Over the past 15 years, machine learning (ML) has become widespread, but most people do not fully understand its role in everyday life. Many of us use applications based on artificial intelligence (AI) and machine learning technologies on a daily basis. These technologies have already revolutionized many industries, for example, contributed to the emergence of virtual assistants such as Siri or the Salyut family of virtual assistants (Sber, Joy, Athena), allowed traffic forecasting using Google Maps.
Machine learning is a specialized way of teaching computers without resorting to programming. In part, this is similar to the process of teaching an infant, which learns to independently classify objects and events, to determine the relationship between them.
ML opens up new possibilities for computers to solve problems previously performed by humans, and teaches a computer system to make accurate predictions when entering data. It stimulates the growth of the potential of artificial intelligence, being its indispensable assistant, and in the minds of many, even a synonym.
Finally, machine learning is one of the most common forms of artificial intelligence used by modern businesses. If a company is not yet using ML, then in the near future it will probably assess its potential, and AI will become the main engine of the IT strategy of many enterprises. After all, artificial intelligence is already playing a huge role in transforming the development of the IT industry: customers are paying more attention to intelligent applications in order to grow their business using AI. It applies to any workflow implemented in software – not only within the traditional business part of enterprises, but also in research, manufacturing processes and, increasingly, the products themselves.
Note: At the international conference on artificial intelligence and data analysis Artificial Intelligence Journey (AI Journey), President of Global Sales, Marketing and Operations of Microsoft Jean-Philippe Courtois said that the COVID-19 pandemic has spurred interest in the use of machine learning: 80% of companies are already implementing it in their activities, and 56% plan to increase investment in this area. Data analytics consultancy.
The extraordinary success of machine learning has led AI researchers and experts to choose this method by default for solving problems today.
Machine Learning: principles and objectives. There are three equally important components at the heart of machine learning:
Data. Collected in all sorts of ways. The more data, the more effective machine learning and the more accurate the future result.
Signs. Determine what parameters machine learning is based on.
Algorithm. Choosing a machine learning method (assuming you have good data) will affect the accuracy, speed, and size of the finished model. (https://idigtexas.com/)
The existence and development of machine learning was based on three main principles:
Innovativeness: ML opportunities open up new prospects for the development and growth of almost all sectors of the economy.
Specificity: machine learning is used exclusively for the implementation and development of new products by people who understand IT technologies.
Simplicity: products sold using machine learning technologies become understandable even for schoolchildren and elderly people. Machine learning services.
The problems that machine learning can solve directly determine the benefits for business and the possibilities for solving social problems by states of different countries. The main tasks include:
Regression. Provides a forecast based on a selection of objects with different characteristics. Based on the results of data analysis, the output is a number or a numeric vector. For example, this is how credit scoring works – an assessment of the creditworthiness of a potential borrower.
Classification. Identifies categories of objects based on the available parameters. Continues the tradition of machine vision, so you can often find the term “pattern recognition”: for example, the identification of wanted people from a photo or on the basis of a verbal description of their appearance.
Clustering. Divides data into similar categories based on a unifying feature. For example, space objects are clustered by distance, size, type, and other characteristics.
Identification. Separates the data with the specified parameters from the rest of the data array. For example, it is involved in making a medical diagnosis based on a set of symptoms.
Forecasting. It works with volumes of data for a certain period and predicts, based on the analysis, their value after a given period of time. An example is a weather forecast.
Extraction of knowledge. Explores the relationship between a number of indicators of the same phenomenon or event. For example, it finds patterns in the interaction of exchange indicators.
As you can see, the range of machine learning tasks is wide, which confirms its promise for use by both commercial enterprises and social projects.