text: #hyperparemeters->humans, paremeters->machine
  #Classification & Regression
  
  #Binary Classification: get True or False
  #Multi-class Classification: get n answers
  #Regression_continue: get result value
  
  #Supervised Learning: learn with Label(answer)_reducing error value between label and prediction
  #Unsupervised Learning: learn without label(objective data)_ex. clustering
  #Reinforced Learning: title is text
  
  #Sample: one data or one row in machine learning or one record in Database
  #Feature: independant variable x(column in record)
  
  #Confusion Matrix: TP FN FP TN for feedback in accuracy
  #Precision=TP/(TP+FP) _TP on returned positive
  #Recall=TP/(TP+FN) _TP on real positive
  
  #fitting = The processing of training
  #Overtitting: error on training data < error on testing data_ depended by Dropout, Early Stopping, etc..
  #Underfitting: error on training data >=< error on testing data
  