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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What is classification?

In this section, we will discuss supervised classification techniques. The classification process is a technique used to arrange data into a fixed number of categories so that it can be used effectively and efficiently.

In machine learning, classification is used to identify the category to which a new datapoint belongs. A classification model is built based on the training dataset containing datapoints and the corresponding labels. For example, let's say that we want to determine whether a given image contains a person's face or not. We would build a training dataset containing classes corresponding to two classes: face and no-face. A model would then be trained based on the available training samples. The trained model can then be used for inference.

A good classification system makes it easy to find and retrieve data. Classification is used extensively in face recognition, spam identification, recommendation engines, and so on. A good...