You're reading from Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Length 618 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Prateek Joshi

Alberto Artasanchez

View More author details

Table of Contents (26) Chapters

Preface

1. Introduction to Artificial Intelligence

2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER

3. Machine Learning Pipelines

4. Feature Selection and Feature Engineering

5. Classification and Regression Using Supervised Learning

6. Predictive Analytics with Ensemble Learning

7. Detecting Patterns with Unsupervised Learning

8. Building Recommender Systems

9. Logic Programming

10. Heuristic Search Techniques

11. Genetic Algorithms and Genetic Programming

12. Artificial Intelligence on the Cloud

13. Building Games with Artificial Intelligence

14. Building a Speech Recognizer

15. Natural Language Processing

16. Chatbots

17. Sequential Data and Time Series Analysis

18. Image Recognition

19. Neural Networks

20. Deep Learning with Convolutional Neural Networks

21. Recurrent Neural Networks and Other Deep Learning Models

22. Creating Intelligent Agents with Reinforcement Learning

23. Artificial Intelligence and Big Data

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...