Position: Junior Machine Learning Engineer/Data Scientist
Category: Data Science
English level: fluent
Years of experience: 0.5

- 3 months of internship in MobiDev: successfully solved CV, NLP+CV, DS tasks
Certificates:
- Python: Fundamentals and Applications (Stepik)
- Machine Learning (ITHillel)
- Automatic Machine Learning with H2O AutoML and Python
- Machine Learning with H2O Flow
- Python Distributed Computing Course: Multithreading in Python & More
- TensorFlow Serving with Docker for Model Deployment
- Transfer Learning for Food Classification
- A Crash Course in Data Science

Courses:
- Machine Learning (ITHillel)
- Data Science Fundamentals (DataRoot Labs)
- Machine Learning from Stanford University (Andrew Ng)
- Calculus (Khan Academy)
- CV+NLP Image captioning task on COCO and Flickr datasets with pretrained CNN in the encoder and custom RNN in the decoder (implemented with PyTorch, available on my github)
- CV image denoising task solving: providing a pipeline with a custom written autoencoder and dataset class (implemented with PyTorch)
- Classic Data Science classification problem with the tabular data (implemented with Scikit-Learn and Jupyter Notebook)
- Used cars price prediction with H2O AutoML (see on my github)
- STL-10 dataset Computer Vision problem (github)
- Regression problem on the tabular data (github)

Machine Learning course (ITHIllel):
- TensorFlow
- Neural Networks
- Siamese Networks
- Decision Trees
- Natural Language Processing (NLTK, Word2Vec)
- Principal Component Analysis
- Autoencoders
- Reccurent Neural Networks: LSTM, GRU
- Hyperparameters tuning
- Feature Engineering

Data Science Fundamentals course (DataRoot Labs):
- calculus
- Python basics and coding experience
- Supervised/Unsupervised Learning
- Linear, Polynomial, Logistic Regressions
- SVM
- KNN
- K-Means
- Naive Bayes
- Neural Nets
- Anomaly Detection
- Data analysis
- Data preprocessing
- Dispersion/Bias problem

Machine Learning (Andrew Ng)

Calculus and Statistics (Khan Academy)
I would be excited to work on any machine learning problem since I love either match or statistics as instruments of its solving.