Explaining class assignment objectives for data frame analytics jobs (#357)

This PR adds a notebook that explains the properties of different class assignment objectives available for the machine learning data frame analytics classification jobs and gives pointers on when to choose which objective.

It intends to be linked in the "Concepts" section of the documentation and provide users with more insights than it is possible in the scope of "Concepts".

The rendered version of the jupyter notebook can be found here.

Closes #356.

Author: valeriy42

Machine Learning/Class Assigment Objectives/README.md

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+# Class Assignment Objective
+
+This notebook explains the properties of different class assignment objectives available for the
+machine learning data frame analytics classification jobs and give pointers on when to
+choose which loss function.
+
+Set up a local instance of Jupyter using the following instructions
+
+1. Create file `credentials.json` containing the username and password to access your Elasticsearch instance.
+
+    ```json
+    {
+        "username": "my_username",
+        "password": "my_password"
+    }
+    ```
+
+2. Set up a virtual environment called `env`
+
+    ```bash
+    python3 -m venv env
+    ```
+
+3. Activate it
+
+    ```bash
+    source env/bin/activate
+    ```
+
+4. Install the required dependencies for your chosen Jupyter notebook
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+5. Launch Jupyter
+
+    ```bash
+    jupyter notebook
+    ```

Machine Learning/Class Assigment Objectives/classification-class-assignment-objective.ipynb

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+eland==7.10.1b1
+elasticsearch==7.10.1
+jupyter==1.0.0
+matplotlib==3.3.4
+numpy==1.19.5
+pandas==1.1.5
+requests==2.25.1
+scikit-learn==0.24.1
+seaborn==0.11.1