Machine Learning for Beginners in Python: How to Find a Bag Of Words

Bag Of Words


import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd

Create Text Data

text_data = np.array(['I love Brazil. Brazil!',
                      'Sweden is best',
                      'Germany beats both'])

Create Bag Of Words

count = CountVectorizer()
bag_of_words = count.fit_transform(text_data)

array([[0, 0, 0, 2, 0, 0, 1, 0],
       [0, 1, 0, 0, 0, 1, 0, 1],
       [1, 0, 1, 0, 1, 0, 0, 0]], dtype=int64)

View Bag Of Words Matrix Column Headers

feature_names = count.get_feature_names()

['beats', 'best', 'both', 'brazil', 'germany', 'is', 'love', 'sweden']

View As A Data Frame

pd.DataFrame(bag_of_words.toarray(), columns=feature_names)
beats best both brazil germany is love sweden
0 0 0 0 2 0 0 1 0
1 0 1 0 0 0 1 0 1
2 1 0 1 0 1 0 0 0



Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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