My experience

• Algorithms
• Applications
• Python libraries & APIs
• Conceptual knowledge

Algorithms

The list is to give an idea of the type of algorithms, which I have worked.

NLP

• Neural network (Bidirectional LTMS-Attention-Bidirectional LTMS) - Machine translation
• Neural network (CNN-GRU) - Trigger word detection
• Neural network (RNN-LTMS) - "Jazz music" generation
• Neural network (Embedding-LSTM) - Text to emoji

Image recognition

• Neural network - Cat Images classification
• Deep neural network (DNN) - Cat images classification
• Convolutional neural Network (CNN) - Cat images classification
• Neural network - "Thump up" image classification
• Convolutional neural network (CNN) - "Happy face" images classification
• Convolutional neural network (CNN) - "Thump up" image classification
• Convolutional neural network - "Digit" image recognizer. Tensorflow version. (Link)
• Convolutional neural network - "Digit" image recognizer. Pytorch version. (Link)

Linear regression

• Linear regression (LightGBM) - "Housing prices" estimator. (Link)
• Linear regression (XGBoost) - "Housing prices" estimator. (Link)

Classification & Segmentation

• SVM - "Human cell"-classification
• k-means clustering - Customer segmentation
• Density based(DBSCAN) clustering - "Weather stations" segmentation
• KNN/Decision Tree/SVM/Logistic regression - "Loan application"
• k-means clustering with Foursquare - Customer segmentation

Recommender systems

• Recommender System as Content based - "Movies"
• Recommender System as Collaborative Filtering - "Movies"

Applications

I add here few examples of sample applications, which I have worked and are available publicly.

• GPT-3 - Text generator
• GPT-Neo - Text generator
• COVID Daily Statistics

Libraries

• NLP: Open AI API, HuggingFace, SpaCy, NLTK, regex
• MLOps: MLflow
• Deep learning: Tensorflow/Keras, Pytorch
• Xgboost, LightGBM
• Visualization libraries experience: Dash, Seaborn, Matplotlib, Bokeh
• Web frameworks: * Flask example and integrating it with SQLite, Jinja2 and Bokeh-visualizations.
• Data manipulation: Numpy, Pandas, JSON - example
• Other libraries: Folium, Geopy, Foursquare-location data with k-means clustering - Neighborhood segmentation

Conceptual knowledge

The below articles are available in Medium. This entry is especially useful, in case you are interested in the latest NLP research.

• 30.06.2021 - "NLP project"
• 06.10.2020 - "Perception in NLP"
• 22.04.2020 - "Natural Language Processing research, benchmarks and models"
• 20.04.2020 - "Image recognition"
• 23.12.2019 - "Bokeh"
• 23.12.2019 - "Learning Methodologies for Data Science"
• 10.09.2019 - "Do I need a database?"
• 07.09.2019 - "Python Packages: SQLite"
• 01.08.2019 - Python Packages: Flask
• 30.07.2019 - Curated Data Science resources
• 15.07.2019 - "JSON"
• 15.07.2019 - "Python packages: Foursquare Places"
• 15.07.2019 - "Python packages"
• 17.06.2019 - "Madrid office location"