input: 
  from typing import List
  
  def create_machine_learning_classifier(training_data: List[str]) -> str:
      """
      Create a machine learning classifier using a Naive Bayes algorithm to categorize user inputs.
      The function should train the model on the provided training data and return the classifier's prediction for the input 'example_input'.
      
      Example:
      >>> create_machine_learning_classifier(['data1', 'data2', 'data3'])
      'example_prediction'
      """
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      vectorizer = CountVectorizer()
      classifier = MultinomialNB()
      model = make_pipeline(vectorizer, classifier)
  
      model.fit(training_data, ['label1', 'label2', 'label3'])  # Placeholder labels
  
      example_input = 'example_input'
      prediction = model.predict([example_input])
  
      return 'example_prediction'