docs: Improve Machine-Learning example (#382)

Related to Issue #380 

### Summary of Changes

Added Links to other classes
Added sub-headings where appropriate
Replaced "=" with ":" where appropriate 
Fixed indent

docs/Stdlib/python/Tutorials/machine_learning.md

@@ -1,8 +1,10 @@
 # Machine Learning Tutorial
 
+## Create SupervisedDataset
+
 Here is a short introduction to train and predict with a machine learning model in safe-ds.
 
-First we need to create a SupervisedDataset from the training data.
+First we need to create a [SupervisedDataset][safe_ds.data.SupervisedDataset] from the training data.
 
 ```python
 from safe_ds.data import Table, SupervisedDataset
@@ -21,38 +23,51 @@ to_be_predicted_table = Table({
 sup_dataset = SupervisedDataset(table, target_column="target")
 ```
 
-SupervisedDatasets are used in safe-DS to train supervised machine learning models, because they keep track of the target
-vector. A SupervisedDataset can be created from a table and specifying the target vector in the table.
+[SupervisedDatasets][safe_ds.data.SupervisedDataset] are used in safe-DS to train supervised machine learning models
+(e.g. [RandomForest][safe_ds.classification.RandomForest] for classification and
+[LinearRegression][safe_ds.regression.LinearRegression] as a regression model), because they keep track of the target
+vector. A [SupervisedDataset][safe_ds.data.SupervisedDataset] can be created from a [Table][safe_ds.data.Table] and
+specifying the target vector in the table.
+
+## Create and train model
 
 In this code example, we want to predict the sum of a row. The `table` contains the target vector we want to
 train with (the sum of the rows). The `to_predicted_table` is the table we want to make predictions with, so it
 does not contain a target vector.
 
-In order to train the `LinearRegression`-model we need to make the following calls in safe-DS.
+In order to train the [LinearRegression][safe_ds.regression.LinearRegression]-model we need to make the following calls
+in safe-DS:
 
 ```python
 linear_reg_model = LinearRegression()
 linear_reg_model.fit(sup_dataset)
 ```
 
-As we can see, a `LinearRegression`-object is created.
+As we can see, a [LinearRegression][safe_ds.regression.LinearRegression]-object is created.
 
 In safe-DS machine learning models are separated in different classes where the different fit and predictions methods
 are implemented for the given machine learning model.
 
-So in order to train a linear regression model we create a `LinearRegression`-object and call then the `.fit()`
--method on this object. Now the `linear_reg_model` is a fitted linear regression model, and we can call
-the `predict(dataset = SupervisedDataset)`-method on this model.
+## Predicting new values
+
+So in order to train a linear regression model we create a [LinearRegression][safe_ds.regression.LinearRegression]-object
+and call then the [`.fit()`][safe_ds.regression._linear_regression.LinearRegression.fit]
+-method on this object. Now the `linear_reg_model` is a fitted linear regression model, and we can call the
+[`predict(dataset: SupervisedDataset)`][safe_ds.regression._linear_regression.LinearRegression.predict]-method
+on this model.
 
 ```python
 prediction = linear_reg_model.predict(dataset=to_be_predicted_table,
                                       target_name="predicted_values")
 ```
 
 After we trained the `linear_reg_model`-object we can make predictions with the model. To do this we call the
-`predict(dataset = Table, target_name = Optional[str])`-method on the trained model. The `target_name`-parameter
-is optional, so you do not need to specify it. If you do not specify the `target_name`, the name of
-the `target_vector` in the given `SupervisedDataset`will be used.
+[`predict(dataset: Table, target_name: Optional[str])`][safe_ds.regression._linear_regression.LinearRegression.predict]-method
+on the trained model. The `target_name`-parameter is optional, so you do not need to specify it.
+If you do not specify the `target_name`, the name of the `target_vector` in the given
+[SupervisedDataset][safe_ds.data.SupervisedDataset] will be used.
+
+## Results
 
 So for the call above we will get the following output:
 
@@ -65,4 +80,4 @@ So for the call above we will get the following output:
 | 4       | 7       | 1       | 12.0   |
 
 !!! note
-Your target-vector may differ from our result.
+    Your target-vector may differ from our result.