Small edit for machinelearning.html

machinelearning.html

@@ -78,7 +78,7 @@ <h2>Advantages over traditional statistical approaches</h2>
 										tends to be computationally very inexpensive in comparison to traditional numerical methods. Another key advantage for using some machine learning 
 										techniques that is extremely valuable for remote sensing in particular, is that the trained model can be updated with new information as more is 
 										acquired; it can be an online learner (Zhang et al., 2017). Many similar machine learning techniques would require a completely new model to be produced 
-										from scratch when more data is ready to be added. However, some (e.g. Long-Short Term Memory) allow the user to make continuous predictions while 
+										from scratch when more data is ready to be added. However, some (e.g., Long-Short Term Memory) allow the user to make continuous predictions while 
 										continuously updating to a stronger, better informed model with new incoming data. Some require no parameter fine-tuning (Hochreiter and Schmidhuber, 2006). 
 										Some also generalize well, can handle sparse data, and can rapidly learn to distinguish between widely separated events that would be entirely missed by 
 										traditional models or human observation.</p>