update docs creation

make_docs.sh

@@ -6,6 +6,8 @@ cp section-2-data-science-and-ml-tools/introduction-to-pandas.ipynb docs/
 cp section-2-data-science-and-ml-tools/introduction-to-matplotlib.ipynb docs/
 cp section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb docs/
 cp section-4-unstructured-data-projects/end-to-end-dog-vision-v2.ipynb docs/
+cp section-3-structured-data-projects/end-to-end-heart-disease-classification.ipynb docs/ 
+cp section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb docs/
 cp communicating-your-work.md docs/
 cp images/* docs/images/
 

mkdocs.yml

@@ -13,7 +13,7 @@ nav:
   - Introduction to Scikit-Learn: "introduction-to-scikit-learn.ipynb"
   - Milestone Projects:
     - End-to-End Heart Disease Classification: "end-to-end-heart-disease-classification.ipynb"
-    - End-to-End Bulldozer Price Regression: "end-to-end-bluebook-for-bulldozers-price-regression-v2.ipynb"
+    - End-to-End Bulldozer Price Regression: "end-to-end-bluebook-bulldozer-price-regression-v2.ipynb"
   - Introduction to TensorFlow, Deep Learning and Transfer Learning: "end-to-end-dog-vision-v2.ipynb"
   - Communicating your work: "communicating-your-work.md"
 

section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb

@@ -5726,6 +5726,12 @@
     "\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",
     "\n",
     "For our case, we're going to start with `OrdinalEncoder`.\n",
     "\n",