fix random caitalization

Author: msakande

articles/machine-learning/how-to-train-scikit-learn.md

@@ -30,18 +30,18 @@ Whether you're training a machine learning scikit-learn model from the ground-up
 ## Prerequisites
 <!-- M.A: update the prerequisites (path to the notebook) before sign-off -->
 
-You can run this code in either an Azure Machine Learning compute instance, or your own Jupyter Notebook.
+You can run the code for this article in either an Azure Machine Learning compute instance, or your own Jupyter Notebook.
 
  - Azure Machine Learning compute instance
-    - Complete the [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md) to create a compute instance. Every compute instance includes a dedicated notebook server pre-loaded with the SDK and the notebooks sample repository. 
-    - Select the notebook tab in the Azure Machine Learning studio. In the samples training folder, find a completed and expanded notebook by navigating to this directory: **v2  > sdk > jobs > single-step > scikit-learn > train-hyperparameter-tune-deploy-with-sklearn** folder.
+    - Complete the [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md) to create a compute instance. Every compute instance includes a dedicated notebook server pre-loaded with the SDK and the notebooks sample repository.
+    - Select the notebook tab in the Azure Machine Learning studio. In the samples training folder, find a completed and expanded notebook by navigating to this directory: **v2  > sdk > jobs > single-step > scikit-learn > train-hyperparameter-tune-deploy-with-sklearn**.
     - You can use the pre-populated code in the sample training folder to complete this tutorial.
 
  - Your Jupyter notebook server.
     - [Install the Azure Machine Learning SDK (v2)](https://aka.ms/sdk-v2-install).
 
 
-## Set up the Job
+## Set up the job
 
 This section sets up the job for training by loading the required Python packages, connecting to a workspace, creating a compute resource to run a command job, and creating an environment to run the job.
 
@@ -80,11 +80,11 @@ Note:
 
 - Creating `MLClient` will not connect the client to the workspace. The client initialization is lazy and will wait for the first time it needs to make a call. In this article, this will happen during compute creation.
 
-### Create a Compute resource to run the job
+### Create a compute resource to run the job
 
 AzureML needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
 
-In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. We only need a basic cluster for this example; thus, we'll pick a Standard_DS3_v2 model with 2 vCPU cores and 7 GB RAM to create an AzureML Compute.
+In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. We only need a basic cluster for this example; thus, we'll pick a Standard_DS3_v2 model with 2 vCPU cores and 7 GB RAM to create an AzureML compute.
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-with-sklearn.ipynb?name=cpu_compute_target)]