Very minor edits.
This folder contains examples and best practices, written in Jupyter notebooks, for explaining and
interpreting models. Being able to explain and understand machine learning models does not only help
-guiding further model improvements. More importantly, it helps gaining user's trust of the models and
-detect biases caused by the training data.
+guiding further model improvements. More importantly, it's critical for gaining user's trust of the
+models and detecting biases caused by the training data.
## Summary
