A curated set of tools, frameworks, and technologies I use to bring ideas to life — efficiently and effectively
Python
Core language for ML, data analysis, and scripting.
SQL
Used for querying, cleaning, and transforming structured data.
Git
Version control for tracking code changes and experiments.
Github
Hosting and documenting codebases and projects.
VS Code
Lightweight editor for Python, ML, and Git workflows.
PyTorch
Building and training deep learning and graph models.
Scikit-learn
Traditional ML, feature selection, and evaluation.
Pandas
Data wrangling, analysis, and preprocessing.
Numpy
Numerical computations and array operations.
OpenCV
Image preprocessing and computer vision tasks.
OpenAI APIs
Integrating generative models and LLM-based workflows.
Matplotlib / Seaborn
Visualizing data and model performance.
Plotly
Creating interactive data visualizations for the web.
Apache Spark
Distributed processing for large-scale datasets.
Google Dataproc
Manage Spark environment for cloud workflows.
AWS
Running compute instances, storing data, and experimenting with SageMaker.
Google Colab
Notebook-based model prototyping with GPU support.
Notion
Project planning, documentation, and knowledge management.
Obsidian
Personal knowledge base for technical notes and research.
Airflow
Scheduling and managing ETL pipelines and batch jobs.
Linux (WSL)
Local Linux environment for ML development on Windows.
Docker
Containerizing environments and ML pipelines.
Jupyter Notebooks
Interactive development and rapid experimentation.