Merge pull request #100587 from luisquintanilla/AB1657805

Updating references to new folder structure in tutorials Jupyter Notebooks

Author: PRMerger10

articles/machine-learning/tutorial-1st-experiment-sdk-setup.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: trevorbye
 ms.author: trbye
 ms.reviewer: trbye
-ms.date: 09/25/2019
+ms.date: 02/10/2020
 ---
 
 # Tutorial: Get started creating your first ML experiment with the Python SDK
@@ -79,7 +79,7 @@ You complete the following experiment set-up and run steps in Azure Machine Lear
     > [!IMPORTANT]
     > You can view notebooks in the **samples** folder but you cannot run a notebook from there.  In order to run a notebook, make sure you open the cloned version of the notebook in the **User Files** section.
     
-1. Select the **tutorial-1st-experiment-sdk-train.ipynb** file in your **tutorials** folder.
+1. Select the **tutorial-1st-experiment-sdk-train.ipynb** file in your **tutorials/create-first-ml-experiment** folder.
 
 1. On the top bar, select a compute instance to use to run the notebook. These VMs are pre-configured with [everything you need to run Azure Machine Learning](concept-compute-instance.md#contents). You can select a VM created by any user of your workspace. 
 

articles/machine-learning/tutorial-1st-experiment-sdk-train.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: trevorbye
 ms.author: trbye
 ms.reviewer: trbye
-ms.date: 11/04/2019
+ms.date: 02/10/2020
 ---
 
 # Tutorial: Train your first ML model
@@ -30,7 +30,7 @@ In this tutorial, you learn the following tasks:
 
 The only prerequisite is to run part one of this tutorial, [Setup environment and workspace](tutorial-1st-experiment-sdk-setup.md).
 
-In this part of the tutorial, you run the code in the sample Jupyter notebook `tutorials/tutorial-1st-experiment-sdk-train.ipynb` opened at the end of part one. This article walks through the same code that is in the notebook.
+In this part of the tutorial, you run the code in the sample Jupyter notebook *tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb* opened at the end of part one. This article walks through the same code that is in the notebook.
 
 ## Open the notebook
 
@@ -40,7 +40,7 @@ In this part of the tutorial, you run the code in the sample Jupyter notebook `t
 
 
 > [!Warning]
-> Do **not** create a *new* notebook in the Jupyter interface! The notebook `tutorials/tutorial-1st-experiment-sdk-train.ipynb` is inclusive of **all code and data needed** 
+> Do **not** create a *new* notebook in the Jupyter interface! The notebook *tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb* is inclusive of **all code and data needed** 
 > for this tutorial.
 
 ## Connect workspace and create experiment

articles/machine-learning/tutorial-auto-train-models.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: trevorbye
 ms.author: trbye
 ms.reviewer: trbye
-ms.date: 11/04/2019
+ms.date: 02/10/2020
 ---
 
 # Tutorial: Use automated machine learning to predict taxi fares
@@ -31,7 +31,7 @@ If you don’t have an Azure subscription, create a free account before you begi
 ## Prerequisites
 
 * Complete the [setup tutorial](tutorial-1st-experiment-sdk-setup.md) if you don't already have an Azure Machine Learning workspace or notebook virtual machine.
-* After you complete the setup tutorial, open the **tutorials/regression-automated-ml.ipynb** notebook using the same notebook server.
+* After you complete the setup tutorial, open the *tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb* notebook using the same notebook server.
 
 This tutorial is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](how-to-configure-environment.md#local). Run `pip install azureml-sdk[automl] azureml-opendatasets azureml-widgets` to get the required packages.
 

articles/machine-learning/tutorial-deploy-models-with-aml.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 
 author: sdgilley
 ms.author: sgilley
-ms.date: 08/26/2019
+ms.date: 02/10/2020
 ms.custom: seodec18
 # As a professional data scientist, I can deploy the model previously trained in tutorial1.
 ---
@@ -37,7 +37,7 @@ Container Instances is a great solution for testing and understanding the workfl
 
 ## Prerequisites
 
-To run the notebook, first complete the model training in [Tutorial (part 1): Train an image classification model](tutorial-train-models-with-aml.md).   Then open the  **img-classification-part2-deploy.ipynb** notebook in your cloned **tutorials** folder.
+To run the notebook, first complete the model training in [Tutorial (part 1): Train an image classification model](tutorial-train-models-with-aml.md).   Then open the  *img-classification-part2-deploy.ipynb* notebook in your cloned *tutorials/image-classification-mnist-data* folder.
 
 This tutorial is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to use it on your own [local environment](how-to-configure-environment.md#local).  Make sure you have installed `matplotlib` and `scikit-learn` in your environment. 
 

articles/machine-learning/tutorial-pipeline-batch-scoring-classification.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: trevorbye
 ms.author: trbye
 ms.reviewer: trbye
-ms.date: 11/04/2019
+ms.date: 02/10/2020
 ---
 
 # Tutorial: Build an Azure Machine Learning pipeline for batch scoring
@@ -35,7 +35,7 @@ If you don’t have an Azure subscription, create a free account before you begi
 ## Prerequisites
 
 * If you don't already have an Azure Machine Learning workspace or notebook virtual machine, complete [Part 1 of the setup tutorial](tutorial-1st-experiment-sdk-setup.md).
-* When you finish the setup tutorial, use the same notebook server to open the *tutorials/tutorial-pipeline-batch-scoring-classification.ipynb* notebook.
+* When you finish the setup tutorial, use the same notebook server to open the *tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb* notebook.
 
 If you want to run the setup tutorial in your own [local environment](how-to-configure-environment.md#local), you can access the tutorial on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials). Run `pip install azureml-sdk[notebooks] azureml-pipeline-core azureml-pipeline-steps pandas requests` to get the required packages.
 

articles/machine-learning/tutorial-train-models-with-aml.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 
 author: sdgilley
 ms.author: sgilley
-ms.date: 11/04/2019
+ms.date: 02/10/2020
 ms.custom: seodec18
 #Customer intent: As a professional data scientist, I can build an image classification model with Azure Machine Learning by using Python in a Jupyter notebook.
 ---
@@ -43,7 +43,7 @@ If you don’t have an Azure subscription, create a free account before you begi
     * Clone the tutorials notebook to your folder in the workspace.
     * Create a cloud-based compute instance.
 
-* In your cloned **tutorials** folder, open the **img-classification-part1-training.ipynb** notebook. 
+* In your cloned *tutorials/image-classification-mnist-data* folder, open the *img-classification-part1-training.ipynb* notebook. 
 
 
 The tutorial and accompanying **utils.py** file is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to use it on your own [local environment](how-to-configure-environment.md#local). Run `pip install azureml-sdk[notebooks] azureml-opendatasets matplotlib` to install dependencies for this tutorial.