Machine Learning

  • Programming Languages:

    Python 2, 3, C/C++, C#

  • Machine Learning Frameworks:

    Pytorch, TensorFlow, Keras, Caffe, ML.NET

  • Python data analysis stack:

    SciKit-Learn, Pandas, NumPy, StatsModels, Seaborn, SciPy, Matplotlib, Plotly

  • Domain-specific Technologies:

    Computer Vision, OpenCV, Dlib, Scikit-Image

  • Natural Language Processing:

    NLTK, PyMorfy, AllenNLP, Yargy, DeepPavlov

  • Tabular data / Time Series:

    CatBoost, XgBoost, LightGBM, Fbprophet, ARIMA

  • Data Warehouse:

    Amazon Redshift, Google BigQuery, Microsoft Azure, Snowflake

  • IDE:

    PyCharm, Jupyter Notebook, MATLAB, Microsoft VS, NVIDIA DIGITS

  • System and hardware tools:

    Docker, NVIDIA Docker, GPUs technologies (CUDA, cuDNN, CUDAmat)

  • Reporting tools:

    SQL, Looker, Jupyter, Microsoft Power BI, Qlik, Tableu, Google Data Studio

  • Project Tracking:

    Git, GitLab, Trello

  • Math Models:

    Classic, Regression, Random Forest, Support Vector Machines, Ensemble and Boosting methods

  • Neural Networks:

    Convolutional, Recurrent, Autoencoders, Generative Adversarial

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