Update how-to-monitor-datasets.md

Author: SturgeonMi

articles/machine-learning/v1/how-to-monitor-datasets.md

@@ -513,7 +513,7 @@ Metrics in the chart depend on the type of feature.
 
     | Metric | Description |
     | ------ | ----------- |
-    | Euclidian distance     |  Computed for categorical columns. Euclidean distance is computed on two vectors, generated from empirical distribution of the same categorical column from two datasets. 0 indicates no difference in the empirical distributions.  The more it deviates from 0, the more this column has drifted. Trends can be observed from a time series plot of this metric and can be helpful in uncovering a drifting feature.  |
+    | Euclidian distance   |  Computed for categorical columns. Euclidean distance is computed on two vectors, generated from empirical distribution of the same categorical column from two datasets. 0 indicates no difference in the empirical distributions.  The more it deviates from 0, the more this column has drifted. Trends can be observed from a time series plot of this metric and can be helpful in uncovering a drifting feature.  |
     | Unique values | Number of unique values (cardinality) of the feature. |
 
 On this chart, select a single date to compare the feature distribution between the target and this date for the displayed feature. For numeric features, this shows two probability distributions. If the feature is numeric, a bar chart is shown.