Fixed typos in canned_estimators.ipynb

Made changes in lines 623 and 692

Author: synandi

site/en/guide/migrate/canned_estimators.ipynb

@@ -620,7 +620,7 @@
         "id": "B1qTdAS-VpXk"
       },
       "source": [
-        "Create a TensorFlow dataset. Note that Decision Forests support natively many types of features and do not need pre-processing."
+        "Create a TensorFlow dataset. Note that Decision Forests natively support many types of features and do not need pre-processing."
       ]
     },
     {
@@ -689,7 +689,7 @@
         "id": "Z22UJ5SUqToQ"
       },
       "source": [
-        "Gradient Boosted Trees is just one of the many decision forests algorithms avaiable in TensorFlow Decision Forests. For example, Random Forests (available as [tfdf.keras.GradientBoostedTreesModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) is very resistant to overfitting) while CART (available as [tfdf.keras.CartModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel)) is great for model interpretation.\n",
+        "Gradient Boosted Trees is just one of the many decision forests algorithms available in TensorFlow Decision Forests. For example, Random Forests (available as [tfdf.keras.GradientBoostedTreesModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) is very resistant to overfitting) while CART (available as [tfdf.keras.CartModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel)) is great for model interpretation.\n",
         "\n",
         "In the next example, we train and plot a Random Forest model."
       ]