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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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Toc

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

Supervised versus unsupervised learning

It's not hard to see from looking at the popular press that one of the hottest areas in artificial intelligence today is machine learning. Machine learning is commonly classified into supervised and unsupervised learning. Other classifications exist, but we'll discuss those later.

Let's get some intuitive understanding about supervised learning versus unsupervised learning before we give a more formal definition. Assume you have a set of portraits of people. The people in this set are a very diverse group of men and women and you have all kinds of nationalities, ages, body weights, and so on. Initially, you put the dataset through an unsupervised learning algorithm. In this case, without any a priori knowledge, the unsupervised algorithm will start classifying these photographs depending on some feature that it recognizes as similar. For example, on its own, it might start recognizing that men and women are different, and it...

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