This repository includes:
- Previous programmes written during my Master's (2023-24) for electrochemical and structural analysis of Na-ion cathodes
- Practice of and notes on key machine learning techniques, through:
- Geron's "Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow"
- DeepLearningAI Labs
- Kaggle Competitions
- Current focus on developing grasp of ensemble methods
- Notes on AI Theory from Andrew Ng & DeepLearning.AI's Machine Learning, NLP, and Deep Learning Specialisations
- Technical practice of fundamental Python skills through:
- University of Helsinki's Python Programming MOOC
- Advent of Code
- Exercism
- Work on Na-ion batteries is documented in Na_ion_independent_research. This includes pre-processing of existing databases (primarily Gou et al. (2024)'s recent release) and tentative attempts at building basic random forest models for predictive Na-ion modelling. However, I am for the next few weeks most focused on building a solid foundation in decision tree and random forest systems, before attempting any further application to the Na-ion context.