[DOCS] Updates ML getting started (#2505)

(cherry picked from commit 3936a91)

docs/en/stack/ml/get-started/ml-getting-started.asciidoc

@@ -47,7 +47,8 @@ user that has authority to manage {anomaly-jobs}. See <<setup>>.
 
 . {kibana-ref}/get-started.html#gs-get-data-into-kibana[Add the sample data sets that ship with {kib}]. 
 
-.. From the {kib} home page, click *Add data*, then select *Sample data*.
+.. From the {kib} home page, click *Try samle data*, then open the 
+*Other sample data sets* section.
 
 .. Pick a data set. In this tutorial, you'll use the *Sample web logs*. While
 you're here, feel free to click *Add data* on all of the available sample data 

docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc

@@ -9,8 +9,8 @@ In addition to detecting anomalous behavior in your data, you can use the
 To create a forecast in {kib}:
 
 . View your job results (for example, for the `low_request_rate` job) in the
-**Single Metric Viewer**. To find that view, follow the link in the **Actions**
-column on the **Anomaly Detection** page.
+**Single Metric Viewer**. To find that view, click the **View series** button in 
+the **Actions** column on the **Anomaly Detection** page.
 
 . Click **Forecast**.
 +

docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc

@@ -41,8 +41,8 @@ For more information, see <<ml-ad-datafeeds>>, <<ml-buckets>>, and
 ****
 
 If you want to see all of the configuration details for your jobs and {dfeeds},
-you can do so on the *Machine Learning* > *Anomaly Detection* > *Job Management*
-page. Alternatively, you can see the configuration files in
+you can do so on the *Machine Learning* > *Anomaly Detection* > *Jobs* page. 
+Alternatively, you can see the configuration files in
 https://github.com/elastic/kibana/tree/{branch}/x-pack/plugins/ml/server/models/data_recognizer/modules/sample_data_weblogs[GitHub
 ]. For the purposes of this tutorial, however, here's a quick overview of the
 goal of each job:

docs/en/stack/ml/get-started/ml-gs-results.asciidoc

@@ -154,11 +154,11 @@ Let's start by looking at the `response_code_rates` job in the
 . Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your
 {anomaly-jobs}.
 
-. Click the grid icon in the *Actions* column for your `response_code_rates` job
-to view its results in the **Anomaly Explorer**.
+. Open the `response_code_rates` job in the Anomaly Explorer to view its results
+ by clicking the corresponding icon in the row of the job.
 
-For this particular job, you can choose to see separate swim lanes for each client
-IP or response code. For example:
+For this particular job, you can choose to see separate swim lanes for each 
+client IP or response code. For example:
 
 [role="screenshot"]
 image::images/ml-gs-job2-explorer.jpg["Anomaly explorer for response_code_rates job"]
@@ -191,7 +191,8 @@ image::images/ml-gs-job2-explorer-anomaly.jpg["Anomaly charts for the response_c
 
 You can see exact times when anomalies occurred. If there are multiple detectors
 or metrics in the job, you can see which caught the anomaly. You can also switch
-to viewing this time series in the **Single Metric Viewer**.
+to viewing this time series in the **Single Metric Viewer** by clicking the 
+**View Series** button in the **Actions** menu.
 
 Below the charts, there is a table that provides more information, such as the
 typical and actual values and the influencers that contributed to the anomaly.

docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc

@@ -17,19 +17,22 @@ exception for your {kib} URL.
 
 --
 
-. Click *Machine Learning* in the side navigation.
+. Click *Machine Learning* in the {kib} main menu.
 
 . Select the *{data-viz}* tab.
 
-. Click *Select index* and choose the `kibana_sample_data_logs` {data-source}.
+. Click *Select data view* and choose the `kibana_sample_data_logs` {data-source}.
 
 . Use the time filter to select a time period that you're interested in 
 exploring. Alternatively, click
-*Use full kibana_sample_data_logs data* to view the full time range of data.
-
-. Optional: Change the sample size, which is the number of documents per shard
-that are used in the {data-viz}. There is a relatively small number of
-documents in the {kib} sample data, so you can choose a value of `all`. For
+*Use full data* to view the full time range of data.
+
+. Optional: You can change the random sampling behavior, which affects the 
+number of documents per shard that are used in the {data-viz}. You can use 
+automatic random sampling that balances accuracy and speed, manual sampling 
+where you can chose a value for the sampling percentage, or you can turn the 
+feaure off to use the full data set. There is a relatively small number of
+documents in the {kib} sample data, so you can turn random sampling off. For
 larger data sets, keep in mind that using a large sample size increases query
 run times and increases the load on the cluster.