In today’s world, machine learning is playing an increasingly important role in various fields of endeavor. This field of artificial intelligence allows computer systems to learn and make autonomous decisions based on data without an explicit program task. Machine learning methods allow computers to process and analyze huge amounts of information, identify patterns and make predictions that are impossible for humans.
Basic concepts of machine learning
Machine learning is a branch of artificial intelligence (AI) that studies methods and algorithms that allow computer systems to automatically learn from data and make predictions or decisions without an explicit programming task. Unlike traditional programming, where the developer explicitly specifies the instructions used by the system, in machine learning, the model is trained based on the data provided, and the results of the training become the basis for further decisions.
There are several key concepts in machine learning that need to be understood.
- Datasets. Datasets are datasets used to train machine learning models. They consist of examples represented by a set of features and their corresponding target parameter. Datasets can be divided into training and test samples;
- Traits. Features represent parameters or aspects of the data that the model uses for prediction or classification. They can be numeric, categorical, or textual;
- Models. Machine learning models are mathematical algorithms or data structures that are trained on the data to make predictions or decisions;
- Prediction and categorization. Machine learning models can be used to predict numerical values or classify objects into specific categories. Prediction and classification are the main tasks of machine learning;
- Training and testing. Training a model is to tune it on a training dataset, and testing is to evaluate the performance of the model on a test set that the model has not seen during training;
- Overtraining and undertraining. During the process of training a model, it is possible to encounter problems of overtraining and undertraining. Overtraining occurs when the model memorizes the training data too well and is unable to work with the new information correctly, resulting in poor performance on the test set. Undertraining occurs when the model is not sufficiently trained on the training data, due to which it cannot subsequently achieve high performance.
Categories of machine learning
Learning with a teacher
Learning with a teacher is the process of training a model on labeled data, where each example has a corresponding label – the desired output of the model. The goal of the model is to find patterns in the data to predict labels for new, unknown examples.
In the field of teacher-guided learning, there is a wide range of methods and algorithms to solve different problems.
Support Vector Method (SVM). SVM is a powerful algorithm for classification and regression tasks. It constructs a hyperplane that separates examples of different classes with the largest gap.
Decision trees and random forest. Decision trees are a tree structure of solutions where each node contains a condition on one of the features of the data. A random forest is an ensemble of decision trees. They are widely used for classification and regression.
Neural Networks. A model based on the workings of the human brain. Neural networks consist of artificial neurons and the connections between them. They have been successfully used in a variety of applications including computer vision, natural language processing, and speech recognition.
Learning without a teacher
Teacherless learning is a branch of machine learning in which models analyze data and find hidden structures in it without pre-labeled labels. This approach can automatically extract information from large amounts of data, making it particularly useful when dealing with unstructured data such as images or audio recordings.