diff --git a/Image Classifier Project.ipynb b/Image Classifier Project.ipynb
index eec7a117..a2882174 100644
--- a/Image Classifier Project.ipynb	
+++ b/Image Classifier Project.ipynb	
@@ -188,7 +188,7 @@
     "\n",
     "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
     "\n",
-    "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
+    "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](https://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.resize) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
     "\n",
     "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
     "\n",