Everyone tells you how important math is in machine learning.

But I believe that if you want to learn machine learning today, there are things more important than math which very few people will tell you about.

Here are 5 of them.

The field of machine learning has been very academic which is why there is so much emphasis on learning math for it.

But, today with the frameworks that we have, not knowing how to multiply matrices is far less of an issue than knowing how to use the tools in this list

Yes, I know; you have R, Julia, and whatnot for machine learning but as of today, Python is the industry standard.

Get your data structures and algorithms right in Python because you will need them, here are some concepts worth learning

Let's say you're building the next big GPT-3 powered app that's going to take over the world.

You add a new feature to your app but it breaks the entire thing, what do you do now?

Learn Git, the ultimate version control tool.

I use this cheat sheet for all my git needs

You will be constantly be working on multiple packages which specific python version requirements

Virtual environments like Anaconda or Poetry will make this process much easier for you.

Who likes to see broken dependencies while running code?

At some point in time, you will have to deploy that crazy machine learning app that you made, say hello to docker.

Docker essentially allows you to package your apps in a box that can run on any operating system, also very helpful in several other situations.

Learn docker

Storytelling is an important part of machine learning, explaining how your model works will make it easier for others to make changes and improve it.

Markdown and LaTeX allow you to present your text explanations beautifully, learn these markup languages.

I started working with these tools a lot more after I got my job this year and it was surprising how few people talk about them.