diff --git a/notebooks/7_hard_drive_failures.ipynb b/notebooks/7_hard_drive_failures.ipynb
index e95632d..78647cc 100644
--- a/notebooks/7_hard_drive_failures.ipynb
+++ b/notebooks/7_hard_drive_failures.ipynb
@@ -117,7 +117,7 @@
    "metadata": {},
    "source": [
     "-------------------------------------------------------------------------------------\n",
-    "You're already given this rudimentary prediction pipeline, now your job is to improve it. Below are some things you might want to try, but feel free to get creative! Have a look at the [cheat sheet](https://github.com/cod3licious/ml_exercises/blob/main/other/cheatsheet.pdf) for more ideas and a concise overview of the relevant steps when developing a machine learning solution in any data science project. \n",
+    "You're already given this rudimentary prediction pipeline, now your job is to improve it. Below are some things you might want to try, but feel free to get creative! Have a look at the [cheat sheet](https://franziskahorn.de/mlws_resources/cheatsheet.pdf) for more ideas and a concise overview of the relevant steps when developing a machine learning solution in any data science project. \n",
     "\n",
     "The previous notebook, \"analyze toydata\", deals with a very similar problem and can serve as a guideline for this exercise. For an example of how to use the t-SNE algorithm, have a look at the first notebook, \"visualize text\" (but please note that since you don't have sparse data here, there is no need to transform the data with a kernel PCA before using t-SNE).\n",
     "\n",