diff --git a/_extras/guide.md b/_extras/guide.md
index e55545aa4..12bbd3d34 100644
--- a/_extras/guide.md
+++ b/_extras/guide.md
@@ -4,23 +4,23 @@ title: "Instructor Notes"
 
 ## Working with Jupyter notebooks
 
-* This lesson is designed to be taught using Jupyter notebooks. We recommend that instructors guide learners to create a new Jupyter note for each episode.
+* This lesson is designed to be taught using Jupyter notebooks. We recommend that instructors guide learners to create a new Jupyter notebook for each episode.
 
 * Python `import` statements typically appear in the first code block near the top of each episode. In some cases, the purpose of specific libraries is briefly explained as part of the exercises.
 
-* The possibility of executing the code cells in a notebook in arbitrary order can cause confusion. Using the "restart kernel and run all cells" feature is one way to accomplish linear execution of the notebook and may help to locate and identify coding issues.
+* The possibility of executing the code cells in a notebook in arbitrary order can cause confusion. Using the "restart kernel and run all cells" feature is one way to accomplish linear execution of the notebook and may help locate and identify coding issues.
 
-* Many episodes in this lesson load image files from disk. To avoid name clashes in episodes that load multiple different files, we have used unique variable names (instead of generic names such as `image` or `img`). When copying code snippets between exercises, the variable names may have to be changed. The maintainers are keen to receive feedback whether this convention proves practical in workshops.
+* Many episodes in this lesson load image files from disk. To avoid name clashes in episodes that load multiple image files, we have used unique variable names (instead of generic names such as `image` or `img`). When copying code snippets between exercises, the variable names may have to be changed. The maintainers are keen to receive feedback on whether this convention proves practical in workshops.
 
 ## Working with imageio and skimage
 
 * `imageio.v3` allows to load images in different modes by passing the `mode=` argument to `imread()`. Depending on the image file and mode, the `dtype` of the resulting Numpy array can be different (e.g., `dtype('uint8')` or `dtype('float64')`. In the lesson, `skimage.util.img_as_ubyte()` and `skimage.util.img_as_float()` are used to convert the data type when necessary.
 
-* Some `skimage` functions implicitly convert the the pixel values to floating point numbers. Several callout boxes have been added throughout the lesson to raise awareness, but this may still prompt questions from learners.
+* Some `skimage` functions implicitly convert the pixel values to floating-point numbers. Several callout boxes have been added throughout the lesson to raise awareness, but this may still prompt questions from learners.
 
-* In certain situations, `imread()` returns a read-only array. This depends on the image file type and the backend (e.g., Pillow). If a read-only error is encountered, `image = np.array(image)` can be used to create a writable copy of the array before manipulating its pixel values.
+* In certain situations, `imread()` returns a read-only array. This depends on the image file type and on the backend (e.g., Pillow). If a read-only error is encountered, `image = np.array(image)` can be used to create a writable copy of the array before manipulating its pixel values.
 
-* Be aware, that learners might get surprising results in the *Keeping only low intensity pixels* exercise, if `plt.imshow` is called without the `vmax` parameter.
+* Be aware that learners might get surprising results in the *Keeping only low intensity pixels* exercise, if `plt.imshow` is called without the `vmax` parameter.
   A detailed explanation is given in the *Plotting single channel images (cmap, vmin, vmax)* callout box.
 
 ## Blurring