Summary
We covered some significant new jargon in this module. Let’s recap what we've learned:
The goal of machine learning is to find patterns in data and use these patterns to make estimates.
Machine learning differs from normal software development in that we use special code, rather than our own intuition, to improve how well the software works.
The learning process conceptually uses four components:
- Data, information we want to learn from.
- A model, which makes estimates about the data.
- An objective the model is trying to achieve.
- An optimizer, extra code that changes the model depending on its performance.
Data can be thought of as features, and labels. Features correspond to potential model inputs, while labels correspond to model outputs, or desired model outputs.
Pandas and Plotly are powerful tools to explore datasets in Python.
Once we have a trained model, we can save it to disk for later use.