[DOCS] Reformatted machine learning overview (elastic/x-pack-elasticsearch#3346)

* [DOCS] Reformatted machine learning overview

* [DOCS] Added intro ML screenshot

Original commit: elastic/x-pack-elasticsearch@b6189000e0

docs/en/ml/analyzing.asciidoc

@@ -0,0 +1,29 @@
+[float]
+[[ml-analyzing]]
+=== Analyzing the Past and Present
+
+The {xpackml} features automate the analysis of time-series data by creating
+accurate baselines of normal behavior in the data and identifying anomalous
+patterns in that data. You can submit your data for analysis in batches or
+continuously in real-time {dfeeds}.
+
+Using proprietary {ml} algorithms, the following circumstances are detected,
+scored, and linked with statistically significant influencers in the data:
+
+* Anomalies related to temporal deviations in values, counts, or frequencies
+* Statistical rarity
+* Unusual behaviors for a member of a population
+
+Automated periodicity detection and quick adaptation to changing data ensure
+that you don’t need to specify algorithms, models, or other data science-related
+configurations in order to get the benefits of {ml}.
+
+You can view the {ml} results in {kib} where, for example, charts illustrate the
+actual data values, the bounds for the expected values, and the anomalies that
+occur outside these bounds.
+
+[role="screenshot"]
+image::images/ml-gs-job-analysis.jpg["Example screenshot from the Machine Learning Single Metric Viewer in Kibana"]
+
+For a more detailed walk-through of {xpackml} features, see
+<<ml-getting-started>>.

docs/en/ml/index.asciidoc

@@ -3,28 +3,18 @@
 
 [partintro]
 --
-The {xpackml} features automate the analysis of time-series data by creating
-accurate baselines of normal behaviors in the data and identifying anomalous
-patterns in that data.
-
-Using proprietary {ml} algorithms, the following circumstances are detected,
-scored, and linked with statistically significant influencers in the data:
-
-* Anomalies related to temporal deviations in values, counts, or frequencies
-* Statistical rarity
-* Unusual behaviors for a member of a population
-
-Automated periodicity detection and quick adaptation to changing data ensure
-that you don’t need to specify algorithms, models, or other data science-related
-configurations in order to get the benefits of {ml}.
-
-[float]
-[[ml-intro]]
-== Integration with the Elastic Stack
-
 Machine learning is tightly integrated with the Elastic Stack. Data is pulled
 from {es} for analysis and anomaly results are displayed in {kib} dashboards.
 
+* <<ml-overview>>
+* <<ml-getting-started>>
+* <<ml-configuring>>
+* <<stopping-ml>>
+* <<ml-troubleshooting, Troubleshooting Machine Learning>>
+* <<ml-api-quickref>>
+* <<ml-functions>>
+
+
 --
 
 include::overview.asciidoc[]

docs/en/ml/overview.asciidoc

@@ -1,6 +1,14 @@
-[[ml-concepts]]
+[[ml-overview]]
 == Overview
 
+include::analyzing.asciidoc[]
+
+[[ml-concepts]]
+=== Basic Machine Learning Terms
+++++
+<titleabbrev>Basic Terms</titleabbrev>
+++++
+
 There are a few concepts that are core to {ml} in {xpack}. Understanding these
 concepts from the outset will tremendously help ease the learning process.