ML resources: reference the ML tasks topic from the glossary

docs/machine-learning/resources/glossary.md

@@ -27,7 +27,7 @@ Related ML.NET API: <xref:Microsoft.ML.Models.BinaryClassificationMetrics.Auc?di
 
 ## Binary classification
 
-A [classification](#classification) case where the [label](#label) is only one out of two classes. For more information, see the [Binary classification](https://en.wikipedia.org/wiki/Binary_classification) article on Wikipedia.
+A [classification](#classification) case where the [label](#label) is only one out of two classes. For more information, see the [Binary classification](tasks.md#binary-classification) section of the [Machine learning tasks](tasks.md) topic.
 
 ## Classification
 
@@ -79,7 +79,7 @@ Traditionally, the parameters for the prediction function. For example, the weig
 
 ## Multiclass classification
 
-A [classification](#classification) case where the [label](#label) is one out of three or more classes. For more information, see the [Multiclass classification](https://en.wikipedia.org/wiki/Multiclass_classification) article on Wikipedia.
+A [classification](#classification) case where the [label](#label) is one out of three or more classes. For more information, see the [Multiclass classification](tasks.md#multiclass-classification) section of the [Machine learning tasks](tasks.md) topic.
 
 ## N-gram
 
@@ -107,7 +107,7 @@ Related ML.NET API: <xref:Microsoft.ML.Models.BinaryClassificationMetrics.Negati
 
 ## Regression
 
-A [supervised machine learning](#supervised-machine-learning) task where the output is a real value, for example, double. Examples include predicting stock prices.
+A [supervised machine learning](#supervised-machine-learning) task where the output is a real value, for example, double. Examples include predicting stock prices. For more information, see the [Regression](tasks.md#regression) section of the [Machine learning tasks](tasks.md) topic.
 
 ## Relative absolute error
 

docs/machine-learning/resources/tasks.md

@@ -23,14 +23,18 @@ A [supervised machine learning](glossary.md#supervised-machine-learning) task th
 * Diagnosing whether a patient has a certain disease or not.
 * Making a decision to mark an email as "spam" or not.
 
-## Multi-class classification
+For more information, see the [Binary classification](https://en.wikipedia.org/wiki/Binary_classification) article on Wikipedia.
+
+## Multiclass classification
 
 A [supervised machine learning](glossary.md#supervised-machine-learning) task that is used to predict the class (category) of an instance of data. The input of a classification algorithm is a set of labeled examples. Each label is an integer between 0 and k-1, where k is the number of classes. The output of a classification algorithm is a classifier, which you can use to predict the class of new unlabeled instances. Examples of multi-class classification scenarios include:
 
 * Determining the breed of a dog as a "Siberian Husky", "Golden Retriever", "Poodle", etc.
 * Understanding movie reviews as "positive", "neutral", or "negative".
 * Categorizing hotel reviews as "location", "price", "cleanliness", etc.
 
+For more information, see the [Multiclass classification](https://en.wikipedia.org/wiki/Multiclass_classification) article on Wikipedia.
+
 ## Regression
 
 A [supervised machine learning](glossary.md#supervised-machine-learning) task that is used to predict the value of the label from a set of related features. The label can be of any real value and is not from a finite set of values as in classification tasks. Regression algorithms model the dependency of the label on its related features to determine how the label will change as the values of the features are varied. The input of a regression algorithm is a set of examples with labels of known values. The output of a regression algorithm is a function, which you can use to predict the label value for any new set of input features. Examples of regression scenarios include: