update markdown cells to sync with doc (#2001)

tutorials/azureml-in-a-day/azureml-in-a-day.ipynb

@@ -131,11 +131,11 @@
    "source": [
     "## Create a compute resource to run your job\n",
     "\n",
-    "You'll need a compute resource for running a job. It can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.\n",
+    "You already have a compute resource you're using to run the notebook.  But now you'll add another type, a **compute cluster** that you'll use to run your training job. The compute cluster can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.\n",
     "\n",
     "You'll provision a Linux compute cluster. See the [full list on VM sizes and prices](https://azure.microsoft.com/pricing/details/machine-learning/) .\n",
     "\n",
-    "For this example, you only need a basic cluster, so you'll use a Standard_DS3_v2 model with 2 vCPU cores, 7-GB RAM and create an Azure ML Compute."
+    "For this example, you only need a basic cluster, so you'll use a Standard_DS3_v2 model with 2 vCPU cores, 7-GB RAM."
    ]
   },
   {
@@ -192,7 +192,7 @@
    "source": [
     "## Create a job environment\n",
     "\n",
-    "To run your AzureML job on your compute resource, you'll need an [environment](https://docs.microsoft.com/azure/machine-learning/concept-environments). An environment lists the software runtime and libraries that you want installed on the compute where you’ll be training. It's similar to your python environment on your local machine.\n",
+    "To run your AzureML job on your compute cluster, you'll need an [environment](https://docs.microsoft.com/azure/machine-learning/concept-environments). An environment lists the software runtime and libraries that you want installed on the compute where you’ll be training. It's similar to your Python environment on your local machine.\n",
     "\n",
     "AzureML provides many curated or ready-made environments, which are useful for common training and inference scenarios. You can also create your own custom environments using a docker image, or a conda configuration.\n",
     "\n",
@@ -219,7 +219,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, create the file in the dependencies directory."
+    "Now, create the file in the dependencies directory. The cell below uses IPython magic to write the file into the directory you just created."
    ]
   },
   {
@@ -309,7 +309,7 @@
    "source": [
     "## Create training script\n",
     "\n",
-    "Let's start by creating the training script - the *main.py* python file.\n",
+    "Let's start by creating the training script - the *main.py* Python file.\n",
     "\n",
     "First create a source folder for the script:"
    ]
@@ -334,7 +334,9 @@
    "source": [
     "This script handles the preprocessing of the data, splitting it into test and train data. It then consumes this data to train a tree based model and return the output model. \n",
     "\n",
-    "[MLFlow](https://mlflow.org/docs/latest/tracking.html) will be used to log the parameters and metrics during our pipeline run."
+    "[MLFlow](https://mlflow.org/docs/latest/tracking.html) will be used to log the parameters and metrics during our pipeline run. \n",
+     "\n",
+    "The cell below uses IPython magic to write the training script into the directory you just created."
    ]
   },
   {
@@ -529,14 +531,14 @@
    "source": [
     "## View job output and wait for job completion\n",
     "\n",
-    "View the job in Azure ML studio by selecting the link in the output of the previous cell. The output of this job will look like this in Azure ML studio. Explore the tabs for various details like metrics, outputs etc. Once completed, the job will register a model in your workspace as a result of training. \n",
+    "View the job in Azure ML studio by selecting the link in the output of the previous cell. \n",
+    "\n",
+    "The output of this job will look like this in Azure ML studio. Explore the tabs for various details like metrics, outputs etc. Once completed, the job will register a model in your workspace as a result of training. \n",
     "\n",
     "![Screenshot that shows the job overview](media/view-job.gif \"View the job in studio\")\n",
     "\n",
     "> [!IMPORTANT]\n",
     "> Wait until the status of the job is complete before returning to this notebook to continue. The job will take 2 to 3 minutes to run. It could take longer (up to 10 minutes) if the compute cluster has been scaled down to zero nodes and custom environment is still building.\n",
-    "\n",
-    "\n",
     "\n"
    ]
   },