Typo

Author: GraemeMalcolm

04 - Clustering.ipynb

@@ -6,7 +6,7 @@
       "source": [
         "# Clustering\n",
         "\n",
-        "In contrast to *supervised* machine learning, *unsupervised* learning is used when there is no \"ground truth\" from which to train and validate label predictions. The most common form of unsupervised learning is *clustering*, which is simllar conceptually to *classification*, exept that the the training data does not include known values for the class label to be predicted. Clustering works by separating the training cases based on similarities that can be determined from their feature values. Think of it this way; the numeric features of a given entity can be though of as vector coordinates that define the entity's position in n-dimensional space. What a clustering model seeks to do is to identify groups, or *clusters*, of entities that are close to one another while being separated from other clusters.\n",
+        "In contrast to *supervised* machine learning, *unsupervised* learning is used when there is no \"ground truth\" from which to train and validate label predictions. The most common form of unsupervised learning is *clustering*, which is simllar conceptually to *classification*, except that the the training data does not include known values for the class label to be predicted. Clustering works by separating the training cases based on similarities that can be determined from their feature values. Think of it this way; the numeric features of a given entity can be though of as vector coordinates that define the entity's position in n-dimensional space. What a clustering model seeks to do is to identify groups, or *clusters*, of entities that are close to one another while being separated from other clusters.\n",
         "\n",
         "For example, let's take a look at the Iris dataset, one of the most famous samples in data science. This contains measurements of iris flowers."
       ]