What are some of the foundational ways that career pros stand out in machine learning?

Machine learning success often requires a combination of skills and experiences. Going into detail about some of these principles and skill sets helps individuals to better understand what companies are looking for when they hire machine learning professionals.

In a very basic sense, you could say that machine learning success often rests on a threefold principle – programming, mathematics and insight. Each of these three things is fundamentally different, but each of them plays a role in developing a career professional as a machine learning expert.

From the programming end, knowing languages like Python and R becomes tremendously useful, but there are also crossover skills from languages like COBOL, Perl and Ruby on Rails that can have some value. Part of that is because of the fundamental nature of programming – that you’re dealing with routing the operations and values where they need to be. Then there are also machine learning projects that take advantage of legacy code.

The second fundamental principle is mathematics. People with advanced mathematical skills or acumen often have much more success in the machine learning world. When they look at neural networks or other models, they are able to break down the mathematical equations that lead to the network outputs. People often talk about neural networks being “black boxes” even to technicians – but to the extent that you’re savvy in mathematics, you can start to journey toward a better understanding of what the program is doing.

That leads to the third principle, which is insights. Understanding probabilistic statistics really helps in machine learning success. That’s because with machine learning, projects are moving from a purely deterministic or linear programming zone into a new probabilistic zone. Individuals who are more savvy about probability can look at weighted inputs and better predict what results might be. However, in another sense, people who are intuitively wise about machine learning will understand how to limit its applications to things that make sense.

One of the big five pitfalls in machine learning today is the rampant and indiscriminate application of machine learning into enterprise applications. There are many situations where machine learning just isn’t a good solution – whether it’s because of system complexity, overfitting, the black box problem previously mentioned, or anything else. Some of the most valuable professionals in the machine learning space will be those who know how to choose projects well – how to curate machine learning applications – and how to handle buy-in and procedure as a skilled consultant.