Machine learning is a field of artificial intelligence (AI) concerned with developing algorithms and models that can learn from data, make predictions, and make decisions without programming.
Principles of machine learning
Machine learning is based on a number of principles that make it work:
- Data. At the heart of ML is the use of data. Training data provides the model with information about input features and corresponding correct answers. The more diverse, qualitative, and representative the data, the better the model will be able to learn, recognize patterns, and make correct predictions on new data;
- Model. Represents an algorithm or mathematical function that transforms input data into output data. The model is chosen depending on the problem and the type of data. It can be linear, decision tree, neural network, etc. One of the key goals of machine learning is to create models that are able to produce accurate predictions for new data that have not been previously applied to the learning process;
- Training. The training process consists of fitting the model to the training data. The model analyzes the data, identifies patterns, and adjusts its internal parameters so as to minimize the error between the model’s predictions and the correct answers. Learning can occur with a teacher (with correct answers), without a teacher (no correct answers), or with reinforcement (with rewards or punishments). Instead of explicit programming, models derive knowledge from data and adjust their parameters to achieve performance;
- Automation. ML seeks to automate processes and data-driven decision making without the need for explicit human intervention. ML algorithms are capable of performing complex tasks with great speed and accuracy;
- Evaluation and Testing. After training the model, it is necessary to evaluate its performance on new data. This is done by using a test dataset that the model has not seen during training. Evaluation is done using metrics that measure accuracy, completeness, F1-measure, and other characteristics of the model. This allows us to evaluate how the model performs and determine if further refinement is needed;
- Generalization. A model in ML must be able to make accurate predictions or decisions on new, previously unknown data. This property is called generalization. A good model is able to generalize knowledge, identify common patterns, and apply them to new situations;
- Regularization and complexity management. When a model becomes complex, there is a risk of overfitting, where the model adapts well to training data but generalizes poorly to new data. Regularization techniques such as L1 and L2 regularization are used to control the complexity of models.
