docs(examples): add intro and install instructions

examples/Preprocess your data with recipes.ipynb

@@ -7,7 +7,33 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "..."
+    "In this article, we'll explore [`Recipe`](/reference/core.html#ibis_ml.Recipe)s, which are designed to help you preprocess your data before training your model. Recipes are built as a series of preprocessing steps, such as:\n",
+    "\n",
+    "- converting qualitative predictors to indicator variables (also known as dummy variables),\n",
+    "\n",
+    "- transforming data to be on a different scale (e.g., taking the logarithm of a variable),\n",
+    "\n",
+    "- transforming whole groups of predictors together,\n",
+    "\n",
+    "- extracting key features from raw variables (e.g., getting the day of the week out of a date variable),\n",
+    "\n",
+    "and so on. If you are familiar with [scikit-learn's dataset transformations](https://scikit-learn.org/stable/data_transforms.html), a lot of this might sound familiar and like what a transformer already does. Recipes can be used to do many of the same things, but they can scale your workloads on any [Ibis](https://ibis-project.org/)-supported backend. This article shows how to use recipes for modeling.\n",
+    "\n",
+    "To use code in this article, you will need to install the following packages: Ibis, IbisML, and your modeling library.\n",
+    "\n",
+    "```bash\n",
+    "pip install 'ibis-framework[duckdb,examples]' ibis-ml [scikit-learn | 'xgboost[scikit-learn]' | skorch torch]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4377a22e-f751-4a12-b07a-c6b31e3c48d9",
+   "metadata": {},
+   "source": [
+    "## The New York City flight data\n",
+    "\n",
+    "Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:"
    ]
   },
   {
@@ -203,16 +229,6 @@
     "weather"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "4377a22e-f751-4a12-b07a-c6b31e3c48d9",
-   "metadata": {},
-   "source": [
-    "## The New York City flight data\n",
-    "\n",
-    "Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,