diff --git a/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb b/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb
index 22dbd4f5b..981ce4eda 100644
--- a/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb
+++ b/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb
@@ -5724,14 +5724,12 @@
     "\n",
     "There are several encoders available for different use cases.\n",
     "\n",
-    "TK - does this table show up?\n",
-    "\n",
-    "| **Encoder** | **Description** | **Use case** | **For use on** |\n",
-    "|:-------------|:-----------------|:--------------|:----------------|\n",
-    "| [LabelEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder) | Encode target labels with values between 0 and n_classes-1. | Useful for turning classification target values into numeric representations. | Target labels. |\n",
-    "| [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#onehotencoder) | Encode categorical features as a [one-hot numeric array](https://en.wikipedia.org/wiki/One-hot). | Turns every positive class of a unique category into a 1 and every negative class into a 0. | Categorical variables/features. |\n",
-    "| [OrdinalEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#ordinalencoder) | Encode categorical features as an integer array. | Turn unique categorical values into a range of integers, for example, 0 maps to 'cat', 1 maps to 'dog', etc. | Categorical variables/features. |\n",
-    "| [TargetEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html#targetencoder) | Encode regression and classification targets into a shrunk estimate of the average target values for observations of the category. Useful for converting targets into a certain range of values. | Target variables. |\n",
+    "| Encoder | Description | Use case | For use on |\n",
+    "| ----- | ----- | ----- | ----- |\n",
+    "| [`LabelEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder) | Encode target labels with values between 0 and n_classes-1. | Useful for turning classification target values into numeric representations. | Target labels. |\n",
+    "| [`OneHotEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#onehotencoder) | Encode categorical features as a [one-hot numeric array](https://en.wikipedia.org/wiki/One-hot). | Turns every positive class of a unique category into a 1 and every negative class into a 0. | Categorical variables/features. |\n",
+    "| [`OrdinalEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#ordinalencoder) | Encode categorical features as an integer array. | Turn unique categorical values into a range of integers, for example, 0 maps to 'cat', 1 maps to 'dog', etc. | Categorical variables/features. |\n",
+    "| [`TargetEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html#targetencoder) | Encode regression and classification targets into a shrunk estimate of the average target values for observations of the category. | Useful for converting targets into a certain range of values. | Target variables. |\n",
     "\n",
     "For our case, we're going to start with `OrdinalEncoder`.\n",
     "\n",