• ✅ Data Visualization/Analytics: Power BI, Looker, Tableau, Matplotlib, Seaborn, Plotly, Streamlit
• ✅ Data Science: PyTorch, TensorFlow, Scikit-learn, Hugging Face, Transformers, OpenCV, NLTK, SpaCy
• ✅ Web Scraping: BeautifulSoup, Scrapy, Selenium
• ✅ Maths and Statistics: Statsmodels, SciPy
• ✅ Domains: Regression, Classification, NLP, LLM, RAG, Computer Vision, Time Series, Neural Networks, Ensemble Methods, PCA, Clustering, Dimensionality, Reduction, Anomaly Detection
• ✅ Data Engineering: dbt, Terraform, SQL, PySpark
• ✅ MLOps: MLflow, Prefect, Mage
• ✅ APIs: Flask, FastAPI
• ✅ Cloud Platforms: GCP, AWS, Azure

Currently working as Teaching Assistant for the Data Analytics and Data Science & AI Bootcamps @ Le Wagon and as AI Course Developer and Technical Editor @ Towards AI and open for further cooperation opportunities!  

👉 CONTACT ME! 👉 Fill in this form or reach out on LinkedIn!

• 🌱 Developed as a project leader a Computer Vision MLOps project FoodScore (summary) and its Website during the last 2 weeks of the Data Science Bootcamp of Le Wagon (March 2023)

• 🔭 After my graduation, I worked as a volunteer in the following Data Science Projects NLP and GIS project (Website) at Omdena

• 💰 My last personal Data Science/Engineering/Analysis and ML projects can be found in these repositories (feel free to click ⭐ if you like them 😎):

  • MLOps:
    • Car Price Prediction: MLOps project using MLFlow, Prefect, Flask, Docker, Grafana, Terraform, CI/CD and AWS
    • Taxi Rides Prediction: MLOps project using MLFlow, Prefect, FastAPI, Docker, and GCP
    • Music Clustering: MLOps project using FastAPI, Docker, CI/CD, AWS and deployment in Streamlit (see app)
  • Data Analysis + Modeling:
    • Cryptocurrencies Analysis: EDA and Modeling project: comparison of ARIMA, XGBoost, LSTM, and Prophet
    • News Classification: EDA, Modeling and Deployment project: comparison of several Neural Networks (CNN, RNN, feedforward) and Multinomial Naive Bayes models and deployment in Streamlit (see app)
    • Breast Cancer Classification: EDA and Modeling project: comparison of Random Forest using Sklearn and Spark, as part of the Advanced Data Science with IBM Specialization
    • Bank Churn Classification: EDA and Modeling project including univariate/bivariate analysis, feature engineering, baseline model selection and voting classifier (LGBMClassifier, XGBoostClassifier, and CatBoostClassifier)
  • Machine Learning & LLM:
    • Birds Classification: Computer Vision project using Pytorch EfficientNet models and deployment in Gradio (see app)
    • Q&A and Summarization: LLM project for audio and text extraction using Whisper and Langchain with app deployment using Streamlit (locally)
    • RAG Llama Index: RAG (Retrieval-Augmented Generation) project for QA retrieval using Llama Index and Deep Lake
    • RAG LangChain Ragas: RAG (Retrieval-Augmented Generation) project for QA retrieval using LangChain and evaluation with RAGAS
  • Data Engineering:
    • Hotel Reviews: Data Engineering project using Prefect, Spark, SQL, dbt, Terraform, Looker, CI/CD and GCP
    • Air Quality Switzerland: Data Engineering project using Mage, dbt, Terraform, Looker, CI/CD and GCP

• 💸 Additionally, you can find my Power BI projects:

  • Personal Finance: Analysis and Comparison of Income, Bills, Profits and Available Money
  • Product Sales Comparison: Product Sales Comparison using DAX functions

• Last but not least, I also have a Tableau portfolio using groups, sets, blends, joins, table calculations, storylines, parameters, animations, and other advanced functions