Machine learning is a branch of Artificial Intelligence that concerns the creation of systems that learn based on the data used and as a result of the activities and tasks performed.
Ultimately, machine learning algorithms use mathematical methods that allow them to learn directly from data, developing adaptive behavior such that their performance improves as the examples from which to learn increase.
We can say, then, that machine learning allows computers and systems to learn from experience, just as human beings do.
In the computer suit, in this case, instead of writing the entire programming code, only a set of data is provided to the machine, which is then processed through algorithms to perform the required task.
The first person to use the term “Machine Learning,” was in 1959 Arthur Lee Samuel, a scientist in the field of artificial intelligence, although a more precise definition was not given until later by Michael Mitchell, department head at Carnegie Mellon University:
“a program is said to learn from experience E with reference to some class of task T and with performance measurement P, if its performance in task T, as measured by P, improves with experience E.”
Machine learning is essentially based on two approaches, theorized as early as the late 1950s by Lee Samuel:
- Supervised learning in which you give the machine complete examples to use as directions;
- Unsupervised learning in which you let the program do the work without any particular guidance.
Today machine learning and artificial intelligence are revolutionizing marketing, which is increasingly data driven.
In a special way, there are many benefits to be derived from this approach:
- accurate audience analytics on customers and prospects for profiling and segmentation activities;
- real-time customer experience personalization;
- optimization of campaigns and activities;
- constant monitoring of performance.
