diff --git a/python_scripts/cross_validation_ex_01.py b/python_scripts/cross_validation_ex_01.py
index 8fd6e4ea8..bf332c1e9 100644
--- a/python_scripts/cross_validation_ex_01.py
+++ b/python_scripts/cross_validation_ex_01.py
@@ -45,7 +45,7 @@
 # exercise.
 #
 # Also, this classifier can become more flexible/expressive by using a so-called
-# kernel that makes the model become non-linear. Again, no requirement regarding
+# kernel that makes the model become non-linear. Again, no undestanding regarding
 # the mathematics is required to accomplish this exercise.
 #
 # We will use an RBF kernel where a parameter `gamma` allows to tune the
diff --git a/python_scripts/cross_validation_sol_01.py b/python_scripts/cross_validation_sol_01.py
index 78518309c..4221c75e7 100644
--- a/python_scripts/cross_validation_sol_01.py
+++ b/python_scripts/cross_validation_sol_01.py
@@ -39,7 +39,7 @@
 # exercise.
 #
 # Also, this classifier can become more flexible/expressive by using a so-called
-# kernel that makes the model become non-linear. Again, no requirement regarding
+# kernel that makes the model become non-linear. Again, no understanding regarding
 # the mathematics is required to accomplish this exercise.
 #
 # We will use an RBF kernel where a parameter `gamma` allows to tune the
diff --git a/python_scripts/linear_regression_non_linear_link.py b/python_scripts/linear_regression_non_linear_link.py
index ca88b8799..155175a20 100644
--- a/python_scripts/linear_regression_non_linear_link.py
+++ b/python_scripts/linear_regression_non_linear_link.py
@@ -37,7 +37,7 @@
 
 # %% [markdown]
 # ```{tip}
-# `np.random.RandomState` allows to create a random number generator which can
+# `np.random.RandomState` allows creating a random number generator which can
 # be later used to get deterministic results.
 # ```
 #
@@ -172,6 +172,7 @@ def fit_score_plot_regression(model, title=None):
 # of the absolute values of the differences between the features generated by
 # both methods and checking that it is close to zero:
 
+# %%
 np.abs(polynomial_expansion.fit_transform(data) - data_expanded).max()
 
 # %% [markdown]