There are several ways IT pros and business professionals can show off their knowledge of machine learning (ML) basics in a job interview. It all comes down to explaining simply and clearly how ML can be used to achieve business goals.
One of the most basic foundational elements of an ML engineer’s career is understanding the typical procedure used to build machine learning programs.
Professionals should have a qualified understanding of using test and training data sets, then introducing new data sets, fitting a model, and working toward convergence. For example, a professional should know the often labor-intensive process of developing those test and training sets, and thinking about where they come from, for example, from manual web scraping, which few people really want to do.
The professional should also know some of the major pitfalls in machine learning. One of the most prominent examples is problems with fitting or dimensionality, where a mismatch leads to programs that are difficult to use and do not support enterprise needs.
The professional should be “good with neural networks” and understand not only classical algorithms, but how weighted inputs are used with functions in these new types of AI/ML systems to create more accurate results. They should know how to control bias and variance and how to effectively move toward a convergent model.
Also, career pros should be conversant on the principles of ethical and explainable AI/ML. Experts have pointed out that in these days of rapid innovation, it's important to conquer what's called the “black box principle” where humans might not know what an ML program is doing, or why. The explainable AI movement works to solve these kinds of problems and promote ethical applications of artificial intelligence and machine learning in today's industries.
