Update to README.md

Fixed a typo!

README.md

@@ -21,7 +21,7 @@ Sample Qlik Sense apps are included and explained so that the techniques shown h
 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).
-- **Unupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
+- **Unsupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
 - **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. 
 - **Time series forecasting** : Implemented using [Facebook Prophet](https://research.fb.com/prophet-forecasting-at-scale/), a modern library for easily generating good quality forecasts.
 - **Seasonality and holiday analysis** : Also using Facebook Prophet.

docs/README.md

@@ -21,7 +21,7 @@ Sample Qlik Sense apps are included and explained so that the techniques shown h
 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).
-- **Unupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
+- **Unsupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
 - **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.  
 - **Time series forecasting** : Implemented using [Facebook Prophet](https://research.fb.com/prophet-forecasting-at-scale/), a modern library for easily generating good quality forecasts.
 - **Seasonality and holiday analysis** : Also using Facebook Prophet.