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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Length 642 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (18) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Operating on images using OpenCV-Python

In this chapter, we will use a library called Open Source Computer Vision Library (OpenCV), to analyze images. OpenCV is the world's most popular library for computer vision. As it has been highly optimized for many different platforms, it has become the de facto standard in the industry. Before you proceed, make sure that you install the library with Python support. You can download and install OpenCV at http://opencv.org. For detailed installation instructions on various operating systems, you can refer to the documentation section on the website.

Getting ready

In this recipe, we will take a look at how to operate on images using OpenCV-Python. In this recipe, we will look at...

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