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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In this chapter, we discussed:

  • What AI is all about and why we need to study it
  • Various applications and branches of AI
  • What the Turing test is and how it's conducted
  • How to make machines think like humans
  • The concept of rational agents and how they should be designed
  • General Problem Solver (GPS) and how to solve a problem using GPS
  • How to develop an intelligent agent using machine learning
  • Different types of machine learning models

We also went through how to install Python 3 on various operating systems, and how to install the necessary packages required to build AI applications. We discussed how to use these packages to load data that's available in scikit-learn.

In the next chapter, we will learn about supervised learning and how to build models for classification and regression.