Machine Learning Is Way Easier Than It Looks

Machine Learning Is Way Easier Than It Looks by Ben McRedmond.

From the post:

It’s easy to believe that machine learning is hard. An arcane craft known only to a select few academics.

After all, you’re teaching machines that work in ones and zeros to reach their own conclusions about the world. You’re teaching them how to think! However, it’s not nearly as hard as the complex and formula-laden literature would have you believe.

Like all of the best frameworks we have for understanding our world, e.g. Newton’s Laws of Motion, Jobs to be Done, Supply & Demand — the best ideas and concepts in machine learning are simple. The majority of literature on machine learning, however, is riddled with complex notation, formulae and superfluous language. It puts walls up around fundamentally simple ideas.

Let’s take a practical example. Say we wanted to include a “you might also like” section at the bottom of this post. How would we go about that? (emphasis in the original)

Yes, Ben uses a simple example. Yes, Ruby isn’t an appropriate language for machine learning. Yes, there are far more complex techniques in common use for machine learning. Just to cover a few of the comments made in response to Ben’s post.

However, Ben does illustrate that it is possible to clearly communicate the essential principles in a machine learning example. And to provide simple code that implements those principles.

That does not take anything away from more complex techniques or more complex code to implement any machine learning approach.

If you are writing about machine learning in professional literature, don’t use this approach as “clarity” there has a different meaning than when writing for non-specialists.

On the other hand, when writing for non-specialists, do use Ben’s approach as “clarity” there isn’t the same as in professional literature.

Neither one is more right or correct than the other, but are addressed to different audiences.

Ben’s style of explanation is one that is worthy of emulation, at least in non-professional literature.

I first saw this in a tweet by Carl Anderson.

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