Fix ML links (#3976)

* Fix ML links

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

* Fix style errors

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

* Fix RCF spelling

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

---------

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

_ml-commons-plugin/algorithms.md

@@ -27,7 +27,7 @@ distance_type | enum, such as `EUCLIDEAN`, `COSINE`, or `L1` | The type of measu
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 * [Train and predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-and-predict)
 
@@ -77,7 +77,7 @@ optimizerType | OptimizerType | The optimizer used in the model. | SIMPLE_SGD
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 
 ### Example
@@ -189,7 +189,7 @@ time_zone | string | The time zone for the `time_field` field. | "UTC"
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 * [Train and predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-and-predict)
 
@@ -200,7 +200,7 @@ For FIT RCF, you can train the model with historical data and store the trained
 
 ## RCF Summarize
 
-RCFSummarize is a clustering algorithm based on the Clustering Using REpresentatives (CURE) algorithm. Compared to [k-means](#k-means), which uses random iterations to cluster, RCFSummarize uses a hierarchical clustering technique. The algorithm starts, with a set of randomly selected centroids larger than the centroids' ground truth distribution. During iteration, centroid pairs too close to each other automatically merge. Therefore, the number of centroids (`max_k`) converge to a rational number of clusters that fits ground truth, as opposed to a fixed `k` number of clusters.  
+RCF Summarize is a clustering algorithm based on the Clustering Using Representatives (CURE) algorithm. Compared to [k-means](#k-means), which uses random iterations to cluster, RCF Summarize uses a hierarchical clustering technique. The algorithm starts, with a set of randomly selected centroids larger than the centroids' ground truth distribution. During iteration, centroid pairs too close to each other automatically merge. Therefore, the number of centroids (`max_k`) converge to a rational number of clusters that fits ground truth, as opposed to a fixed `k` number of clusters.  
 
 ### Parameters
 
@@ -211,7 +211,7 @@ RCFSummarize is a clustering algorithm based on the Clustering Using REpresentat
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 * [Train and predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-and-predict)
 
@@ -429,7 +429,7 @@ A classification algorithm, logistic regression models the probability of a disc
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 
 ### Example: Train/Predict with Iris data
@@ -651,7 +651,7 @@ PUT /_cluster/settings
 
 ### Parameters
 
-To use the metrics correlation algorthim, include the following parameters.
+To use the metrics correlation algorithm, include the following parameters.
 
 | Parameter | Type | Description | Default value |
 |---|---|---|---|
@@ -661,7 +661,7 @@ metrics | Array | A list of metrics within the time series that can be correlate
 
 The metrics correlation input is an $M$ x $T$ array of metrics data, where M is the number of metrics and T is the length of each individual sequence of metric values. 
 
-When inputting metrics into the algorthim, assume the following:
+When inputting metrics into the algorithm, assume the following:
 
 1. For each metric, the input sequence has the same length, $T$.
 2. All input metrics should have the same corresponding set of timestamps.

_ml-commons-plugin/cluster-settings.md

@@ -39,7 +39,7 @@ plugins.ml_commons.task_dispatch_policy: round_robin
 
 ### Values
 
-- Dafault value: `round_robin`
+- Default value: `round_robin`
 - Value range: `round_robin` or `least_load`
 
 ## Set number of ML tasks per node
@@ -74,7 +74,7 @@ plugins.ml_commons.max_model_on_node: 10
 
 ## Set sync job intervals 
 
-When returning runtime information with the [Profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#returning-model-profile-information), ML Commons will run a regular job to sync newly deployed or undeployed models on each node. When set to `0`, ML Commons immediately stops sync-up jobs.
+When returning runtime information with the [Profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#profile), ML Commons will run a regular job to sync newly deployed or undeployed models on each node. When set to `0`, ML Commons immediately stops sync-up jobs.
 
 
 ### Setting

_ml-commons-plugin/index.md

@@ -12,7 +12,7 @@ ML Commons for OpenSearch eases the development of machine learning features by
 
 Interaction with the ML Commons plugin occurs through either the [REST API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api) or [`ad`]({{site.url}}{{site.baseurl}}/search-plugins/sql/ppl/functions#ad) and [`kmeans`]({{site.url}}{{site.baseurl}}/search-plugins/sql/ppl/functions#kmeans) Piped Processing Language (PPL) commands.
 
-Models [trained]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#training-a-model) through the ML Commons plugin support model-based algorithms such as kmeans. After you've trained a model enough so that it meets your precision requirements, you can apply the model to [predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#predict) new data safely. 
+Models [trained]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#train-model) through the ML Commons plugin support model-based algorithms such as kmeans. After you've trained a model enough so that it meets your precision requirements, you can apply the model to [predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#predict) new data safely. 
 
 Should you not want to use a model, you can use the [Train and Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#train-and-predict) API to test your model without having to evaluate the model's performance.