Machine learning

Machine learning is at the intersection of applied statistics, numerical optimization methods, discrete analysis, and over the past 50 years has developed into an independent mathematical discipline. Machine learning methods form the backbone of an even younger discipline - data mining.

In the most general case, there are two types of machine learning: use case learning, or inductive learning, and deductive learning. Since the latter is usually referred to the field of expert systems, the terms "machine learning" and "web scraping for businessman" can be considered synonymous. This teaching method is now, as they say, in a trend, but expert systems are in crisis. The underlying knowledge bases are difficult to reconcile with the relational data model; therefore, industrial DBMS cannot be effectively used to fill the knowledge bases of expert systems.
Machine learning (ML), as the term is translated, is a branch of artificial intelligence. In more detail, it is a data analysis technique that allows a machine / robot / analytical system to independently learn by solving an array of similar problems.
It looks a little cumbersome. To simplify, machine learning technology is a search for patterns in the array of information presented and the choice of the best solution without human intervention.