I call learning the math and theory for machine learning first the “bottom-up” approach to machine learning.

It is the approach taught by universities and used in textbooks.

It requires that you learn the mathematical prerequsites, then the general theories of the field, then the equations and their derivations for each algorithm.

• It is much slower.
• It is much harder.
• It is great for training academics (not practitioners).

A final problem is, that is where the bottom-up approach ends.

I teach an alternative approach that inverts the process called “top-down” machine learning.

We start by learning the process of how to work through predictive modeling problems end to end, from defining the problem to making predictions. Then we practice this process and get good at it. We start by learning how to deliver results and add value.

Later we circle back to the math and theory, but only in the context of the process. Meaning, only the theory and math that helps us deliver better results faster is considered.

You can learn more about the contrast between these two approaches here:

You can learn how to get started with this approach here:

But it’s Dangerous!

I have seen this criticism a lot.

It is dangerous for beginners to use algorithms they don’t understand to make predictions that the business depends upon.

I agree.

• I agree for the same reason that I think a student learning to drive should not drive the school bus.
• I agree for the same reason that I think a student learning to code should not put their hello world code into production.

But,

• The student driver can practice and get good enough to drive the school bus eventually.
• The student coder can practice and get good enough to put code into production.

Trust is earned in machine learning, just like with any other profession or skill.

Does knowing how the math of an algorithm works give you that trust?

Maybe, but probably not.

• Does knowing how a combustion engine works give you trust enough to drive?
• Does knowing how a compiler works give you trust enough to push code to production?

I write more about this here:

But Math is Required!

It is, just not first.

Learning how algorithms work and about machine learning theory can make you a better machine learning practitioner.

But, it can come later, and it can come progressively.

You can iteratively dip into textbooks and papers, as needed, with a specific focus of learning a specific thing that will make you better, faster or more productive.

Knowing how an algorithm works is important, but it cannot tell you much about when to use it.

In supervised machine learning, we are using data to build a model to approximate an unknown and noisy mapping function. If we knew enough about this function in order to correctly choose the right algorithm, we probably don’t need machine learning (e.g. we could use statistics and descriptive modeling of already understood relationships).

The badly kept secret in machine learning is that you can use machine learning algorithms like black boxes, at least initially, because the hard part is actually figuring out how to best frame the problem, prepare the data and figure out which of one thousand methods might perform well.

You can learn more about this here:

The math does not have to come first. It can, if you prefer to learn that way, but perhaps this site is not the best place for you to start.