Correct a few typos

projects/customer_segments/customer_segments.ipynb

@@ -229,8 +229,8 @@
     "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
     "\n",
     "In the code block below, you will need to implement the following:\n",
-    " - Assign a copy of the data to `log_data` after applying a logarithm scaling. Use the `np.log` function for this.\n",
-    " - Assign a copy of the sample data to `log_samples` after applying a logrithm scaling. Again, use `np.log`."
+    " - Assign a copy of the data to `log_data` after applying logarithmic scaling. Use the `np.log` function for this.\n",
+    " - Assign a copy of the sample data to `log_samples` after applying logarithmic scaling. Again, use `np.log`."
    ]
   },
   {
@@ -426,7 +426,7 @@
     "\n",
     "In the code block below, you will need to implement the following:\n",
     " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n",
-    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the reuslts to `reduced_data`.\n",
+    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the results to `reduced_data`.\n",
     " - Apply a PCA transformation of the sample log-data `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
    ]
   },