Machine learning is one of the hottest topics in tech at the moment. Companies are hungry to hire professionals with machine-learning knowledge, even if it costs them quite a bit more than a “standard” technologist salary.
The just-released IEEE-USA Salary & Benefits Salary suggests that engineers with machine-learning knowledge are making an average of $185,000 per year. That places them second among the survey’s top-paid engineering jobs; only engineers who specialized in smartphones and wearables made more ($215,771). (The survey was based on responses from 6,739 engineers employed full-time in their “primary area of technical competence.”)
On the other end of the scale, engineers specializing in robotics and automation only pulled down an average of $130,000. This is somewhat surprising, as automation is widely regarded as another “hot” category within tech, and it’s linked in many ways with machine learning.
Other studies have confirmed that machine learning is a tech category to pursue if you want to earn the big bucks. In March, for example, Indeed pegged the average machine learning engineer salary at $146,085, and its job growth between 2015 and 2018 at 344 percent.
Moreover, these specialized salaries skyrocket in the big tech hubs. According to an analysis that Dice ran late last year, machine learning experts could pull down an average of $165,760 in New York City, and $154,096 in San Francisco (and that doesn’t include other benefits and perks such as stock options and bonuses). This is clearly a skillset that employers want. But if you’re new to machine learning, how can you begin to educate yourself in its nuances?
Machine Learning Educational Resources
Fortunately, the current popularity of machine learning has translated into lots of online learning resources for novices and experts alike. For example, OpenAI, the kinda-nonprofit foundation (yes, it’s as odd as it sounds) that’s trying to create an ethical framework for A.I. development, hosts what it calls “Gym,” a toolkit for developing and comparing reinforcement algorithms, as well as a set of models and tools for training A.I. and ML. If that’s not enough, they also have a handy, very extensive tutorial in deep reinforcement learning (i.e., deep RL).
For those who are more advanced in their studies on this topic, there’s also Bloomberg’s Foundations of Machine Learning, a free online course that requires some advanced knowledge of mathematics (how’s your multivariate differential calculus these days?) but teaches some deep topics such as optimization, kernel methods, performance evaluation, and more.
But maybe you’re a total beginner when it comes to machine learning. That’s great! Everyone starts somewhere. Over at Hacker Noon, there is a very interesting breakdown of machine learning and artificial intelligence. Google also has a “crash course,” complete with 25 lessons and 40+ exercises, that’s well worth checking out (as designed, it should take roughly 15 hours to complete). Good luck!