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 are decision trees?

A decision tree is a way to partition a dataset into distinct branches. The branches or partitions are then traversed to make simple decisions. Decision trees are produced by training algorithms, which identify how to split the data in an optimal way.

The decision process starts at the root node at the top of the tree. Each node in the tree is a decision rule. Algorithms construct these rules based on the relationship between the input data and the target labels in the training data. The values in the input data are utilized to estimate the value of the output.

Now that we understand the basic concept behind decision trees, the next concept to understand is how the trees are automatically constructed. We need algorithms that can construct the optimal tree based on the data. In order to understand it, we need to understand the concept of entropy. In this context, entropy refers to information entropy and not thermodynamic entropy. Information entropy is...