Book Image

Artificial Intelligence with Python - Second Edition

By : Alberto Artasanchez, Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Alberto Artasanchez, Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
24
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25
Index

Label encoding

When performing classification, we usually deal with lots of labels. These labels can be in the form of words, numbers, or something else. Many machine learning algorithms require numbers as input. So, if they are already numbers, they can be directly used for training. But this is not always the case.

Labels are normally words, because words can be understood by humans. Training data is labeled with words so that the mapping can be tracked. To convert word labels into numbers, a label encoder can be used. Label encoding refers to the process of transforming word labels into numbers. This enables the algorithms to be able to process the data. Let's look at an example:

Create a new Python file and import the following packages:

import numpy as np
from sklearn import preprocessing

Define some sample labels:

# Sample input labels
input_labels = ['red', 'black', 'red', 'green', 'black', &apos...