Machine Learning for non-PhDs – a guest post from speaker Derek Haynes

Derek Haynes on February 18, 2019

Since I was a pasty, awkward middle schooler I’ve been writing computer code. Much of my coding time is spent fixing my own silly bugs, but a couple of magical coding moments standout:

  • When I accessed my hideous, lets animate-all-the-gifs AOL-hosted homepage on a middle school computer.
  • Character-by-character, copying Tetris onto my TI-85 calculator from a friend.
  • Watching the How to build a blog engine in 15 minutes video with Rails video. Rails was the secret weapon for small teams with big dreams.

Whether it’s skepticism honed by time or just reality, I’ve experienced fewer of these of moments over the past ten years. That is, until recently.

About a year ago, I solved my first problem with machine learning, identifying handwritten digits with only a few lines of code. It was magical taking something that seemed so human and solving it with so little code. There’s no way I could solve this problem elegantly with my traditional toolset. The problem I worked on is only scratching the surface of what machine learning can do. For example, machine learning is used today to:

It’s possible machine learning and the larger field of artificial intelligence will be the largest technological leap since the Internet.

So, where to begin? And should you even try?

There’s clearly demand for jobs in machine learning. Notice the large gap in employer demand vs. job seekers:

A major reason for this gap is that many companies are mistakenly looking for machine learning researchers, not candidates that know how to apply machine learning. Paraphrasing Cassie Kozyrko, Google’s Chief Decision Intelligence Engineer: if you run a bakery, you don’t hire an oven maker. You hire someone that knows how to bake bread. There’s a small minority of ML problems that require a new algorithm. Most problems can be solved with off-the-shelf models.

This hiring mindset is changing, but the wheels of big business move slowly. Today is a great time to take advantage of this gap: it’s significantly easier and cheaper for small companies to collect large amounts of data than it was a decade ago. Many smaller software companies have large enough data sets to solve time-consuming, differentiating problems. However, very few small companies have an applied machine learning engineer on staff to take advantage of all that data.

If you’re like me (machine learning-curious and too lazy to go back to school), applied machine learning might be for you. At 11AM Tuesday the 26th of February at The Innosphere, I’ll be giving a talk on getting started with machine learning. You’ll learn enough to talk intelligently about machine learning at your next network event, see me live-code a machine learning problem, and receive a curated list of resources to get you started.

In short, take advantage of my wasted efforts, false starts, and wrong turns. Just get the good stuff for applied machine learning.