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knob

I hope this post helps someone looking to start with ML.

When I started my journey, I thought I should do what this article mentions.

>>First, you should learn the fundamentals:

>> Learn mathematics

My problem is that "learn mathematics" can take years. To me, it would be very frustrating.

So I started down that path... and got completely overwhelmed.

When everything took off and I went from 2 mph to 60 mph was when I did the fast.ai course. The fast.ai course is strongly recommended by me. You'll start seeing results and "actionable" work very quickly. If you so desire, then you can keep on with the more fundamental stuff like mathematics. Also have to give a shout-out to their forums. The people are super nice.

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IMO, for reaching PhD eligibility level at ML, you just need 2-3 core math courses. (as long as each is rigorous)

Linear Algebra, Probability and Statistics. (and some optimization)

The fast.ai course is great, but having the math background really helps bring the whole field together. Different ideas and models with the math, seem to stand completely independent of each other.

As a software engineer who wants to learn ML, the fast.ai course is great. But, if you find yourself in a situations where you are scoping out a data science problem, the math background helps immensely in coming up with a solution.

A calculus course would be good as well.

Agree 100%, but for "rigorous" courses in probability and statistics, that should be a prerequisite.

Yeah, that's ridiculous. Who actually does that and is capable of sticking to it?

To me the best way of learning stuff is diving head first and playing around with it, making projects with it, and then going back and learning more theory when I start to understand why it's important.

One strategy that helps with learning math is learning it through actionable programming.

Maybe worth checking out are two books that I'm writing right now where this is used and explained in more detail: https://aiprobook.com

Deep Learning for Programmers at https://aiprobook.com/deep-learning-for-programmers/

Numerical Linear Algebra for Programmers at https://aiprobook.com/numerical-linear-algebra-for-programme...

Everything uses open source software:

https://github.com/uncomplicate

Reading the "buy my books!" blurbs, the "No C++, blah, blah, No C++!, blah, No C++!!" had me laughing.

I have a similar feeling. To learn the basic math (single, multivariable calculus, linear algebra) will probably take you 300 hours of serious study if you don’t already know any of it. That’s one hour a day for a year (if you actually succeed at studying 8 days out of 10, which is pretty good) without implementing anything. If you want to get to something like a beginner nonlinear optimization course you probably need to study proofwriting then advanced calculus first. Add in a serious undergrad probability and stats course and you’re at 2-3 years of study before you actually did anything...

If you’re someone who enjoys math you might do it for its own sake but it’s difficult for me to imagine someone making serious progress in this direction without either a passion for the subject, an already relatively strong background, or being in a university.

>To learn the basic math (single, multivariable calculus, linear algebra) will probably take you 300 hours of serious study if you don’t already know any of it.

Frankly, compared to many other disciplines, that's pretty light. Single variable calculus, linear algebra and probability were all required in my undergrad CS curriculum. Multivariable calculus was the only extra course. And if you're in most engineering disciplines, all of this is required.

I'm guessing the difference for CS folks is that they rarely use this stuff in the curriculum, whereas in most engineering programs, you'll use calculus day and night.[0] So for someone like me (engineering background), it was easy to dive into even years out of school as I'd not forgotten a lot of math.

Now of course, if you want to get deep into ML, there's a lot more math than that. However, most successful people using ML do not need to know that math.

And compared to other disciplines like control theory, communications theory, etc, the prerequisites for ML are a lot lighter.

[0] Only in school. Almost never in industry.

I agree with the overall sentiment as well. However, 300 hours of study to go from 0 to single & multivariable calculus plus linear algebra doesn't actually sound that bad. 10 hours per week (2 hours per day post work, make up missed days on the weekend) for 30 weeks?

It depends on how many other responsibilities you have or other activities you engage in, and how much you care about the project, I suppose. To me 10 hours of solid study after work is a lot, but other people might throw out some other stuff I do and be willing to spend it studying. I can’t comment on whether it’s a good idea. (I believe there is very much such a thing as too much work ethic).

For us studying physics, thats like 30 hours of actually attending class for each course, so 90 total, then some days of study, say 3 x 8h, thats more like 114h, not 300...

The trick with high quality machine learning is that folks need to know programming, math, statistics and the industry they are in. Not everyone can do that.

This is an important point. Some Deep Learning proponents suggest that modern algorithms are so powerful that they no longer require feature engineering or expert industry knowledge. In reality, knowing exactly which questions to ask and what data to collect can be more difficult than the ML/DL implementation.

Hahaha. You thought you would ascend to an engineering-type profession in less than "years"?

Maybe it's easier to become a surgeon by reading blogposts? Better paid?

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