Projects / Independent
Independent Projects & Experiments
This section reflects work I pursue independently to experiment, solve problems, and continuously learn new approaches across machine learning and AI.
It brings together explorations where I test ideas, work with new techniques, and deepen my understanding through hands-on problem solving, without being tied to immediate production use.
Areas of independent exploration
Exploration Area
Machine Learning Foundations
Building and refining core modelling intuition across structured data problems, with a focus on feature engineering, evaluation, and model behaviour.
Includes
- Regression and classification
- Tree-based models and boosting
- Feature engineering and model comparison
Exploration Area
Deep Learning & Advanced Models
Exploring neural architectures and sequence-based approaches for problems where traditional models may not be sufficient on their own.
Includes
- Neural networks and sequence models
- LSTMs and transformers
- Representation learning and advanced modelling
Exploration Area
AI & Language Systems
Working with modern AI systems including embeddings, semantic search, prompting, and language-model driven workflows.
Includes
- LLMs and prompting strategies
- Embeddings and semantic retrieval
- Knowledge-driven AI workflows
Exploration Area
Insight & Analytics
Using analysis, dashboards, and structured reporting to turn raw data into clearer understanding and more usable decisions.
Includes
- Exploratory analysis
- Dashboards and reporting
- Business-facing analytical narratives
A note on this work
Independent work plays a different role in my overall approach. It gives me space to explore unfamiliar techniques, work through new kinds of problems, and keep learning through direct experimentation.
Over time, this creates a useful loop. Experimentation sharpens intuition, intuition improves judgment, and that judgment carries back into more structured, real-world problem solving.