Identifying Machine Learning Solvable Problems
machine learning is a field of learning patterns through data without any need for explicitly programming or hand-written rules. It’s a subfield of artificial intelligence (AI) and computer science.
Before machine learning became mainstream, programmers wrote rules derived from a function of their domain knowledge, observation of some hand-picked instances, and the business requirement to perform a particular task. But this legacy way of delivering business results suffered some evident constraints.
- Hand-written rules are limited by the knowledge of what edge cases a programmer can cover. This concept is very well explained by one of the most highly cited papers in the world of psychology titled “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information.”
- Commonly cited as Miller’s law, the paper describes the limited amount of information an average brain can hold and how it becomes unmanageable with the increasing number of variables and dimensions.
- Data is dynamic by nature and has become more so over the last decade with the proliferation of technology in our day-to-day lives. The varying data patterns fed to static pre-written rules are of little help to the business in taking meaningful actions. That is where the pattern mining ability of machine learning algorithms is put to the best use.