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Machine Learning for OpenCV

You're reading from   Machine Learning for OpenCV Intelligent image processing with Python

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781783980284
Length 382 pages
Edition 1st Edition
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Authors (2):
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Michael Beyeler Michael Beyeler
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Michael Beyeler
Michael Beyeler (USD) Michael Beyeler (USD)
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Michael Beyeler (USD)
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Toc

Table of Contents (13) Chapters Close

Preface 1. A Taste of Machine Learning 2. Working with Data in OpenCV and Python FREE CHAPTER 3. First Steps in Supervised Learning 4. Representing Data and Engineering Features 5. Using Decision Trees to Make a Medical Diagnosis 6. Detecting Pedestrians with Support Vector Machines 7. Implementing a Spam Filter with Bayesian Learning 8. Discovering Hidden Structures with Unsupervised Learning 9. Using Deep Learning to Classify Handwritten Digits 10. Combining Different Algorithms into an Ensemble 11. Selecting the Right Model with Hyperparameter Tuning 12. Wrapping Up

Using regression models to predict continuous outcomes

Now let's turn our attention to a regression problem. As I'm sure you can recite in your sleep by now, regression is all about predicting continuous outcomes rather than predicting discrete class labels.

Understanding linear regression

The easiest regression model is called linear regression. The idea behind linear regression is to describe a target variable (such as Boston house pricing) with a linear combination of features.

To keep things simple, let's just focus on two features. Let's say we want to predict tomorrow's stock prices using two features: today's stock price and yesterday's stock price. We will denote today's stock...

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