The real prerequisite for machine learning isn’t math, it’s data analysis

When beginners get started with machine learning, the inevitable question is “what are the prerequisites? What do I need to know to get started?”

And once they start researching, beginners frequently find well-intentioned but disheartening advice, like the following:

You need to master math. You need all of the following:
– Calculus
– Differential equations
– Mathematical statistics
– Optimization
– Algorithm analysis
– and
– and
– and ……..

A list like this is enough to intimidate anyone but a person with an advanced math degree.

It’s unfortunate, because I think a lot of beginners lose heart and are scared away by this advice.

If you’re intimidated by the math, I have some good news for you: in order to get started building machine learning models (as opposed to doing machine learning theory), you need less math background than you think (and almost certainly less math than you’ve been told that you need). If you’re interested in being a machine learning practitioner, you don’t need a lot of advanced mathematics to get started.

But you’re not entirely off the hook.

There are still prerequisites. In fact, even if you can get by without having a masterful understanding of calculus and linear algebra, there are other prerequisites that you absolutely need to know (thankfully, the real prerequisites are much easier to master).