Thread by @svpino

When you start with machine learning, it's tempting to learn as many different algorithms and methods as possible.

This is not the best approach. This will not make you the best you can be.

[1 / 5] 🧵👇

Instead, focus on understanding the power of representations and getting as good as you can at feature engineering.

Feed garbage to your fancy algorithms and they will give you garbage back. No exceptions.

[2 / 5]

"Representation" is the process of mapping data into useful features.

"Feature engineering" is the process of determining which features might be useful in training a model.

There's a lot of creativity involved here! The time you spend will pay you back in spades.

[3 / 5]

Most of the time, if you put in the work to select (and create) the best possible set of features, the algorithm you end up using becomes secondary.

I like to call this a "data-first approach." Not sexy, but extremely powerful.

[4 / 5]

At this game, the best is not whoever knows the most, but whoever can think creatively.

And if you are looking for some help, follow me, and every week I'll help you navigate this thing from doing it, failing at it, learning, and fixing it.

[5 / 5]

🦕

I put this thread about Feature Engineering some time ago:

https://twitter.com/svpino/status/1313678930134667264?s=20

Excellent point: breath is needed to bypass interviews.

https://twitter.com/nneal1213/status/1364352352170299395?s=20