Опис навчальної дисципліни

The purpose of teaching the discipline is to provide students with knowledge of basic models of machine learning; types of machine learning; data preparation and processing process; selection of significant features of data, their transformation; the process of deploying machine learning models and wrapping them in an application programming interface; introduction to machine learning libraries for Python 3: numpy, scikit-learn.

The main objectives of the discipline: to teach students to use Python to create models and algorithms for machine learning; to acquaint students with classical and current algorithms of machine learning and data processing; provide understanding of models based on the integration of mathematical knowledge and software code; to give skills for application of the received knowledge in designing of standard decisions on the basis of machine learning.

According to the requirements of the educational and professional program, students must:

know: the process of creating a “production line” of machine learning – from data processing to model deployment; basics of data processing, their transformation and preparation for machine learning models; typical algorithms for detecting and evaluating data features; basics of learning models and evaluating their effectiveness; typical software tools for machine learning; theoretical and practical material according to the course program: mathematical bases of machine learning models, software implementations, etc.

be able to: apply the mastered material to create prototypes of machine learning models; use Python for software implementation of machine learning models, their testing and evaluation, as well as determine the type of machine learning task: regression, classification and clustering and solve the problem based on the acquired knowledge and skills.