small text fix

02_vector_data_preparation/02_vector_preparation.ipynb

@@ -615,7 +615,7 @@
    "source": [
     "## 7. Feature Scaling\n",
     "\n",
-    "Feature Scaling is one of the most important data preparation steps. This is to avoid biasing algorithms that compute distances between features (e.g. like KNN, SVM and other non-treebased) towards numerically larger values. Feature scaling also helps the algorithm to train and converge faster.\n",
+    "Feature Scaling is one of the most important data preparation steps. This is to avoid biasing models that compute distances between features (e.g. like KNN, SVM and other non-treebased) towards numerically larger values. Feature scaling also helps the algorithm to train and converge faster.\n",
     "The most popoular scaling techniques are normalization and standardization. Both scale the values of the current cell based on all given other cells, this means that scaling has to be done before train/test split to avoid bias towards unseen data. Apply to test set afterwards.\n",
     "\n",
     "## 7.1 Normalization or min-max scaling \n",