Update Part 1 - Introduction to Machine Learning with scikit-learn.md

Part 1 - Introduction to Machine Learning with scikit-learn.md

@@ -64,7 +64,7 @@ Thankfully, scikit-learn gives us a method for automatically splitting up our fu
 ```
 from sklearn.model_selection import train_test_split
 # random_state=42 seeds the random value with 42, meaning that everyone that runs this code will have the same accuracy.
-# Machine learning algorithms have a degree of randomness to them, which can be used by using the same random seed.
+# Machine learning algorithms have a degree of randomness to them, which can be mitigated by using the same random seed.
 # Disregard this if you don't know what that means.
 X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.50, random_state=42)
 ```