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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Converting text to its base form using lemmatization


The goal of lemmatization is also to reduce words to their base forms, but this is a more structured approach. In the previous recipe, we saw that the base words that we obtained using stemmers don't really make sense. For example, the word "wolves" was reduced to "wolv", which is not a real word. Lemmatization solves this problem by doing things using a vocabulary and morphological analysis of words. It removes inflectional word endings, such as "ing" or "ed", and returns the base form of a word. This base form is known as the lemma. If you lemmatize the word "wolves", you will get "wolf" as the output. The output depends on whether the token is a verb or a noun. Let's take a look at how to do this in this recipe.

How to do it…

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

    from nltk.stem import WordNetLemmatizer
  2. Let's define the same set of words that we used during stemming:

    words = ['table', 'probably', 'wolves', 'playing'...