This project implemented the Multinomial Naive Bayes classifier to solve the NLP problem of text (message) classification into spam or ham categories. In this classifier, problems such as "arithmetic overflow" and "unknown words" were resolved. Model training was implemented using parallelization technology.

This project implemented recording and generation of signals, signal processing using discrete Fourier transforms, concatenation of signals, plotting of signals and discrete Fourier transforms, and playback of signals through speakers. One of the implementations of DFT and iDFT was implemented using multicore processing.

This project implemented the website layout based on a previously created design in Figma. The website was developed using the BEM (Block Element Modifier) methodology, and CSS was written using the SCSS preprocessor.

Coursework on the Basics of Artificial Intelligence in the 5th semester. This project implemented Natural Language Processing (NLP) with the "bag-of-words" method, a feedforward neural network with two hidden layers, and the development of a Telegram bot for communication.

Coursework on Programming Methods in the 3rd semester. This project implemented the Bellman-Ford algorithm for finding the shortest path in a graph. Additionally, code optimization was performed to reduce the algorithm's execution time.

Coursework on Programming in the 2nd semester. This project implemented the following algorithms as classes: Caesar Cipher, Affine Cipher, Vigenère Cipher, Playfair Cipher, Polybius Cipher, and Hill Cipher.