You're reading from Artificial Intelligence with Python - Second Edition

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Pages 618 pages

Edition 2nd Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Prateek Joshi

Table of Contents (26) Chapters

Preface

1. Introduction to Artificial Intelligence

2. Fundamental Use Cases for Artificial Intelligence

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

Computing similarity scores

To build a recommendation system, it is important to understand how to compare various objects in the dataset. If the dataset consists of people and their various movie preferences, then in order to make a recommendation we need to understand how to compare any two people with one another. This is where the similarity score is important. The similarity score gives an idea of how similar two data points are.

There are two scores that are used frequently in this domain – the Euclidean score and the Pearson score. The Euclidean score uses the Euclidean distance between two data points to compute the score. If you need a quick refresher on how Euclidean distance is computed, you can go to:

https://en.wikipedia.org/wiki/Euclidean_distance

The value of the Euclidean distance can be unbounded. Hence, we take this value and convert it in a way that the Euclidean score ranges from 0 to 1. If the Euclidean distance between two objects is large...