Course Summary

Congratulations on completing Data Preparation and Feature Engineering for Machine Learning!

You should now have a better understanding of how to:

  • Recognize the relative impact of data quality and size to algorithms.
  • Set informed and realistic expectations for the time to transform the data.
  • Explain a typical process for data collection and transformation within the overall ML workflow.
  • Collect raw data and construct a dataset.
  • Sample and split your dataset with considerations for imbalanced data.
  • Transform numerical and categorical data.

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Data Preparation and Feature Engineering for Machine Learning