update samples from Release-139 as a part of 1.0.55 SDK release

tutorials/README.md

@@ -8,15 +8,20 @@ two sets of tutorial articles for:
 
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 
+### Create first ML experiment
+
+* [Part 1](https://docs.microsoft.com/azure/machine-learning/service/tutorial-quickstart-setup): Set up workspace & dev environment
+* [Part 2](tutorial-quickstart-train-model.ipynb): Learn the foundational design patterns in Azure Machine Learning service, and train a simple scikit-learn model based on the diabetes data set
+
 ### Image classification
 
  * [Part 1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning.
  * [Part 2](img-classification-part2-deploy.ipynb): Deploy an image classification model from first tutorial in Azure Container Instance (ACI).
 
  ### Regression
- * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK. 
+ * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK.
  * [Part 2](regression-part2-automated-ml.ipynb): Train a model using Automated Machine Learning.
 
  Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
 
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png) 
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png)

tutorials/img-classification-part1-training.ipynb

@@ -218,7 +218,7 @@
       "source": [
         "### Display some sample images\n",
         "\n",
-        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `util.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays."
+        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays."
       ]
     },
     {
@@ -260,7 +260,7 @@
         "\n",
         "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
         "\n",
-        "The MNIST files are uploaded into a directory named `mnist` at the root of the datastore."
+        "The MNIST files are uploaded into a directory named `mnist` at the root of the datastore. See [access data from your datastores](https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/service/how-to-access-data) for more information."
       ]
     },
     {
@@ -674,6 +674,18 @@
       "language": "python",
       "name": "python36"
     },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    },
     "msauthor": "roastala"
   },
   "nbformat": 4,

tutorials/tutorial-1st-experiment-sdk-train.ipynb

@@ -11,7 +11,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/tutorial-1st-experiment-sdk-train.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/tutorial-quickstart-train-model.png)"
       ]
     },
     {

tutorials/tutorial-1st-experiment-sdk-train.yml

@@ -0,0 +1,5 @@
+name: tutorial-1st-experiment-sdk-train
+dependencies:
+- pip:
+  - azureml-sdk
+  - sklearn