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mostaphaelansari/README.md

Hi πŸ‘‹, I'm EL ANSARI Mostapha

🧠 Machine Learning Engineer & AI Enthusiast

Dedicated Machine Learning practitioner with a strong foundation in AI/ML fundamentals and hands-on experience in implementing deep learning solutions. Passionate about leveraging AI to solve real-world problems, with a particular focus on Natural Language Processing and Computer Vision applications. Currently exploring the fascinating world of Large Language Models and their practical applications.

πŸ”¬ Current Focus & Learning Journey

  • Primary Focus: Building and fine-tuning Large Language Models for specific domains
  • Research Interests: Transformer architectures, Few-shot learning, Model optimization
  • Learning Path: Advanced MLOps practices, Production-grade ML systems
  • Side Projects: Contributing to open-source ML projects, Experimenting with state-of-the-art models

πŸ› οΈ Technical Stack

Machine Learning & Deep Learning

PyTorch TensorFlow scikit-learn Hugging Face OpenCV NumPy Pandas

Programming Languages

Python C++ R

Development & Deployment

Docker FastAPI Google Cloud Git Linux

πŸš€ Featured Projects

Text Summarization Engine

  • Implemented BART and T5 models for automatic text summarization
  • Fine-tuned models on domain-specific data achieving 35% ROUGE-L score improvement
  • Developed streamlined pipeline for data preprocessing and model training
  • Tech stack: PyTorch, Transformers, FastAPI

Computer Vision Application

  • Built an object detection system using YOLOv5 architecture
  • Created custom dataset and trained model achieving 82% mAP@.5
  • Deployed model using Docker and FastAPI
  • Implemented real-time inference optimization

Sentiment Analysis Tool

  • Developed BERT-based sentiment classifier for social media analysis
  • Created data pipeline processing 100K+ tweets daily
  • Achieved 89% accuracy on multi-class sentiment classification
  • Implemented model quantization reducing inference time by 40%

πŸ“Š GitHub Analytics

πŸ“ˆ Contribution Insights

GitHub Stats Most Used Languages

πŸ” Profile Summary

Detailed GitHub Contributions Summary

πŸ”₯ Streak Stats

Top Languages GitHub Stats
GitHub Streak

🌱 Continuous Learning

  • Currently Reading: "Deep Learning" by Ian Goodfellow
  • Online Courses: Advanced MLOps on Coursera, Full Stack Deep Learning
  • Technical Blogs: Regular contributor on Medium about ML/AI topics
  • Competitions: Active participant in Kaggle competitions

🀝 Connect & Collaborate

LinkedIn Kaggle Medium Hugging Face

πŸ“š Research Areas of Interest

  • Transfer Learning & Few-shot Learning
  • Natural Language Processing
  • Computer Vision & Image Processing
  • Model Optimization & Compression
  • MLOps & Production ML Systems

πŸ“š Repository Categories

πŸ€– Machine Learning

ML Algorithms Deep Learning NLP Projects

πŸ› οΈ ML Engineering

MLOps Model Deployment Data Pipeline

πŸ“Š Data Science

Data Analysis Visualization Kaggle Solutions

πŸ“« Contact: elansarimostapha011@gmail.com

"The best way to predict the future is to implement it" πŸš€


πŸ’‘ Always open to collaborating on interesting ML/AI projects and research opportunities!

Pinned Loading

  1. aymaneelfahsi1/UnsupervisedML_GAN_TransUNET_SegmentationCovid aymaneelfahsi1/UnsupervisedML_GAN_TransUNET_SegmentationCovid Public

    Jupyter Notebook

  2. COVID-19-Global-Dashboard- COVID-19-Global-Dashboard- Public

    This interactive COVID-19 Global Dashboard is a comprehensive analytics platform built with Streamlit, providing real-time insights into the global COVID-19 pandemic. The dashboard offers a wide ra…

    Python 1

  3. streamlit-image-augmentation-app streamlit-image-augmentation-app Public template

    Image Data Augmentation App is a user-friendly and powerful tool designed to perform image augmentation on multiple images simultaneously. Built with Streamlit and PyTorch, this app enables data sc…

    Python

  4. Learning-by-doing Learning-by-doing Public

    Python

  5. LLM-Powered-Document-Q-A-with-Llama-3.2 LLM-Powered-Document-Q-A-with-Llama-3.2 Public

    A Retrieval-Augmented Generation (RAG) application that enables intelligent document analysis and question answering using Llama 3.2. Built with Streamlit, Langchain, and Ollama.

    Python 1