Release 7.0 doc updates

README.md

@@ -34,6 +34,7 @@ The current implementation includes:
 - **Supervised Machine Learning** : Implemented using [scikit-learn](http://scikit-learn.org/stable/index.html), the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition, models can be interpreted using [Skater](https://datascienceinc.github.io/Skater/overview.html).
 - **Unsupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
 - **Deep Learning** : Implemented using [Keras](https://keras.io/) and [TensorFlow](https://www.tensorflow.org/). This SSE implements the full flow of setting up a neural network, training and evaluating it, and using it to make predictions. Deep Learning models can be used for sequence predictions and complex timeseries forecasting.
+- **Use of pretrained ML models in Qlik** : Pre-trained scikit-learn and Keras models can be called from this SSE, allowing predictions to be exposed within the broader analysis and business context of a Qlik app. The implementation also allows for What-if analysis using the models.
 - **Named Entity Recognition** : Implemented using [spaCy](https://spacy.io/), an excellent Natural Language Processing library that comes with pre-trained neural networks. This SSE allows you to use spaCy's models for Named Entity Recognition or retrain them with your data for even better results.
 - **Association rules** : Implemented using [Efficient-Apriori](https://github.com/tommyod/Efficient-Apriori). Association Rules Analysis is a data mining technique to uncover how items are associated to each other. This technique is best known for Market Basket Analysis, but can be used more generally for finding interesting associations between sets of items that occur together, for example, in a transaction, a paragraph, or a diagnosis.
 - **Clustering** : Implemented using [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html), a high performance algorithm that is great for exploratory data analysis.  
@@ -176,7 +177,7 @@ Most of the sample apps require the Dashboard Extension Bundle which was release
 | [Correlations](docs/Correlation.md) | [Correlations](docs/Sample_App_Correlations.qvf) | None. |
 | [Clustering](docs/Clustering.md) | [Clustering with HDBSCAN](docs/Sample_App_Clustering.qvf) | None. |
 | [Predictions with pretrained models](docs/Pretrained.md) | [Predictions with scikit-learn and Keras](docs/Sample-App-Pretrained-Predict.qvf) | Follow the [pre-requisites](docs/Pretrained.md#pre-requisites) and [steps](docs/Pretrained.md#complete-example) in the documentation.<br><br>If using Qlik Sense Desktop you will need to download the [data source](docs/HR-Employee-Attrition.xlsx), create a data connection named `AttachedFiles` in the app, and point the connection to the folder containing the source file. |
-| [Machine Learning](docs/scikit-learn.md)<br><br>[Using pre-trained models](docs/Pretrained.md) | [Train & Test](docs/Sample-App-scikit-learn-Train-Test.qvf)<br><br>[Predict](docs/Sample-App-scikit-learn-Predict.qvf)<br><br>[K-fold Cross Validation](docs/Sample-App-scikit-learn-K-fold-Cross-Validation.qvf)<br><br>[Parameter Tuning](docs/Sample-App-scikit-learn-Parameter-Tuning.qvf)<br><br>[K-fold CV & Parameter Tuning](docs/Sample-App-scikit-learn-K-fold-CV-Grid-Search.qvf)<br><br>[Complex Forecasting with scikit-learn](docs/Sample-App-Forecasting-with-Sklearn.qvf) | Make sure you reload the K-fold Cross Validation or Train & Test app before using the Predict app.<br><br>If using Qlik Sense Desktop you will need to download the [data source](docs/HR-Employee-Attrition.xlsx), create a data connection named `AttachedFiles` in the app, and point the connection to the folder containing the source file.<br><br>The forecasting app is best understood together with the Deep Learning section below. Here we just use more traditional ML algorithms rather than Deep Learning for producing the forecast.<br><br>Make sure you reload the app before using the final sheets to make predictions. The data source for this app can be found [here](docs/bike-sharing/). |
+| [Machine Learning](docs/scikit-learn.md) | [Train & Test](docs/Sample-App-scikit-learn-Train-Test.qvf)<br><br>[Predict](docs/Sample-App-scikit-learn-Predict.qvf)<br><br>[K-fold Cross Validation](docs/Sample-App-scikit-learn-K-fold-Cross-Validation.qvf)<br><br>[Parameter Tuning](docs/Sample-App-scikit-learn-Parameter-Tuning.qvf)<br><br>[K-fold CV & Parameter Tuning](docs/Sample-App-scikit-learn-K-fold-CV-Grid-Search.qvf)<br><br>[Complex Forecasting with scikit-learn](docs/Sample-App-Forecasting-with-Sklearn.qvf) | Make sure you reload the K-fold Cross Validation or Train & Test app before using the Predict app.<br><br>If using Qlik Sense Desktop you will need to download the [data source](docs/HR-Employee-Attrition.xlsx), create a data connection named `AttachedFiles` in the app, and point the connection to the folder containing the source file.<br><br>The forecasting app is best understood together with the Deep Learning section below. Here we just use more traditional ML algorithms rather than Deep Learning for producing the forecast.<br><br>Make sure you reload the app before using the final sheets to make predictions. The data source for this app can be found [here](docs/bike-sharing/). |
 | [Deep Learning](docs/Keras.md) | [Complex Forecasting with Keras](docs/Sample-App-Forecasting-with-Keras.qvf) | Make sure you reload the app before using the final two sheets to make predictions.<br><br>If using Qlik Sense Desktop you will need to download the [data source](docs/bike-sharing/), create a data connection named `AttachedFiles` in the app, and point the connection to the folder containing the source file. |
 | [Forecasting](docs/Prophet.md) | [Facebook Prophet (Detailed)](docs/Sample_App_Prophet.qvf)<br><br>[Facebook Prophet (Simple)](docs/Sample_App_Forecasting_Simple.qvf)<br><br>[Facebook Prophet (Multiple regressors)](docs/Sample-App-Prophet-Multivariate.qvf) | For the detailed app, use the bookmarks to step through the sheets with relevant selections.<br><br>For calling Prophet through the load script refer to the simple app. If you want to reload the app using Qlik Sense Desktop you will need to download the [data source](docs/VIC-Emergency-Department-Attendances.xlsx), create a data connection named `AttachedFiles` in the app, and point the connection to the folder containing the source file.<br><br>For the use of Prophet's additional regressors capability refer to the multiple regressors app. The data for this app is found [here](docs/bike-sharing/). |
 | [Named Entity Recognition](docs/NER.md) | [NER and Association Rules](docs/Sample-App-NER-Apriori.qvf) | If using Qlik Sense Desktop you will need to download the [data sources](docs/LOTR/), create a data connection named `AttachedFiles` in the app, and point the connection to the folder containing the source files. |

docs/README.md

@@ -34,6 +34,7 @@ The current implementation includes:
 - **Supervised Machine Learning** : Implemented using [scikit-learn](http://scikit-learn.org/stable/index.html), the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition, models can be interpreted using [Skater](https://datascienceinc.github.io/Skater/overview.html).
 - **Unsupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
 - **Deep Learning** : Implemented using [Keras](https://keras.io/) and [TensorFlow](https://www.tensorflow.org/). This SSE implements the full flow of setting up a neural network, training and evaluating it, and using it to make predictions. Deep Learning models can be used for sequence predictions and complex timeseries forecasting.
+- **Use of pretrained ML models in Qlik** : Pre-trained scikit-learn and Keras models can be called from this SSE, allowing predictions to be exposed within the broader analysis and business context of a Qlik app. The implementation also allows for What-if analysis using the models.
 - **Named Entity Recognition** : Implemented using [spaCy](https://spacy.io/), an excellent Natural Language Processing library that comes with pre-trained neural networks. This SSE allows you to use spaCy's models for Named Entity Recognition or retrain them with your data for even better results.
 - **Association rules** : Implemented using [Efficient-Apriori](https://github.com/tommyod/Efficient-Apriori). Association Rules Analysis is a data mining technique to uncover how items are associated to each other. This technique is best known for Market Basket Analysis, but can be used more generally for finding interesting associations between sets of items that occur together, for example, in a transaction, a paragraph, or a diagnosis.
 - **Clustering** : Implemented using [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html), a high performance algorithm that is great for exploratory data analysis.