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8 months ago
The Cold Start Problem: How to Build Your Machine Learning Portfolio

 

Originally published in KDNuggets, January, 2019

I’m a physicist who works at a YC startup. Our job is to help new grads get hired into their first machine learning jobs.

Some time ago, I wrote about the things you should do to get hired into your first machine learning job. I said in that post that one thing you should do is build a portfolio of your personal machine learning projects. But I left out the part about how to actually to do that, so in this post, I’ll tell you how.  (In case you’re wondering why this is important, it’s because hiring managers try to assess you by looking at your track record. If you don’t have a track record, personal projects are the closest substitute.)

Because of what our startup does, I’ve seen hundreds of examples of personal projects that ranged from very good to very bad. Let me tell you about two of the very good ones.

The all-in

What follows is a true story, except that I’ve changed names for privacy.

Company X uses AI to alert grocery stores when it’s time for them to order new inventory. We had one student, Ron, who really wanted to work at Company X. Ron wanted to work at Company X so badly, in fact, that he built a personal project that was 100% dedicated to getting him an interview there.

We don’t usually recommend going all-in on one company like this. It’s risky to do if you’re starting out. But — like I said — Ron really wanted to work at Company X.

So what did Ron build?

To continue reading this article click here.

About the Author

Edouard Harris is the founder of @SharpestMindsAI (YC W18), physicist and machine learning engineer.

 

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