AI Workshop (Training)

Engine Failure Detection with Artificial Intelligence and Machine Learning

Instructor:Dr. Bassem Ben Hamed

Email: bassem.benhamed@enetcom.usf.tn 

Dr. Bassem BEN HAMED Titulaire d’un doctorat en mathématiques obtenu en 2006 à l’Université Paul Sabatier de Toulouse, il a également décroché un diplôme d’habilitation universitaire en 2013 à l’Université de Sfax. Actuellement, il est professeur en mathématiques appliquées à l’École Nationale d’Électronique et de Télécommunications de Sfax. Certifié Instructeur senior en intelligence artificielle par Huawei, il possède également des certifications d’instructeur en apprentissage automatique, apprentissage profond, MLOps et Big Data , délivrées par divers organismes accrédités. Co-fondateur et data scientist chez DataCamp Training and Consulting, il est également co-fondateur et responsable d’équipe au sein de Digital Innovation Partner, une cellule du Swiss Digital Network.

A mathematics Ph.D. holder, he earned his doctoral degree from Paul Sabatier University in Toulouse in 2006. In 2013, he obtained his university habilitation diploma from the University of Sfax. Currently, he serves as a professor of applied mathematics at the National School of Electronics and Telecommunications in Sfax. He holds senior instructor certifications from Huawei in artificial intelligence and is also certified as an instructor in machine learning, deep learning, MLOps and Big Data, from various accredited organizations. Additionally, he is a co-founder and data scientist at DataCamp Training and Consulting. He is also a co-founder and team manager at Digital Innovation Partner, a cell of the Swiss Digital Network.

• Objectives

At the end of this one-day hands-on training, participants will be able to:

    - Understand the fundamentals of Artificial Intelligence and Machine Learning applied to predictive maintenance

    - Master the complete lifecycle of an ML project: from data exploration to deployment

   - Implement engine failure detection models using real-world datasets

   - Evaluate and optimize the performance of classification models

   - Apply best practices in preprocessing, feature engineering, and validation

   - Interpret results and translate technical insights into business recommendations

• Practical Information

    - Duration: 1 day (6 hours)

    - Level: Intermediate

    - Prerequisites: Basic knowledge of Python and statistics

    - Language: English / French

•   Environment Setup

   - Anaconda Installation 

1. Download Anaconda from the official website:
   🔗 https://www.anaconda.com/download
   Choose the Python 3.11+ version for your operating system.

2. Install Anaconda (follow the installation wizard using the default options).

3. Verify the installation by opening Anaconda Prompt or a terminal and checking the versions of Conda and Python. 

  - Additional Packages to Install

           Install the following packages: scikit-learn, pandas, numpy, matplotlib, seaborn, plotly, xgboost, lightgbm, and imbalanced-learn.

• Key Takeaways

    - Hands-on experience with real-world predictive maintenance datasets

    - Application of Machine Learning models for fault detection

    - Best practices in industrial data preprocessing and model evaluation

    - Insights on deploying and monitoring ML models in production

• Fees

    - 250 TND: with accommodation

    - 150 TND: without accommodation

 Note: Priority will be given to conference participants.

• Registration

Don’t miss the opportunity to join our workshop! Please register using the following link: Click here to register

 Discover all workshop details and download the full program Click here to download.