Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: 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)
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One-hot encoding

One-hot encoding is an often-used technique in machine learning for feature engineering. Some machine learning algorithms cannot handle categorical features, so one-hot encoding is a way to convert these categorical features into numerical features. Let's say that you have a feature labeled "status" that can take one of three values (red, green, or yellow). Because these values are categorical, there is no concept of which value is higher or lower. We could convert these values to numerical values and that would give them this characteristic. For example:

Yellow = 1

Red = 2

Green = 3

But this seems somewhat arbitrary. If we knew that red is bad and green is good, and yellow is somewhere in the middle, we might change the mapping to something like:

Red = -1

Yellow = 0

Green = 1

And that might produce better performance. But now let's see how this example can be one-hot encoded. To achieve the one-hot encoding of...