Added basic one-hot encoding

docs/source/automl/pipeline_search.ipynb

@@ -20,7 +20,7 @@
    "source": [
     "## How it works\n",
     "\n",
-    "EvalML selects and tunes machine learning pipelines built of numerous steps. This includes missing value imputation, feature selection, feature scaling, and finally machine learning. As EvalML tunes pipelines, it uses the objective function selected and configured by the user to guide its search. \n",
+    "EvalML selects and tunes machine learning pipelines built of numerous steps. This includes encoding categorical data, missing value imputation, feature selection, feature scaling, and finally machine learning. As EvalML tunes pipelines, it uses the objective function selected and configured by the user to guide its search. \n",
     "\n",
     "\n",
     "At each iteration, EvalML uses cross-validation to generate an estimate of the pipeline's performances. If a pipeline has high variance across cross-validation folds, it will provide a warning. In this case, the pipeline may not perform reliably in the future.\n",

docs/source/automl/regression_example.ipynb

@@ -245,7 +245,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

docs/source/automl/search_results.ipynb

@@ -811,7 +811,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,