new_contents: from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.linear_model import SGDClassifier
  from sklearn.externals import joblib
  
  print "Grabbing data..."
  
  training_text_collection_f = open("training_text_collection.pkl", "rb")
  training_text_collection = joblib.load(training_text_collection_f)
  training_text_collection_f.close()
  
  training_targets_f = open("training_targets.pkl", "rb")
  training_targets = joblib.load(training_targets_f)
  training_targets_f.close()
  
  print("Vectorizing data...")
  
  vectorizer = TfidfVectorizer(analyzer = "word",   \
                               tokenizer = None,    \
                               preprocessor = None, \
                               stop_words = "english",   \
                               max_features = 2500, \
                               min_df = 5, \
                               max_df = 0.5)
  
  train_tfidf = vectorizer.fit_transform(training_text_collection)
  
  save_vect = open("svm_tfidf_vect.pkl", "wb")
  joblib.dump(vectorizer, save_vect)
  save_vect.close()
  
  clf = SGDClassifier(loss='hinge', penalty='l2', alpha=1e-3, n_iter=5, random_state=42).fit(train_tfidf, training_targets)
  
  save_clf = open("svm_clf.pkl", "wb")
  joblib.dump(clf, save_clf)
  save_clf.close()
  
subject: Create Support Vector Machine vectorizer and classifier pickles