Verified Expert in Engineering
Machine Learning Developer
Mikhail has a degree in computer science and received a certification from MADE: Academy of Big Data. He has over 10 years of overall experience handling complex data in finance across different industries. Mikhail has two years of machine learning and data science experience with a focus on neural networks, particularly CV and NLP.
MacOS, Visual Studio Code (VS Code), Git, PyTorch, Pandas, Deep Learning, Machine Learning, NumPy, Seaborn
The most amazing...
...model I've developed is an end-to-end solution for a car plate recognition of real life pictures of cars. It incorporates advanced CV and NLP techniques.
Data Science Engineer
- Developed an MVP of analytical service and took part in the initial hypothesis testing process.
- Developed a cloud-based real-time data analysis system for data gathered from sensors installed on agricultural equipment.
- Ensured that models used in the system provide a good basis for the data-driven decision-making process of our clients.
- Took part in the launch of the whole system with real customers (each customer is an agricultural business).
Chief Finance Officer
- Served as the head of the financial department of a medium-sized IT company.
- Improved the entire financial reporting process in the group with subsidiaries in three different countries.
- Communicated with external auditors in the jurisdictions where the audit is obligatory.
- Gathered information from a number of sensors installed on the agriculture equipment (plows, cultivators, tractors): GNSS data, accelerometers, gyroscopes, etc.
- Worked with this data as a time series. This step involves filtering out noise from the data and normalize the data across the time axis (different sensors have different data frequencies).
- Analyzed the data using heuristics alongside decision trees and linear classifiers
- Prepared reports for management using Seaborn and Plotly
- Used PostgreSQL as storage for raw, normalized, and processed data
- Used pandas, NumPy, and GeoPy to process the data
I also proposed and implemented a novel technique based on a few papers from https://arxiv.org/. We used multimodal learning for the generated image quality improvement. Particularly, we built representations in one feature space for texts and images and then trained the model using triplet loss to classify (text, image) pairs.
Later on, this classifier was used as an additional term in the generator loss.
Car Plate Recognizer
I made a pipeline which consisted of the following parts:
1) Mask R-CNN (https://arxiv.org/abs/1703.06870), a model that detects all car plates presented in the photo.
2) Preprocessing of car plate images using OpenCV library, adjusting blur and the contrast of the images in order to normalize them before passing them to OCR.
3) Char-RNN (https://arxiv.org/abs/1706.01069) a model specifically designed for OCR.
4) An additional step was made to increase the quality of OCR based on the knowledge of the car plate's text structure. I implemented and trained a language model based on the Beam Search.
This model achieved a Levenshtein Mean of 1.05 on the test dataset. As the company disclosed afterward, the test dataset contained car plates from different regions, so test data had a different distribution from the train data.
Management, Data Science, RESTful Development
Computer Vision, Natural Language Processing (NLP), EDA, Complex Data Analysis, Finance, GPT, Generative Pre-trained Transformers (GPT), Deep Learning, Machine Learning, Deep Reinforcement Learning, Statistics, Bayesian Statistics, Applied Mathematics, Artificial Intelligence (AI), Discrete Mathematics, Calculus, Probability Theory, Generative Adversarial Networks (GANs), OCR
PyTorch, Pandas, NumPy, REST APIs
Git, Seaborn, Amazon Simple Queue Service (SQS)
MacOS, Visual Studio Code (VS Code), Amazon Web Services (AWS)
Bachelor's Degree in Informatics and Applied Mathematics
South Federal University - Rostov-on-Don, Russia
MADE: Big Data Academy
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