[Lesson 19] Add training part

5-Data-Science-In-Cloud/19-tbd/README.md

@@ -10,7 +10,9 @@ Table of contents:
   - [2. Training a model with the Azure ML SDK](#2-training-a-model-with-the-azure-ml-sdk)
     - [2.1 Create an Azure ML workspace](#21-create-an-azure-ml-workspace)
     - [2.2 Create a compute instance](#22-create-a-compute-instance)
+    - [2.3 Loading the Dataset](#23-loading-the-dataset)
     - [2.3 Creating Notebooks](#23-creating-notebooks)
+    - [2.4 Training a model with the Azure ML SDK](#24-training-a-model-with-the-azure-ml-sdk)
   - [🚀 Challenge](#-challenge)
   - [Post-Lecture Quiz](#post-lecture-quiz)
   - [Review & Self Study](#review--self-study)
@@ -63,30 +65,51 @@ Let's create a compute instance to provision a jupyter notebook.
 3. Choose your options: CPU or GPU, VM size and core number.
 4. Click in the Create button.
 
-Congratulations, you have just created a compute instance! We will use this compute instance to create a Notebook the [Creating Notebooks section](#creating-notebooks)
+Congratulations, you have just created a compute instance! We will use this compute instance to create a Notebook the [Creating Notebooks section](#23-creating-notebooks).
+
+### 2.3 Loading the Dataset
+Refer the [previous lesson](../18-tbd/README.md) in the section **2.3 Loading the Dataset** if you have not uploaded the dataset yet.
 
 ### 2.3 Creating Notebooks
 Notebook are a really important part of the data science process. They can be used to Conduct Exploratory Data Analysis (EDA), call out to a computer cluster to train a model, call out to an inference cluster to deploy an endpoint. 
 
-To create a Notebook, we need a compute node that is serving out the jupyter notebook instance. Go back to the [Azure ML workspace](https://ml.azure.com/) and click on Compute instances. In the list of compute instances you should see the [compute instance we created earlier](#222-creating-compute-resources). 
+To create a Notebook, we need a compute node that is serving out the jupyter notebook instance. Go back to the [Azure ML workspace](https://ml.azure.com/) and click on Compute instances. In the list of compute instances you should see the [compute instance we created earlier](#22-create-a-compute-instance). 
 
 1. In the Applications section, click on the Jupyter option. 
 2. Tick the "Yes, I understand" box and click on the Continue button.
 ![notebook-1](img/notebook-1.PNG)
-3. This should open a new browser tab with you jupyter notebook instance as follow.
+3. This should open a new browser tab with your jupyter notebook instance as follow. Click on the "New" button to create a notebook.
 
 ![notebook-2](img/notebook-2.PNG)
 
-Import the class and create a new workspace by using the following code:
+Now that we have a Notebook, we can start training the model with Azure ML SDK.
+
+### 2.4 Training a model with the Azure ML SDK
+
+First of all, if you ever have a doubt, refer to the [Azure ML SDK documentation](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/?view=azure-ml-py). In contains all the necessary information to understand the modules we are going to see in this lesson.
+
+You need to load the `workspace` from the configuration file using the following code:
 
 ```python
 from azureml.core import Workspace
-ws = Workspace.create(name='myworkspace',
-                      subscription_id='<azure-subscription-id>',
-                      resource_group='myresourcegroup',
-                      create_resource_group=True,
-                      location='eastus2'
-                     )
+ws = Workspace.from_config()
+```
+
+This returns an object of type `Workspace` that represents the workspace. The you need to create an `experiment` using the following code:
+
+```python
+from azureml.core import Experiment
+experiment_name = 'aml-experiment'
+experiment = Experiment(ws, experiment_name)
+```
+To get or create an experiment from a workspace, you request the experiment using the experiment name. Experiment name must be 3-36 characters, start with a letter or a number, and can only contain letters, numbers, underscores, and dashes. If the experiment is not found in the workspace, a new experiment is created.
+
+You can get the dataset from the workspace using the dataset name in the following way:
+
+```python
+dataset = ws.datasets['heart-failure-records']
+df = dataset.to_pandas_dataframe()
+df.describe()
 ```
 
 ## 🚀 Challenge