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taeefnajib/README.md

taeefnajib

Header

Hey there, I'm Taeef! πŸ‘‹

Data Scientist

I'm a Data Scientist, AI Enthusiast and Pythonista with a mission to drive innovation through intelligent solutions and contribute meaningfully to AI research.

πŸ’‘ What drives me: Turning complex data challenges into elegant solutions that make a real-world impact.


πŸ›  Core Tech Arsenal

  • Language:
    • Python
    • SQL
    • Spark
  • Core ML Expertise:
    • 🎯 Supervised Learning: Classification, Regression with advanced ensemble methods
    • πŸ” Unsupervised Learning: Clustering, Anomaly Detection
    • ⏱️ Time Series: Forecasting with Prophet, ARIMA & SARIMA
    • 🎬 Recommender Systems: Collaborative & Content-based filtering
    • πŸ—οΈ Feature Engineering: Advanced preprocessing and feature extraction
    • πŸ“Š Model Evaluation: Cross-validation, hyperparameter tuning (Optuna, GridSearch)
    • πŸ”¬ Explainable AI: SHAP, LIME, Facets for model interpretability
  • Advanced Libraries: XGBoost β€’ CatBoost β€’ LightGBM β€’ PyCaret β€’ Prophet

🧠 Deep Learning & Neural Networks

  • Architectures: ANN, CNN, RNN, LSTM
  • Framework: PyTorch for production-ready models

πŸ—£οΈ Natural Language Processing

  • Libraries: SpaCy, NLTK, Transformers

πŸ‘οΈ Computer Vision

  • Tools: OpenCV, SAM, YOLO for object detection and image processing

πŸš€ MLOps & Deployment

  • Production Pipeline:
    • πŸ”„ Version Control: DVC for data versioning. Git/GitHub for collaborative development
    • πŸ“ˆ Experiment Tracking: MLFlow for model management
    • 🐳 Containerization: Docker for scalable deployments
    • 🌐 API Development: FastAPI, BentoML for REST APIs
    • πŸ“± Web Apps: Flask, Streamlit, Gradio for interactive dashboards
    • 🐳 Containerization: Docker for scalable model deployment
    • πŸ”„ CI/CD: GitHub Actions for automated ML pipelines

πŸ“Š Data Visualization and Analytics

  • Power BI
  • DAX
  • Tableau
  • Superset

πŸ—οΈ Data Engineering

  • Data Pipeline & Orchestration:
    • πŸ”οΈ Data Warehouse: Snowflake for scalable cloud data warehousing
    • πŸ”„ ELT/ETL: Meltano, Airbyte for data integration and extraction
    • πŸ› οΈ Data Transformation: dbt for analytics engineering and data modeling
    • πŸ“… Workflow Orchestration: Dagster, Apache Airflow for pipeline management

☁️ Cloud Platforms

  • Cloud Infrastructure:
    • Amazon Web Services (AWS): EC2, S3, Lambda, SageMaker, RDS
    • Google Cloud Platform (GCP): Compute Engine, BigQuery, Cloud ML Engine

🀝 Let's Connect!

I'm always excited to collaborate on innovative projects and discuss the latest in AI/ML! :

LinkedIn

"Data is the new oil, but insights are the refined fuel that powers innovation."
πŸ’« Always learning, always building, always innovating! πŸ’«

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