Master's in Data Science Projects Repository

This repository is a structured archive of my projects and assignments completed during my Master's M1 in Data Science. Each directory corresponds to a specific course module and contains relevant notebooks, python scripts, and summaries.

Directory Structure

• Applied_Statistics: Laboratory notebooks and Python solutions focused on statistical methods in data science.
• AI_Logique_&_Contraintes: Contains exams, corrected TPs and theory summaries.
• Distributed_Systems_for_Massive_Data_Management: Summaries and basic usage examples of systems like MongoDB, Hadoop, Redis, Docker.
• Hands_on_NLP: Practical notebooks covering various Natural Language Processing techniques and algorithms.
• Hands_on_Scikit_Learn: Laboratory notebooks exemplifying the usage of Scikit Learn for machine learning tasks.
• History_AI: A curated list of book recommendations exploring the field of Artificial Intelligence.
• Information_Retrieval: Laboratory notebooks and presentations done during the course of Information Retrieval, focused on NLP processing, Ranking Documents and similarity comparison.
• TER: Notebooks, Python files, Documentation and Research Papers used during the Travail Encadré de Recherche (TER) in Qatent about Information Retrieval on Patents.
• Deep_Learning: Practical Notebooks + Theory Presentation Slides
• Large_Scale_Distributed_Data_Processing: Continuation of course Distributed_Systems_for_Massive_Data_Management. Summaries and basic usage examples of systems like Spark, Streaming Spark, GraphX, ElasticSearch.
• Machine_Learning_Algorithms: Recommended Books + Notebooks + Handwritten Notes
• Foundational_Principles_Machine_Learning: Python files and notebooks detailing foundational machine learning principle such as:
  1. Linear Regression & Gradient Descent
  2. Classification: Binary (Perceptron (ReLu) + Logistic Regression) & Multiclassification (Perceptron (Softmax))
  3. Overfitting & Regularization: Lasso, Ridge and ElasticNet
  4. Feature Maps & PCA
• NARLABS_Internship: Reports and Notes written during the 3-Months long internship in Narlabs/NCHC in Hsinchu, Taiwan.

Usage

Clone the repository to explore the materials:

git clone https://github.com/your-username/Master_M1_Data_Science.git

Dive into each folder to find detailed notebooks and scripts related to each topic. The History_AI folder diverges by offering literature to broaden the understanding of AI's evolution and impact.

Contribution

Your contributions are welcome! Please fork the repository, create a feature branch, and submit your pull request for review.