Machine Learning, Text Mining, Explainable AI. Crash-Course.

A huge amount of information is presented in an unstructured form, and learning how to use it will, therefore, increase the efficiency of working with clients, increase sales, quickly respond to complaints, and be able to evaluate the results of marketing campaigns. **FREE CONSULTATIONS FOR ALL STUDENTS** Scientific articles related to this course can be found in Google Scholar by searching Maksym Lupei. Github: TMining repository. In addition, a competent analysis of information presented in text form (on the Internet, etc.) opens up additional opportunities for business growth and expansion. Data Mining and Text Mining technologies serve to achieve these goals. The key groups of Text Mining tasks are: text categorization, information extraction and information search, processing of changes in text collections, as well as development of information presentation tools for the user. Recently, text analysis has attracted more and more attention in various fields, such as security, commerce, and science. This course will cover basic techniques for extracting and analyzing textual data to discover interesting patterns, extract useful knowledge, and support decision-making, with an emphasis on statistical approaches that can generally be applied to arbitrary textual data in any ordinary language with zero or minimal human intervention. Detailed analysis of textual data requires understanding the text in plain language, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for a "superficial" but reliable analysis of textual data to search for patterns and uncover information. You will learn the basic concepts, principles and basic algorithms of intellectual text analysis and their possible application.