Wording as per Olivier's comments

python_scripts/01_tabular_data_exploration.py

@@ -360,9 +360,10 @@
 # We made important observations (which will be discussed later in more detail):
 #
 # * if your target variable is imbalanced (e.g., you have more samples from one
-#   target category than another), you may need special techniques for training
-#   and evaluating your machine learning model;
-# * having redundant (or highly correlated) columns can be a problem for some
-#   machine learning algorithms;
+#   target category than another), you may need to be careful when interpreting
+#   the values of performance metrics;
+# * columns can be redundant (or highly correlated), which is not necessarily a
+#   problem, but may require special treatment as we will cover in future
+#   notebooks;
 # * decision trees create rules at constant values of the features, such as
 #   vertical and horizontal lines in a 2-dimensional feature space.