CodeVox: Machine Learning Fundamentals
Building intuition for datasets, features, labels, train-test splits, model evaluation, and overfitting.
Building intuition first
This CodeVox session introduced machine learning as a workflow for learning patterns from data. The focus was on understanding the role of each step before moving into more advanced algorithms.
Topics covered
- Differences between traditional programming and machine learning
- Datasets, features, labels, and train-test splits
- Supervised learning through a simple example
- Accuracy, confusion matrices, validation, and overfitting
From data to evaluation
The demonstration followed a small dataset through preparation, training, and evaluation. Keeping the example compact made it possible to discuss why a high score alone does not guarantee a useful model.
What I learned
Teaching this topic improved how I separate model terminology from the decisions behind it. Starting with the question and data made the evaluation metrics easier to explain.