20 ноября

python pandas numpy scikit pytorch tensorflow keras english
Английский: eng: Intermediate

— Good knowledge in machine learning i.e. random forest, xgboost, clustering algorithms, dimensionality reduction(PCA, t-SNE). A good foundation in basic statistics and linear algebra
— Strong knowledge of Python
— Time-series analyses
— Anomaly detection
— Comprehensive knowledge of the Python data analysis ecosystem (Pandas, Numpy, Scikit-learn, etc.);
— At least minor experience with python visualization tools(matplotlib/seaborn, Plotly)
— Experience with following neural network architectures: LSTM, GRU and other RNN-based
— Strong practical experience with Deep Learning frameworks like PyTorch, MXNet, Tensorflow, or Keras
— Upper-intermediate level of English mandatory

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