featureScaling <- function(X){
  
  #   Normalizes the features in X 
  #
  #   Xnorm, mu, sigma <- featureScaling(X) returns a normalized version 
  #   of X where the mean value of each feature is 0 and the standard deviation
  #   is 1. This is often a good preprocessing step to do when
  #   working with learning algorithms.
  
  # ====================== YOUR CODE HERE ========================
  # Instructions: First, for each feature dimension, compute the mean
  #               of the feature and subtract it from the dataset,
  #               storing the mean value in mu. Next, compute the 
  #               standard deviation of each feature and divide
  #               each feature by it's standard deviation, storing
  #               the standard deviation in sigma. 
  #
  #               Note that X is a matrix where each column is a 
  #               feature and each row is an example. You need 
  #               to perform the normalization separately for 
  #               each feature. 
  #
  # Hint: You might find the 'mean' and 'sd' functions useful, 
  #       or alternatively, the function 'scale'.
  
  
  
  # ==============================================================
  
  return(list(Xnorm = Xnorm, mu = mu, sigma = sigma))
}