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

Logistic regression classifiers

Logistic regression is a technique that is used to explain the relationship between input variables and output variables. Regression can be used to make predictions on continuous values, but it can also be useful to make discrete predictions where the result is True or False, for example, or Red, Green, or Yellow as another example.

The input variables are assumed to be independent and the output variable is referred to as the dependent variable. The dependent variable can take only a fixed set of values. These values correspond to the classes of the classification problem.

Our goal is to identify the relationship between the independent variables and the dependent variables by estimating the probabilities using a logistic function. This logistic function in this case will be a sigmoid curve that's used to build the function with various parameters. Some of the reasons a sigmoid function is used in logistic regression models are:

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