I’m on fire for the Machine Learning and AI progress alltime.
My goal is to be a valuable gear in a data-driven team that pushes the edges of what’s possible. I’m pumping up my ML & DL developer’s skills in a constant manner and I aim to be a professional with a top-notch stuff and AI approaches like it’s my best friends.
The rich analytical background joined with the deep skills in mathematics, statistics, and algebra along with the solid knowledge and real long experience in Machine Learning and Deep Learning algorithms and its approaches, allow me to design, develop, and maintain topnotch ML models and AI nets to crackle any obstacles for better business solutions and profitability.
KEY SKILLS:
• Machine Learning • Deep Learning (incl. Transformers) • Graph ML & Analytics • Statistics •
• Data Science • Data Wrangling • Data Mining • Data Analysis • Data Visualization •
• Community detection• Dimensionality Reduction • Anomaly Detection •
• Classification • Regression • Clustering • Statistical modeling •
• Predictive modeling • Fraud detection •
TECHNICAL SKILLS AND TOOLS:
– DL: • Transformers • Natural Language Processing (NLP) • Recurrent nets (RNN, GRU, LSTM) • Convolutional nets (CNN) • Sequential MLP.
– ML: • Random Forests (Decision Trees) • Gradient Boosting • Logistic Regression • DBSCAN Clustering • Principal Component Analysis (PCA) • Ensembles.
– MLOps: • Mlflow • Amazon SageMaker • Google Vertex AI • Dataiku.
– Frameworks: • TensorFlow (Keras API) • Scikit-Learn.
– Languages: • Python • SQL • Cypher QL.
– Libraries: • Pandas • XGBoost • LigthGBM • Graph Data Science • Fast API.
– Databases: • Neo4j Graph DB • PostgreSQL • SQL Server.
– Visualization: • Looker Studio • NeoDash • Apache Superset • MS PowerBI • Matplotlib • Plotly.
– Development: • Git • Jira • Agile.
ADDITIONAL SOLID EXPERIENCE WITH:
• Problems: Sequences & Time Series forecasting, Customer segmentation, Churn prediction.
• Languages: R-language, M-language (MS PowerQuery), DAX (Data Analysis eXpressions).
• Platforms: KNIME Analytics, H2O.ai, Weka.
• Algorithms: Autoencoders, Self-Organizing maps (SOM), Support vector machines (SVM), k-Nearest Neighbors (kNN).
• Libraries: Natural Language Toolkit (NLTK).
• Visualization: Tableau, QlikView, Oracle BI.