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

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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

Feature engineering

According to a recent survey performed by the folks at Forbes, data scientists spend around 80% of their time on data preparation:

https://miro.medium.com/max/1200/0*-dn9U8gMVWjDahQV.jpg

Figure 4: Breakdown of time spent by data scientists (source: Forbes)

This statistic highlights the importance of data preparation and feature engineering in data science.

Just like judicious and systematic feature selection can make models faster and more performant by removing features, feature engineering can accomplish the same by adding new features. This seems contradictory at first blush, but the features that are being added are not features that were removed by the feature selection process. The features being added are features that might have not been in the initial dataset. You might have the most powerful and well-designed machine learning algorithm in the world, but if your input features are not relevant, you will never be able to produce useful results. Let's analyze a couple of simple examples to get...