How do you learn machine learning when you're rusty at math?

Mum, how can I get out of the exam?

What?

I’m going to fail.

Tears started filling my eyes. I was sitting at my desk with the lamp on, 11 pm the night before the final exam.

Maths C. That’s what they called it. There was Maths A and B but C was the hardest and I was doing it.

There was something about matrices and imaginary numbers and proofs. I couldn’t do any of it. Only a few matrix multiplications, the easier ones.

I did the exam. Somehow I passed. I shouldn’t have. My teacher let me off the hook. That was 2010.

University came and I majored in biomedicine. I failed my first statistics course, twice. Then I changed out of biomedicine.

I graduated in 2015 with a dual major in food science and nutrition. Now food is one of my religions.

2017 happened and I decided to get into machine learning. I’d seen the headlines, read the articles.

Andrew Ng’s machine learning course kept getting recommended so I started there.

The content was tough. All the equations, all the Greek symbols. I hadn’t touched any of it since high school.

But the code was exciting, and the concepts made sense. Ng’s teaching skills meant they just made sense.

So I followed those. Kept going at it. This time I didn’t have an Xbox to distract me like high school.

My math is still rusty. I’ve done some Khan Academy courses on matrix manipulation, calculus and bookmarked some linear algebra courses to get into. There’s one from Rachel Thomas and another one from Stanford.

Math is a language, it takes time to learn, time to be able to speak it. Programming is the same. Machine learning combines them both and a bit more.

I started with programming first. Built some machine learning models, using Python code, TensorFlow, PyTorch and the others. Saw how the concepts linked with the code. It got me hooked.

You can start learning machine learning without an in-depth knowledge of the math behind it. If your math is rusty, you can learn machine learning with concepts and code first. Many of the tools available to you abstract away the math and let you build.

But when you gain a little momentum, learn a little more, hit a roadblock, you can dive into the math.

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