Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Building a bag-of-words model

When we deal with text documents that contain millions of words, we need to convert them into some kind of numeric representation. The reason for this is to make them usable for machine learning algorithms. These algorithms need numerical data so that they can analyze them and output meaningful information. This is where the bag-of-words approach comes into picture. This is basically a model that learns a vocabulary from all the words in all the documents. After this, it models each document by building a histogram of all the words in the document.

How to do it…

  1. Create a new Python file, and import the following packages:

    import numpy as np
    from nltk.corpus import brown
    from chunking import splitter
  2. Let's define the main function. Load the input data from the Brown corpus:

    if __name__=='__main__':
        # Read the data from the Brown corpus
        data = ' '.join(brown.words()[:10000])
  3. Divide the text data into five chunks:

        # Number of words in each chunk