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 text classifier

The goal of text classification is to categorize text documents into different classes. This is an extremely important analysis technique in NLP. We will use a technique, which is based on a statistic called tf-idf, which stands for term frequency—inverse document frequency. This is an analysis tool that helps us understand how important a word is to a document in a set of documents. This serves as a feature vector that's used to categorize documents. You can learn more about it at

How to do it…

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

    from sklearn.datasets import fetch_20newsgroups
  2. Let's select a list of categories and name them using a dictionary mapping. These categories are available as part of the news groups dataset that we just imported:

    category_map = {'': 'Sales', '': 'Motorcycles', 
            '': 'Baseball', 'sci.crypt': 'Cryptography', 
            '': 'Space'}
  3. Load the...