I have a successful experience in working out data science tasks:

• business problem analysis, data science hypothesis formulation (Jupyter Notebooks);
• source data analysis, cleaning, preparing data for testing machine learning hypotheses (NumPy, Pandas, PyTorch, matplotlib);
• machine learning application for problems solving of forecasting, classification and regression (scikit-learn, PyTorch, TorchVision);
• optimization of neural networks, tuning computer vision models (PyTorch, TorchVision);
• accuracy analysis of applying machine learning models, optimization of meta-parameters (scikit-learn, matplotlib);
• registration of data science hypothesis testing results, recommendations for use (Jupyter Notebooks, matplotlib).

My many years of experience in business analysis allows me to solve full-cycle tasks (starting with the analysis of a business problem and ending with recommendations on the use of classification or forecasting results). In my profile you can see examples of my research:

• forecasting major man-made incidents on the plant;
• recognition of cells in microscope images. I completed 4 courses using Python for machine learning tasks, including “Deep Neural Networks with PyTorch” from IBM.