diff --git a/docs/data-science/algorithms/supervised/classification.md b/docs/data-science/algorithms/supervised/classification.md
index 58d11582..c93da573 100644
--- a/docs/data-science/algorithms/supervised/classification.md
+++ b/docs/data-science/algorithms/supervised/classification.md
@@ -102,7 +102,7 @@ squares, logistic regression typically uses Maximum Likelihood Estimation
likelihood of the observed data.
-
+ Lo and behold, even more math...
diff --git a/docs/data-science/algorithms/supervised/tree-based/cart.md b/docs/data-science/algorithms/supervised/tree-based/cart.md
index a44a7101..e9137630 100644
--- a/docs/data-science/algorithms/supervised/tree-based/cart.md
+++ b/docs/data-science/algorithms/supervised/tree-based/cart.md
@@ -66,10 +66,7 @@ which is a classic binary classification task.
Excited for some theory?
-
+
## Theory
@@ -461,10 +458,7 @@ data well.
Now get to the point!
-
+
In practice, you have to find the right parameters to balance model complexity
diff --git a/docs/data-science/algorithms/unsupervised/clustering.md b/docs/data-science/algorithms/unsupervised/clustering.md
index c3345822..abebe542 100644
--- a/docs/data-science/algorithms/unsupervised/clustering.md
+++ b/docs/data-science/algorithms/unsupervised/clustering.md
@@ -371,10 +371,7 @@ cluster_indices = kmeans.fit_predict(X)
Now to the fun part!
-
+
The goal of this exercise is to recommend a song based on a previous track. The
diff --git a/docs/data-science/data/preparation.md b/docs/data-science/data/preparation.md
index 868dd491..9c75c51c 100644
--- a/docs/data-science/data/preparation.md
+++ b/docs/data-science/data/preparation.md
@@ -370,8 +370,7 @@ With `#!python index=False`, we do ==not==
> [pandas `to_csv()` docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html)
-
-
+
Congratulations! 🎉 You have finally completed your quest to merge the
data.
diff --git a/docs/data-science/data/preprocessing.md b/docs/data-science/data/preprocessing.md
index b516e973..285f488d 100644
--- a/docs/data-science/data/preprocessing.md
+++ b/docs/data-science/data/preprocessing.md
@@ -121,10 +121,10 @@ print(data.isna().sum().sum())
The output once more indicates that the whole data set has `#!python 0` missing
values. So far so good, but this is not the end of the story (who saw that
-coming 🤯).
+coming :exploding_head:).
