ML/AutoML: Slight changes for requirements

topic/machine-learning/automl/automl_timeseries_forecasting_with_pycaret.ipynb

@@ -107,26 +107,32 @@
    "source": [
     "## Getting started\n",
     "\n",
-    "First, install the required dependencies. \n",
-    "\n",
-    "```bash\n",
-    "pip install -r requirements.txt\n",
-    "```\n",
+    "First, install the required dependencies. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install -r requirements.txt\n",
     "\n",
+    "# In an environment like Google Colab, please use the abolute path of requirements.txt:\n",
+    "#!pip install -r https://raw.githubusercontent.com/crate/cratedb-examples/main/topic/machine-learning/automl/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "**Note:** As of time of this writing, PyCaret requires Python 3.8, 3.9 or 3.10.\n",
     "\n",
     "Second, you will need a CrateDB instance to store and serve the data. The \n",
     "easiest way to get started is to use the free CrateDB cloud offering. You can \n",
     "sign up for a free account at https://console.cratedb.cloud and \n",
     "[deploy a cluster].\n",
     "\n",
-    "[deploy a cluster]: https://cratedb.com/docs/cloud/en/latest/tutorials/deploy/stripe.html#deploy-cluster\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "To populate your environment with corresponding database access credentials,\n",
     "create an `.env` file with the following content:\n",
     "\n",
@@ -140,6 +146,7 @@
     "\n",
     "You can find your CrateDB credentials in the [CrateDB Cloud Console].\n",
     "\n",
+    "[deploy a cluster]: https://cratedb.com/docs/cloud/en/latest/tutorials/deploy/stripe.html#deploy-cluster\n",
     "[CrateDB Cloud Console]: https://cratedb.com/docs/cloud/en/latest/reference/overview.html#cluster"
    ]
   },