Data Analyst turned ML Engineer, on a focused path into AI and deep learning. Six months ago I was managing operations for a non-profit. Today I'm building neural networks from scratch and deploying ML applications.
Currently working through deep learning fundamentals and reinforcement learning — with the long-term goal of working at the frontier of AI development.
| Project | Description | Stack | Demo |
|---|---|---|---|
| House Price Predictor | End-to-end ML app — data cleaning, model selection, deployed predictor | Python, Streamlit, scikit-learn | Live |
| Sales Dashboard | Interactive analytics dashboard with filters, KPIs, and visualisations | Python, Streamlit, Plotly | Live |
| Neural Net from Scratch | Perceptron and MLP built in pure NumPy — no ML libraries | Python, NumPy | — |
| LSTM Slogan Model | Two LSTM models: slogan generator and slogan classifier | Python, TensorFlow, Keras | — |
| Power BI Sales Analysis | Star schema data model, 15+ DAX measures, $8.9M recovery identified | Power BI, DAX | — |
- Fast.ai Practical Deep Learning (Part 1)
- Reinforcement Learning — Sutton & Barto
- Implementing ML algorithms from scratch
Documenting the journey on Dev.to
