Typo: Ooe-hot -> one-hot

notebooks/05.04-Feature-Engineering.ipynb

@@ -86,7 +86,7 @@
     "It turns out that this is not generally a useful approach in Scikit-Learn: the package's models make the fundamental assumption that numerical features reflect algebraic quanitites.\n",
     "Thus such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford - Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense.\n",
     "\n",
-    "In this case, one proven technique is to use *Ooe-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively.\n",
+    "In this case, one proven technique is to use *one-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively.\n",
     "When your data comes as a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you:"
    ]
   },