Machine learning and its methods. Practical application of AI in Ukrainian business

Alternatively, you know what you want to achieve, but there is no clearly defined method of achieving the goal. There are many different solutions, methods, algorithms and functions available on the market that can find practical application in companies in Eastern Europe. However, as the artificial intelligence company iTizzi advises, before deciding to invest in machine learning support tools and infrastructure, you should carefully analyze the planned decisions, determine which machine learning method is best for the requirements, what data will be used for training systems.

Machine learning is not artificial intelligence

The terms machine learning and artificial intelligence are often used interchangeably, but artificial intelligence technology has a much broader scope and includes technologies used, for example, for image and video recognition or natural language processing that does not require machine learning. Machine learning is primarily about creating dynamic patterns based on the analysis of large datasets that present results that seem “reasonable”, but in reality, the basis for obtaining them is the use of statistical analysis engines that work with high efficiency and at a large scale.

Basic Machine Learning Methods – iTizzi Software Development Company, Ukraine

Supervised learning

It’s okay if the user knows exactly what the system needs to learn. After providing a sufficiently large amount of data for training the system, checking the generated results, changing the predefined parameters, the system should be tested by entering a set of new, previously not provided data. This allows you to check if the results obtained are as expected. Supervised learning techniques are used for applications such as determining the level of financial risk associated with individuals and companies based on prior information about their activities. In addition, by using analysis of past behavior patterns, they can provide useful insights into how customers are likely to behave and what their preferences are.

Such technologies are increasingly being used in financial institutions. They can significantly speed up decision-making, reduce the risk and cost associated with analyzing customer applications, and better and faster tailor the offer to their individual requirements.

Automatic learning (unsupervised learning)

Automatic learning is a method for automatically analyzing big data for hidden patterns, relationships between various variables. It is used to classify and group data based on their statistical parameters. This method can be useful, among other things, in enterprises that need to integrate data from different sources, business units, and departments in order to get a consistent and complete view of customers.

It is also increasingly used to determine the feelings and emotional state of people based on the analysis of their social media posts, emails, and other published opinions. This allows, for example, an automatic customer satisfaction survey.

Semi-automatic training (semisupervised learning)

The partially supervised learning method is a combination of the techniques mentioned above. In this case, a small part of the data is marked, the results of their analysis are checked by the system, and then the trainee determines how they should be included in the rest of the set of information that forms the basis for creating models and behaviors. This method is used, among other things, to detect fraudulent attempts in online transaction systems using stolen personal data.

Leverage Artificial Intelligence and Machine Learning with iTizzi Software Development Company to deliver better customer experiences. iTizzi in Vinnytsia, Odesa offers the introduction of AI into financial transactions, increasing the flexibility and efficiency of financial analysis and planning with an intelligent management system. Accelerate analytics and improve productivity with specialized AI innovations built by iTizzi in Dnipro, Kyiv, and Lviv to help you make timely and risk-sensitive decisions.