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Artificial Intelligence with Python

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

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839219535
Length 618 pages
Edition 2nd Edition
Languages
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Authors (2):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
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Table of Contents (26) Chapters Close

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...

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