What is Machine Learning

The general term "Machine Learning" or "machine learning" refers to a set of mathematical, statistical and computational methods for developing algorithms that can solve a problem not in a direct way, but based on the search for patterns in a variety of input data. The solution is calculated not according to a clear formula, but according to the established dependence of the results on a specific set of features and their values. For example, if every day during the week the ground is covered with snow and the air temperature is significantly below zero, then it is most likely that winter has come. Therefore, machine learning is used for diagnostics, forecasting, recognition and decision making in various applied fields: from medicine to banking. If you would like to consult about machine learning and AI, you can contact here https://www.dataart.com/services/ai-and-ml

Types and essence of Machine Learning
There are 2 types of machine learning:

1) Inductive or by precedents, which is based on the identification of empirical patterns in the input data;
2) Deductive, which involves the formalization of expert knowledge and their transfer into digital form in the form of a knowledge base.

The deductive type is usually attributed to the field of expert systems, so the general term "machine learning" means learning by precedents. Cases or training samples are sets of input objects and their corresponding results. At the same time, there is no clear formula that analytically describes the relationship between results and inputs. For example, what will the weather be like tomorrow if during the week the days were frosty, sunny, with low air humidity, without wind and precipitation? In this case, many more parameters should be taken into account: geographical coordinates, terrain, movement of warm and cold air fronts, etc. It is necessary to build an algorithm that will give a fairly accurate result for any possible input. The accuracy of the results is governed by the estimated quality functional. Thus, the decision is formed empirically, based on the analysis of accumulated experience. At the same time, the learning system must be capable of generalization - an adequate response to data that goes beyond the limits of the existing training sample. In practice, input data can be incomplete, inaccurate, and heterogeneous. Therefore, there are many methods of machine learning. We can say that machine learning implements the Case Based Reasoning (CBR) approach - a method of solving problems by reasoning by analogy, by making assumptions based on similar cases (precedents).

Machine Learning Methods
There are many machine learning methods. We will list the most popular ones, leaving their detailed classification to specialized resources. There are 2 types of classical Machine Learning:

1) With a teacher (supervised learning), when it is necessary to find the functional dependence of the results on the inputs and build an algorithm that accepts a description of the object at the input and issues an answer at the output. The quality functional, as a rule, is determined through the average error of the algorithm's responses for all objects in the sample. Supervised learning includes classification, regression, ranking, and prediction problems.
2) Without a teacher (unsupervised learning), when answers are not given, and you need to look for dependencies between objects. This includes the tasks of clustering, searching for association rules, filtering outliers, building a trust region, reducing dimensionality, and filling in missing values.

Non-classical, but very popular methods include reinforcement learning, in particular, genetic algorithms, and artificial neural networks. The pairs “situation, decision made” act as input objects, and the answers are the values ​​of the quality functional, which characterizes the correctness of the decisions made (environment reaction). These methods are successfully applied to the formation of investment strategies, automatic control of technological processes, self-learning of robots and other similar tasks.

Machine Learning Implementers
Today, the most commonly used languages ​​for creating machine learning programs are R, Python, Scala, and Julia [4]. They are supported by many integrated development environments, in particular, R-Studio, R-Brain, Visual Studio, Eclipse, PyCharm, Spyder, IntelliJ IDEA, Jupyter Notebooks, Juno, etc. [4]. In our practical courses, we will teach you how to successfully work with these tools, so that later you can independently form input data sets, build effective algorithms for solving applied problems in your field: from the oil and gas industry to stock analytics.