01/04/2024 updated
100 % available
Data scientist
Tunisia
Tunisia
engineer in AI & data scienceArtificial IntelligenceAlgorithmsData AnalysisApplied MathematicsGitHubInnovation ManagementPython (Programming Language)Linear AlgebraMachine LearningMathematicsNumPyTensorFlowStatisticsData ScienceDeep Learning
I am Ouni Mohamed Amine, a Data Scientist based in Tunisia, holding a degree in Applied Mathematics and possessing advanced training in data science and artificial intelligence. My proficiency in Python, including libraries such as NumPy, Pandas, and TensorFlow, is reinforced by a strong foundation in statistics and linear algebra.
My passion for data analysis and machine learning is demonstrated through my personal projects on GitHub (https://github.com/Amine136). I have completed projects based on deep learning architectures such as GAN, CNN, and RNN. A solid understanding of mathematical concepts gives me a strong grasp of algorithms and tools for the analysis and creation of machine learning models.
Feel free to contact me to discuss how I can contribute to your project. I am excited about the prospect of collaborating and creating innovative solutions.
My passion for data analysis and machine learning is demonstrated through my personal projects on GitHub (https://github.com/Amine136). I have completed projects based on deep learning architectures such as GAN, CNN, and RNN. A solid understanding of mathematical concepts gives me a strong grasp of algorithms and tools for the analysis and creation of machine learning models.
Feel free to contact me to discuss how I can contribute to your project. I am excited about the prospect of collaborating and creating innovative solutions.
Languages
ArabicNative speakerEnglishGoodFrenchGood
Project history
AI Data Structuring Project
• Project Objective: Extract essential information from images of individuals, including eye color, gender, age prediction, presence of makeup, identification of tattoos, and sentiment analysis (happy or sad).
• Methodology: Development of a set of Convolutional Neural Network (CNN) models aiming for at least 70% accuracy. Creation of a user-friendly web interface to facilitate interaction with the model.
• Training and Evaluation: Training the CNN model on Google Colab with data storage in Google Drive.
• Project Objective: Extract essential information from images of individuals, including eye color, gender, age prediction, presence of makeup, identification of tattoos, and sentiment analysis (happy or sad).
• Methodology: Development of a set of Convolutional Neural Network (CNN) models aiming for at least 70% accuracy. Creation of a user-friendly web interface to facilitate interaction with the model.
• Training and Evaluation: Training the CNN model on Google Colab with data storage in Google Drive.