This repository contains materials and implementations I created while auditing or completing various coding courses, such as CS50 and other advanced topics in machine learning (ML) and natural language processing (NLP). The repository serves as a structured collection of algorithms, techniques, and hands-on coding exercises for reference and further learning.

📂 algorithms/ This directory includes Jupyter notebooks (.ipynb) and Python scripts (.py) for implementing key machine learning and data science algorithms. The collection covers:

• Supervised Learning: Decision Trees, k-Nearest Neighbors (KNN), etc.
• Unsupervised Learning: K-means Clustering (PyTorch implementation), etc.
• Other ML Algorithms: Various techniques studied and practiced.

📂 NLP/ This directory contains implementations, exercises, and notes related to a Natural Language Processing (NLP) course I completed. It includes: Text preprocessing (tokenization, stemming, lemmatization), Word embeddings (Word2Vec, GloVe, Transformers), etc.

The repository serves as a personal knowledge base for algorithmic implementation and ML/NLP exploration. Each script/notebook is documented with explanations and usage examples.

I plan to expand this repository with additional concepts, projects, and optimizations as I progress in my research and coursework.