Merge pull request MicrosoftDocs#44 from mkrasmus/patch-1

Update 04 - Clustering.ipynb

04 - Clustering.ipynb

@@ -233,7 +233,7 @@
     "- Complete or Maximal linkage uses the maximum distance between the members of the two clusters.\n",
     "\n",
     "Several different distance metrics are used to compute linkage functions:\n",
-    "- Euclidian or l2 distance is the most widely used. This metric is only choice for the Ward linkage method.\n",
+    "- Euclidian or l2 distance is the most widely used. This is the only metric for the Ward linkage method.\n",
     "- Manhattan or l1 distance is robust to outliers and has other interesting properties.\n",
     "- Cosine similarity, is the dot product between the location vectors divided by the magnitudes of the vectors. Notice that this metric is a measure of similarity, whereas the other two metrics are measures of difference. Similarity can be quite useful when working with data such as images or text documents.\n",
     "\n",