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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Date manipulation

Time features can be of critical importance for some data science problems. In time series analysis, dates are obviously critical. Predicting that the S&P 500 is going to 3,000 means nothing if you don't attach a date to the prediction.

Dates without any processing might not provide much significance to most models and the values are going to be too unique to provide any predictive power. Why is 10/21/2019 different from 10/19/2019? If we use some of the domain knowledge, we might be able to greatly increase the information value of the feature. For example, converting the date to a categorical variable might help. If the target feature is that you are trying to determine when rent is going to get paid, convert the date to a binary value where the possible values are:

  • Before the 5th of the month = 1
  • After the 5th of the month = 0

If you are asked to predict foot traffic and sales at a restaurant, there might not be any...