Machine learning is one of the important components of the technology traditionally referred to as artificial intelligence. Machine learning uses statistical methods to learn independently, that is, without direct instruction from project developers and without the use of programming.
The basis for machine learning is algorithms, which are a complex set of mathematical models and rules from which AI analyzes information, draws conclusions, makes decisions and predictions.
Machine learning methods
- Supervised learning is a method of using algorithms on the labeled training data. Artificial intelligence is trained on a prepared database and then extrapolates from the experience to predict situations in new scenarios.
- Unsupervised learning is a method of using algorithms on an unprepared database. Artificial intelligence analyzes the information and independently identifies structures, relationships, and patterns.
- Reinforcement learning is a method when algorithms learn in a certain environment on the basis of their own actions by trial and error. In this case, the principle of reward maximization is relevant, when the machine strives to get the best result based on the experience gained.