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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Is heuristic search artificial intelligence?

In Chapter 2, Fundamental Use Cases for Artificial Intelligence, we learned about the five tribes as defined by Pedro Domingos. One of the most "ancient" tribes is the symbolist tribe. At least to me, this fact is not surprising. As humans, we try to find rules and patterns in everything. Unfortunately, the world is sometimes messy and not everything follows simple rules.

This is the reason why other tribes emerged to help us when we don't have an orderly world. However, when our search spaces are small and the domain is limited, using heuristics, constraint satisfaction, and other techniques laid out in this chapter, is useful for this set of problems. These techniques are useful when the number of combinations is relatively small and combinatorial explosion is limited. For example, solving the traveling salesman problem is simple with these techniques when the number of cities is around 20. If we try to solve the...