diff --git a/README.md b/README.md
index fd2b42c..7f59521 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 
 Implementation of basic pattern recognition for anomaly detection.
 
-Implementation of analytic unit for Hastic.
+Implementation of analytics unit for Hastic.
 
 See also:
 * [Hooks](https://github.com/hastic/hastic-server/blob/master/HOOKS.md) - notifications about events
@@ -14,10 +14,10 @@ See also:
 
 ## Build & run
 
-Server needs Grafana's API key (http://<your_grafana_url>/org/apikeys) to query data from Grafana datasources.
-API key role needs only `Viewer` access. 
+Hastic server requires Grafana's API key (http://<your_grafana_url>/org/apikeys) to query data from Grafana datasources.
+API key role requires only `Viewer` access. 
 
-You can install it on:
+Possible to install on:
 
 * [Linux](#linux)
 * [Docker](#docker)
@@ -26,18 +26,18 @@ You can install it on:
 
 #### Environment variables
 
-You can export following environment variables for hastic-server to use:
+It is possible to export the following environment variables for hastic-server to use:
 - HASTIC_API_KEY - (required) API-key of your Grafana instance
 - HASTIC_PORT - (optional) port you want to run server on, default: 8000
 
 #### Dependencies
 
-You need in your system:
+System prerequisites:
 * [git](https://git-scm.com/download/linux)
 * [nodejs >= 6.0.0](https://nodejs.org/en/download/package-manager/#debian-and-ubuntu-based-linux-distributions)
 * [python3](https://www.python.org/downloads/) with pip3
 
-#### Intallation
+#### Installation
 ```bash
 pip3 install pandas seglearn scipy tsfresh
 
@@ -72,6 +72,6 @@ docker run -d --name hastic-server -p 80:8000 -e HASTIC_API_KEY=<your_grafana_ap
 
 ### Known bugs & issues
 
-- If you add labeled segments while learning - it fails
-- Dataset doesn't get updated after 1st learning
+- Adding labeled segments while learning is in progress is not supported
+- Dataset doesn't update after 1st learning
 - Currently only influxDB datasource is supported