Dr. Chemseddine Berbague

is researching.

Angestellt, Associate professor, ESTIN, BéJaia, Algeria
Bejaia, Algerien

Fähigkeiten und Kenntnisse

Data Science
Research and Development
Machine Learning
Data Analysis
Big Data
Computer Science
Documentation
Artificial intelligence
Knowledge management
Scientific report
Software Development
Coaching
Recommender Systems
Database
XML
ML
Deep learning
Web applications
Agile Development
TensorFlow
Python
Programming Language
IT Application Management
Java
Team work
dynamic

Werdegang

Berufserfahrung von Chemseddine Berbague

  • Bis heute 3 Jahre und 6 Monate, seit Feb. 2022

    Associate professor

    ESTIN, BéJaia, Algeria

    As an Associate Professor at ESTIN, my role encompassed the following responsibilities: • Documentation and Knowledge Sharing. • Course Development. • Research and Application. • Industry Collaboration.

  • 1 Jahr und 6 Monate, Okt. 2020 - März 2022

    Data Management Engineer

    Nechma Technology

    As an Engineer and Business Analyst, I collaborated with a team to assess the company's needs and propose effective solutions for automating information processes. Through my project work, I gained valuable experience in Python, XML, business analysis, and teamwork. Specifically, my team and I focused on: • Accounting Module Development. • Configurable Reporting Solutions. • Data Verification Automation. • User Interface Improvement.

  • 1 Jahr, Okt. 2019 - Sep. 2020

    Freelance developer

    Freelancer

    As a freelance developer, I specialized in creating dynamic web applications and desktop applications using a variety of modern technologies. My experience included: • Frameworks. • State Management. • Database Integration. • Java.

Ausbildung von Chemseddine Berbague

  • 5 Jahre und 6 Monate, Jan. 2016 - Juni 2021

    PhD in Computer Science

    Badji Mokhtar, Annaba, Algeria

    Thesis Title: Studying the Recommendation Novelty and Diversity in Collaborative-based Filtering Approaches. – Summary: The objective of our proposal is to improve the quality of recommendation from different aspects by developing a scalable optimization algorithm that deals with the conflicting combination of different recommendation quality measurements: diversity, novelty, and relevancy.

Sprachen

  • Französisch

    Fließend

  • Arabisch

    Muttersprache

  • Englisch

    Fließend

  • Deutsch

    Grundlagen

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