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)
24
Other Books You May Enjoy
25
Index

Log transform

Logarithm transformation (or log transform) is a common feature engineering transformation. Log transform helps to flatten highly skewed values. After the log transformation is applied, the data distribution is normalized.

Let's go over another example to again gain some intuition. Remember when you were 10-year-old and looking at 15-year-old boys and girls and thinking "They are so much older than me!" Now think of a 50-year-old person and another that is 55-year-old. In this case, you might think that the age difference is not that much. In both cases, the age difference is 5 years. However, in the first case a 15-year-old is 50 percent older than the 10-year-old, and in the second case the 55-year-old is only 10 percent older than the 50-year-old.

If we apply a log transform to all these data points it normalizes magnitude differences like this.

Applying a log transform also decreases the effect of the outliers, due to the normalization...