docs: Update Vowpal Wabbit - Contextual Bandits.ipynb (#1970)

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notebooks/features/vw/Vowpal Wabbit - Contextual Bandits.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "# Contextual-Bandits using Vowpal Wabbit\n",
     "\n",
-    "In the contextual bandit problem, a learner repeatedly observes a context, chooses an action, and observes a loss/cost/reward for the chosen action only. Contextual bandit algorithms use additional side information (or context) to aid real world decision-making. They work well for choosing actions in dynamic environments where options change rapidly, and the set of available actions is limite\n",
+    "In the contextual bandit problem, a learner repeatedly observes a context, chooses an action, and observes a loss/cost/reward for the chosen action only. Contextual bandit algorithms use additional side information (or context) to aid real world decision-making. They work well for choosing actions in dynamic environments where options change rapidly, and the set of available actions is limited.\n",
     "\n",
     "An in-depth tutorial can be found [here](https://vowpalwabbit.org/docs/vowpal_wabbit/python/latest/tutorials/python_Contextual_bandits_and_Vowpal_Wabbit.html)\n",
     "\n",
@@ -77,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2: Use VowalWabbitFeaturizer to convert data features into vector"
+    "## Step 2: Use VowpalWabbitFeaturizer to convert data features into vector"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "source": [
     "## Step 3: Train model\n",
     "\n",
-    "VowaplWabbitGeneric performs these steps:\n",
+    "VowpalWabbitGeneric performs these steps:\n",
     "\n",
     "* trains a model for each split (=group)\n",
     "* synchronizes accross partitions after every split\n",