diff --git a/getting_started/04_numpy.ipynb b/getting_started/04_numpy.ipynb
index 23b4131ecdd19d26f411a08610cdbc0c851138f5..bccf81a46347a23fed03a371d09d3f0096f1204b 100644
--- a/getting_started/04_numpy.ipynb
+++ b/getting_started/04_numpy.ipynb
@@ -1404,7 +1404,7 @@
    "metadata": {},
    "source": [
     "But if you are writing a script or application using Numpy, I implore you to\n",
-    "Numpy (and its commonly used sub-modules) like this instead:"
+    "import Numpy (and its commonly used sub-modules) like this instead:"
    ]
   },
   {
@@ -1497,12 +1497,12 @@
     "By now you should be aware that a Numpy `array` does not behave in quite the\n",
     "same way as a Matlab matrix. The primary difference between Numpy and Matlab\n",
     "is that in Numpy, the `*` operator denotes element-wise multiplication,\n",
-    "gwhereas in Matlab, `*` denotes matrix multiplication.\n",
+    "whereas in Matlab, `*` denotes matrix multiplication.\n",
     "\n",
     "\n",
     "Numpy does support the `@` operator for matrix multiplication, but if this is\n",
-    "a complete show-stopper for you - if you just can't bring yourself to write `A\n",
-    "@ B` to denote the matrix product of `A` and `B` - if you _must_ have your\n",
+    "a complete show-stopper for you - if you just can't bring yourself to write\n",
+    "`A @ B` to denote the matrix product of `A` and `B` - if you _must_ have your\n",
     "code looking as Matlab-like as possible, then you should look into the Numpy\n",
     "[`matrix`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html)\n",
     "data type.\n",
diff --git a/getting_started/04_numpy.md b/getting_started/04_numpy.md
index 8ea10a8521108c14c0c4a37a39dca132e961da4d..3a28cc830442b08628633cd04372f9091a6683ee 100644
--- a/getting_started/04_numpy.md
+++ b/getting_started/04_numpy.md
@@ -1055,7 +1055,7 @@ print(d)
 
 
 But if you are writing a script or application using Numpy, I implore you to
-Numpy (and its commonly used sub-modules) like this instead:
+import Numpy (and its commonly used sub-modules) like this instead:
 
 
 ```
@@ -1124,12 +1124,12 @@ print(np.atleast_2d(r).T)
 By now you should be aware that a Numpy `array` does not behave in quite the
 same way as a Matlab matrix. The primary difference between Numpy and Matlab
 is that in Numpy, the `*` operator denotes element-wise multiplication,
-gwhereas in Matlab, `*` denotes matrix multiplication.
+whereas in Matlab, `*` denotes matrix multiplication.
 
 
 Numpy does support the `@` operator for matrix multiplication, but if this is
-a complete show-stopper for you - if you just can't bring yourself to write `A
-@ B` to denote the matrix product of `A` and `B` - if you _must_ have your
+a complete show-stopper for you - if you just can't bring yourself to write
+`A @ B` to denote the matrix product of `A` and `B` - if you _must_ have your
 code looking as Matlab-like as possible, then you should look into the Numpy
 [`matrix`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html)
 data type.