[DOCS] Updates ML/anomaly detection terms in the Kibana guide from 7.…

…1 to 6.5 (#42428)

* [DOCS] Updates ML/anomaly detection terms in the Kibana guide from 7.1 to 6.5.

* [DOCS] Fixes indentation.

docs/ml/creating-jobs.asciidoc

@@ -1,8 +1,8 @@
 [role="xpack"]
 [[ml-jobs]]
-== Creating machine learning jobs
+== Creating {anomaly-jobs}
 
-Machine learning jobs contain the configuration information and metadata
+{anomaly-jobs-cap} contain the configuration information and metadata
 necessary to perform an analytics task.
 
 {kib} provides the following wizards to make it easier to create jobs:
@@ -42,7 +42,7 @@ activity on your systems, the following wizards appear:
 [role="screenshot"]
 image::ml/images/ml-data-recognizer-auditbeat.jpg[A screenshot of the {auditbeat} job creation wizards] 
 
-These wizards create {ml} jobs, dashboards, searches, and visualizations that
+These wizards create {anomaly-jobs}, dashboards, searches, and visualizations that
 are customized to help you analyze your {auditbeat} and {filebeat} data.
 
 If you are not certain which type of job to create, you can use the
@@ -53,7 +53,7 @@ a time field, it can identify possible fields for {ml} analysis.
 ===============================
 If your data is located outside of {es}, you cannot use {kib} to create
 your jobs and you cannot use {dfeeds} to retrieve your data in real time.
-Machine learning analysis is still possible, however, by using APIs to
+{anomaly-detect-cap} is still possible, however, by using APIs to
 create and manage jobs and post data to them. For more information, see
 {ref}/ml-apis.html[Machine Learning APIs].
 ===============================

docs/ml/index.asciidoc

@@ -1,35 +1,36 @@
 [role="xpack"]
 [[xpack-ml]]
-= Machine Learning
+= {ml-cap}
 
 [partintro]
 --
 
 As datasets increase in size and complexity, the human effort required to
 inspect dashboards or maintain rules for spotting infrastructure problems,
-cyber attacks, or business issues becomes impractical. The Elastic {ml-features}
-automatically model the normal behavior of your time series data — learning
-trends, periodicity, and more — in real time to identify anomalies, streamline
-root cause analysis, and reduce false positives.
+cyber attacks, or business issues becomes impractical. The Elastic {ml} 
+{anomaly-detect} feature automatically model the normal behavior of your time 
+series data — learning trends, periodicity, and more — in real time to identify 
+anomalies, streamline root cause analysis, and reduce false positives.
 
-The {ml-features} run in and scale with {es}, and include an
-intuitive UI on the {kib} *Machine Learning* page for creating anomaly detection
-jobs and understanding results.
+{anomaly-detect-cap} run in and scale with {es}, and include an
+intuitive UI on the {kib} *Machine Learning* page for creating {anomaly-jobs} 
+and understanding results.
 
 If you have a basic license, you can use the *Data Visualizer* to learn more
 about your data. In particular, if your data is stored in {es} and contains a
 time field, you can use the *Data Visualizer* to identify possible fields for
-{ml} analysis:
+{anomaly-detect}:
 
 [role="screenshot"]
 image::ml/images/ml-data-visualizer-sample.jpg[Data Visualizer for sample flight data]
 
 experimental[] You can also upload a CSV, NDJSON, or log file (up to 100 MB in size). 
-The {ml-features} identify the file format and field mappings. You can then 
+The *Data Visualizer* identifies the file format and field mappings. You can then 
 optionally import that data into an {es} index.  
 
-If you have a trial or platinum license, you can <<ml-jobs,create {ml} jobs>> 
-and manage jobs and {dfeeds} from the *Job Management* pane: 
+If you have a trial or platinum license, you can 
+<<ml-jobs,create {anomaly-jobs}>> and manage jobs and {dfeeds} from the *Job 
+Management* pane: 
 
 [role="screenshot"]
 image::ml/images/ml-job-management.jpg[Job Management]
@@ -42,7 +43,7 @@ You can use the *Settings* pane to create and edit
 image::ml/images/ml-settings.jpg[Calendar Management]
 
 The *Anomaly Explorer* and *Single Metric Viewer* display the results of your
-{ml} jobs. For example:
+{anomaly-jobs}. For example:
 
 [role="screenshot"]
 image::ml/images/ml-single-metric-viewer.jpg[Single Metric Viewer]
@@ -56,7 +57,7 @@ occurring in your operational environment at that time:
 image::ml/images/ml-annotations-list.jpg[Single Metric Viewer with annotations]
 
 In some circumstances, annotations are also added automatically. For example, if
-the {ml} analytics detect that there is missing data, it annotates the affected
+the {anomaly-job} detects that there is missing data, it annotates the affected
 time period. For more information, see 
 {stack-ov}/ml-delayed-data-detection.html[Handling delayed data].
 The *Job Management* pane shows the full list of annotations for each job.
@@ -65,8 +66,8 @@ NOTE: The {kib} {ml-features} use pop-ups. You must configure your
 web browser so that it does not block pop-up windows or create an exception for
 your {kib} URL.
 
-For more information about {ml}, see
-{stack-ov}/xpack-ml.html[Machine learning in the {stack}].
+For more information about the {anomaly-detect} feature, see
+{stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}].
 
 --