Machine Learning
Machine Learning Projects
A hands-on machine learning repository where I used notebooks to work through the full rhythm of applied ML: cleaning data, building features, training models, tuning parameters, and interpreting results.
Period
May 2026
Stack
PythonJupyter NotebookScikit-learnNLPLDAGloVe
Motivation
The goal was to move beyond reading about algorithms and actually feel where models succeed, fail, and become hard to explain.
Links
Core Features
- Uses TF-IDF, embeddings, and classic ML workflows on text-heavy datasets.
- Explores topic modeling and semantic relationships through interpretable notebooks.
- Documents experiments in a way that is easy to rerun and compare.
My Contribution
- Implemented preprocessing, modeling, evaluation, and visualization workflows.
- Compared model behavior across different feature representations.
What I Learned
- Strengthened practical understanding of NLP pipelines and unsupervised learning.
- Learned how preprocessing decisions can matter as much as model choice.