address some comments for tutorial

samples/1.1 [Numeric] Classification with Synthetic Data.ipynb

@@ -360,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

samples/1.2 [Text] Twitter Sentiment 1.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sentimental Analysis Using Twitter Data 1 - Loading with Pandas\n",
+    "# Sentiment Analysis Using Twitter Data 1 - Loading with Pandas\n",
     "\n",
     "In this example, we develop a binary classifier using the manually generated Twitter data to detect the sentiment of each tweet. For example, \"This is awesome!\" will be a positive one and \"I am sad\" will be negative. The input data is the text and we use nimbusml NGramFeaturizer to extract numeric features and input them to a AveragedPerceptron classifier."
    ]

samples/2.1 [Text] Fast Data Loading with Schema - Twitter Sentiment 2.ipynb

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sentimental Analysis Using Twitter Data 2 - Loading with NimbusML\n",
+    "# Streaming Data Loading with NimbusML - Sentiment Analysis Using Twitter Data 2\n",
     "\n",
-    "In this example, we develop a similar model as in the tutorial for Twitter Data 1. Instead of loading data in pandas, we load the data with nimbusml and the model can be simply trained using the input file name. Instead of saving the whole dataset in memory, nimbusml processes the data by passing a DataFileStream in the training/testing process to achieve exponentially speed up."
+    "In this example, we develop a similar model as in the tutorial for [Twitter Data 1](https://docs.microsoft.com/en-us/nimbusml/tutorials/a_b-twitter-sentiment-1). Instead of loading data in pandas, we load the data with nimbusml and the model can be simply trained using the input file name. Instead of saving the whole dataset in memory, nimbusml processes the data by passing a DataFileStream in the training/testing process to achieve exponentially speed up."
    ]
   },
   {

samples/2.2 [Numeric] Syntax for Column Selection - Classification Using Flight Schedule Data.ipynb

@@ -101,7 +101,7 @@
     }
    ],
    "source": [
-    "display(Image(filename='images/FDFigure.png')) #TO DO: REPLACE WITH GRAPH GENERATOR"
+    "display(Image(filename='images/FDFigure.png'))"
    ]
   },
   {
@@ -199,7 +199,9 @@
     "\n",
     "            OneHotVectorizer(columns = {'UniqueCarrier':'UniqueCarrier' , 'Origin':'Origin', 'Dest':'Dest'})\n",
     "\n",
-    "For each operator, just like creating a neural network, the input and output is specified in a dictionary/list. If the input column names are not specified, all the input columns from previous transformation will be used. "
+    "For each operator, just like creating a neural network, the input and output is specified in a dictionary/list. If the input column names are not specified, all the input columns from previous transformation will be used. \n",
+    "\n",
+    "For more details about the column operations for transforms, please refer to our [documentation](https://docs.microsoft.com/en-us/nimbusml/concepts/columns)."
    ]
   },
   {
@@ -222,7 +224,9 @@
     "\n",
     "            LightGbmBinaryClassifier(feature = categorical_columns + numeric_columns, label = 'Label')\n",
     "\n",
-    "Indicates that the input features for LightGbmBinaryClassifier are columns  categorical_columns + numeric_columns, and the label column is the column named 'Label'. Other roles are Role.GroupId, Role.Weight, etc.. If the label role was specified, user can use ppl.fit(data) directly without setting the y. "
+    "Indicates that the input features for LightGbmBinaryClassifier are columns  categorical_columns + numeric_columns, and the label column is the column named 'Label'. Other roles are Role.GroupId, Role.Weight, etc.. If the label role was specified, user can use ppl.fit(data) directly without setting the y. \n",
+    "\n",
+    "For more details about the column operations for learners, please refer to our [documentation](https://docs.microsoft.com/en-us/nimbusml/concepts/roles)."
    ]
   },
   {

samples/2.3 [Image] Image Processing - Clustering.ipynb

@@ -317,7 +317,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...</td>\n",
+       "      <td>D:\\nimbusml_github\\...\\src\\py...</td>\n",
        "      <td>https://express-tlcresources.azureedge.net/dat...</td>\n",
        "      <td>dog</td>\n",
        "      <td>0.0</td>\n",
@@ -341,7 +341,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...</td>\n",
+       "      <td>D:\\nimbusml_github\\...\\src\\py...</td>\n",
        "      <td>https://express-tlcresources.azureedge.net/dat...</td>\n",
        "      <td>fruit</td>\n",
        "      <td>0.0</td>\n",
@@ -365,7 +365,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...</td>\n",
+       "      <td>D:\\nimbusml_github\\...\\src\\py...</td>\n",
        "      <td>https://express-tlcresources.azureedge.net/dat...</td>\n",
        "      <td>dog</td>\n",
        "      <td>0.0</td>\n",
@@ -389,7 +389,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...</td>\n",
+       "      <td>D:\\nimbusml_github\\...\\src\\py...</td>\n",
        "      <td>https://express-tlcresources.azureedge.net/dat...</td>\n",
        "      <td>fruit</td>\n",
        "      <td>0.0</td>\n",
@@ -413,7 +413,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...</td>\n",
+       "      <td>D:\\nimbusml_github\\...\\src\\py...</td>\n",
        "      <td>https://express-tlcresources.azureedge.net/dat...</td>\n",
        "      <td>fruit</td>\n",
        "      <td>0.0</td>\n",
@@ -442,11 +442,11 @@
       ],
       "text/plain": [
        "                                           ImagePath  \\\n",
-       "0  D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...   \n",
-       "1  D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...   \n",
-       "2  D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...   \n",
-       "3  D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...   \n",
-       "4  D:\\nimbusml_github\\ML.NET-for-Python_Alpha\\src\\py...   \n",
+       "0  D:\\nimbusml_github\\...\\src\\py...   \n",
+       "1  D:\\nimbusml_github\\...\\src\\py...   \n",
+       "2  D:\\nimbusml_github\\...\\src\\py...   \n",
+       "3  D:\\nimbusml_github\\...\\src\\py...   \n",
+       "4  D:\\nimbusml_github\\...\\src\\py...   \n",
        "\n",
        "                                      ImagePath_full  Label  Relu_1.0  \\\n",
        "0  https://express-tlcresources.azureedge.net/dat...    dog       0.0   \n",