Put simply, machine learning is the art of making a computer do useful things without explicitly coding it for it. More specifically, machine learning is the acquisition of new knowledge through an artificial system. Like a human, the computer independently generates knowledge from experience and can independently find solutions to new and unknown problems.
For this purpose, a computer program analyzes examples and uses self-learning algorithms to identify patterns and patterns in the data. The goal of machine learning is to intelligently link data together, recognize relationships, draw conclusions, and make predictions.
How does machine learning work in principle?
In principle, similar to human learning. Just as a child learns that certain objects can be seen on pictures, so too can a computer “learn” to identify objects or distinguish persons. For this purpose, the learning software is first fed with data and trained. For example, programmers tell the system that one object is “one dog” and another is “no dog.”
As it progresses, the learning software constantly receives feedback from the programmer, who uses the algorithm to adapt and optimize the model: With each new record, the model gets better and can finally clearly distinguish dogs from non-dogs.
In order for the software to learn independently and to find solutions, a prior action by humans is necessary. For example, the systems must first be provided with the data and algorithms relevant for learning. In addition, rules must be set up for the analysis of the data stock and the recognition of the patterns. If matching data is available and rules are defined, machine learning systems can:
- Find, extract and summarize relevant data
- Make predictions based on the analyzed data,
- Calculate probabilities for specific events,
- to adapt to developments independently and
- Optimize processes based on recognized patterns.