adds minimum es version

Author: blaklaybul

Machine Learning/Outlier Detection/Introduction/README.md

@@ -2,5 +2,5 @@
 
 To run this example, please ensure that you have the following:
 
-* an elasticsearch cluster running on `localhost:9200`
+* an elasticsearch cluster (version 7.2+) running on `localhost:9200`
 * the dependencies listed in `requirements.txt`. To install these, please run  - `pip install -r requirements.txt`

Machine Learning/Outlier Detection/Introduction/advent-outliers.ipynb

@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Outlier detection has been available in machine learning since 7.2 - what follow is a demonstration about how to create outlier detection analyses and how to analyze the results.\n",
+    "Outlier detection has been available in machine learning since 7.2 - what follows is a demonstration about how to create outlier detection analyses and how to analyze the results.\n",
     "\n",
     "For the sake of demonstration, we'll be using an artifical two-dimensional dataset that I've created using `numpy` and `scikit-learn`."
    ]
@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This dataset contains 2500 points normally-distributed around two main clusters centers at `(-1, -1)` and `(3, 1)`. Scikit-learn's `make_blobs` method allows us to control the standard deviations of each cluster, which I've set to `1.25` and `0.5`, respctively. This allows us to see how outlier detection functions on data with differing densities.\n",
+    "This dataset contains 2500 points normally-distributed around two main clusters centers at `(-1, -1)` and `(3, 1)`. Scikit-learn's `make_blobs` method allows us to control the standard deviations of each cluster, which I've set to `1.25` and `0.5`, respectively. This allows us to see how outlier detection functions on data with differing densities.\n",
     "\n",
     "After making the data clusters, we introduce 99 new points, with `x` and `y` locations randomly sampled from a uniform distribution. Some of these points will fall well outside the clusters we created and should be deemed outliers; others will lie within the clusters and appear as normal points.\n",
     "\n",

Machine Learning/Outlier Detection/README.md

@@ -1,5 +1,5 @@
 # Outlier Detection
 
-This directory contains examples showcasing Elastic's outlier detection analyses within Machine Learning. 
+This directory contains examples showcasing Elastic's outlier detection analyses within Machine Learning, which was released in version 7.2.
 
 * [Introduction](Introduction/) shows you how to think about outliers in a 2-dimensional case. We walk you through building up a dataset with particular features and demonstrates how to create and start an outlier detection analysis, and how to analyze the outlier score and feature influence results.