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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

Understanding the regression task


Recall that the regression task is of a particular case of supervised learning, where real numbers take the place of labels. It is the primary difference from the classification, where all labels are categories. You can use regression analysis to study the interactions between two or more variables; for example, the way personal computer price depends on the computer's characteristics, such as a number of CPU cores and the type, memory size, video card characteristics, and storage type and size. In the context of regression, we usually call features independent variables and labels dependent variables. In our example, independent variables are the computer's characteristics and the dependent variable is its price. Having a regression model, we can predict which machine is better to buy. Moreover, regression allows you to make educated guesses about the contribution of each feature to the final price. Could be an idea for the next viral app.

Regression analysis...

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