Update README.md

README.md

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 Due to the development of IoT solutions, we can observe the constantly growing number of these devices in almost every aspect of our lives. The machine learning may improve increase their intelligence and smartness. Unfortunately, the highly regarded programming libraries consume to much resources to be ported to the embedded processors.
 
 The structure of the project is as follows:
-* the generators folder contains the source code generators for machine learning models i.e.: naive bayes, decision trees and neural nets;
-* the demos folder contains the example MNIST digit recognition for Arduino board and the simple TFT touchscreen.
+* the `src` folder contains the source code generators for machine learning models i.e.: naive bayes, decision trees/forrest and neural nets;
+* the `example` folder contains the simple examples and the MNIST digit recognition for Arduino board and the simple TFT touchscreen.
+
+## Usage
+
+`pip install fogml`
+
+
+# FogML research
 
 If you think that the project is interesting to you, please cite the paper:
 _Tomasz Szydlo, Joanna Sendorek, Robert Brzoza-Woch, Enabling machine learning on resource constrained devices by source code generation of the learned models, ICCS 2018_