diff --git a/content/haxby_data.ipynb b/content/haxby_data.ipynb
index fac2051..78d1c53 100644
--- a/content/haxby_data.ipynb
+++ b/content/haxby_data.ipynb
@@ -2598,7 +2598,7 @@
    "source": [
     "### Labels and stimulus annotations\n",
     "\n",
-    "As mentioned in prior sessions (e.g.[Supervised learning using scikit-learn](https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn) and hinted at the [beginning of this session](#A-short-primer-on-datasets), when working on a `supervised learning problem`, we also need the `ground truth`/`true labels` for each `sample`. Why? Because we need to evaluate how a given `model` performs via comparing the `labels` it `predicted` to the `true labels`. What these `labels` refer to can be manifold and of course depends on the `task` at hand. \n",
+    "As mentioned in prior sessions (e.g.[Supervised learning using scikit-learn](https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn) and hinted at the [beginning of this session](#A-short-primer-on-datasets)), when working on a `supervised learning problem`, we also need the `ground truth`/`true labels` for each `sample`. Why? Because we need to evaluate how a given `model` performs via comparing the `labels` it `predicted` to the `true labels`. What these `labels` refer to can be manifold and of course depends on the `task` at hand. \n",
     "\n",
     "For example, a `supervised learning problem` in the `dataset` at hand could entail `training` a `model` to `recognize` and `predict` what `category` `participants` perceived based on their `brain activation`. Thus, we would need to know what `category` was shown when during the acquisition of the `data` (or which `category` resulted in which `estimated` `brain activity`). \n",
     "\n",