to come by with formal education.

2. Knowing how to use APIs and make correct distributional assumptions; despite the bullshit fed by our industry, it is not easy or non trivial to design a completely idiot proof API. So having know-how of how the math works under the hood is helpful even if you are going to just use the API.

Thank you for saying that, I was feeling a bit down yesterday.

You don't have to be a mathematician to do machine learning. Jeff Dean said that almost every engineer working at Google should be capable of picking up the math for machine learning [1]. In particular with deep learning the math is quite manageable.

I would encourage you to take part of some Kaggle competitions to get a better feel for the practical aspect of machine learning.

[1] https://backchannel.com/how-google-is-remaking-itself-as-a-m...

For sure work through a linear algebra book, a stats book and some probability theory. Some basic calculus as well. Work through all of the simple examples. Just like programming..

That level of math helps to model the problem domain. The part of modeling the problem is to see that everything in ML is a graph. So you can look at it from that point of view as well, at least computational wise. Mapping the math to the graph is the heart of it all.

I'm a little confused -- how does learning math involve burning through savings?

If you live in an expensive place like San Francisco, you burn through savings by default without a job.

Ah... I didn't catch the "without a job" part...

So where are you at in terms of job search and preparation?

I'm about 3 months from completing this course https://www.udacity.com/ai, having finished the coursera ML course and the udacity data anlayst class.

Honestly I haven't started the job search, I plan to start in earnest in about a month.

Udacity seems sure about placing us in jobs though, apparently, the market has a great demand and a small supply.

Do you have any advice ?

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