The words “machine learning” have been imbued with an almost magical aura. Ordinary people don’t teach machines to learn. That’s for highly specialized alchemists, like data scientists, who transform data into gold in research divisions and labs with little explanation beyond simply saying, “Science.”
Of course, it may be a little known fact, but over the years machine learning tools have evolved to a point where almost anyone with a bit of pluck and drive can push a button and start some machine on a path to learning something valuable. It’s not exactly a snap, but the hard work of corralling the data and turning it into actionable insights has been automated enough that smart people with some motivation can do it themselves.
This slow renaissance has been driven by the reality that many non-programmers in the business world are already pretty savvy with data. Spreadsheets loaded with numbers are the lingua franca of decision makers at all levels of business and machine learning algorithms also like data in tables with cleanly defined rows and columns. To dispel a little magic, the new tools for machine learning are essentially just another collection of strategies and options for turning tabular data into useful answers.
The strength of the tools is in their ability to handle the grungy work of collecting data, adding structure and consistency where possible, and then starting the calculation. They simplify the data gathering process and the grind of keeping the information in rows and columns.
The tools, alas, are not yet smart enough to do all of this learning for you. You do have to ask the right questions and look in the right places. But the tools accelerate the search for answers so you can cover more ground, look behind more doors, and poke around in more crevices.
Credit: Google News