3 minutes

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 about the topic we're interested in.
  • A model, which makes estimates.
  • An objective the model is trying to achieve.
  • An optimizer, which is the 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 to disk for later use.