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You're reading from Machine Learning for OpenCV 4 Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn

Product type Paperback

Published in Sep 2019

Publisher Packt

ISBN-13 9781789536300

Length 420 pages

Edition 2nd Edition

Languages

C++

Tools

OpenCV

Concepts

Computer Vision

Authors (4):

Aditya Sharma

Michael Beyeler (USD)

Vishwesh Ravi Shrimali

Michael Beyeler

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Table of Contents (18) Chapters

Preface

1. Section 1: Fundamentals of Machine Learning and OpenCV FREE CHAPTER

2. A Taste of Machine Learning

3. Working with Data in OpenCV

4. First Steps in Supervised Learning

5. Representing Data and Engineering Features

6. Section 2: Operations with OpenCV

7. Using Decision Trees to Make a Medical Diagnosis

8. Detecting Pedestrians with Support Vector Machines

9. Implementing a Spam Filter with Bayesian Learning

10. Discovering Hidden Structures with Unsupervised Learning

11. Section 3: Advanced Machine Learning with OpenCV

12. Using Deep Learning to Classify Handwritten Digits

13. Ensemble Methods for Classification

14. Selecting the Right Model with Hyperparameter Tuning

15. Using OpenVINO with OpenCV

16. Conclusion

17. Other Books You May Enjoy

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Installation

Before we get started, let's make sure that we have all the tools and libraries installed that are necessary to create a fully functioning data science environment. After downloading the latest code for this book from GitHub, we are going to install the following software:

Python's Anaconda distribution, based on Python 3.6 or higher
OpenCV 4.1
Some supporting packages

Don't feel like installing stuff? You can also visit https://mybinder.org/v2/gh/PacktPublishing/Machine-Learning-for-OpenCV-Second-Edition/master, where you will find all the code for this book in an interactive, executable environment and 100% free and open source, thanks to the Binder project.

Getting the latest code for this book

...

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Authors (4)

Michael Beyeler (USD)

Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.

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Sharma

Tushar Sharma has a master's degree specializing in data science from the International Institute of Information Technology, Bangalore. He works as a data scientist with Intel. In his previous job he used to work as a research engineer for a financial consultancy firm. His work involves handling big data at scale generated by the massive infrastructure at Intel. He engineers and delivers end to end solutions on this data using the latest machine learning tools and frameworks. He is proficient in R, Python, Spark, and mathematical aspects of machine learning among other things. Tushar has a keen interest in everything related to technology. He likes to read a wide array of books ranging from history to philosophy and beyond. He is a running enthusiast and likes to play badminton and tennis.

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Vishwesh Ravi Shrimali

Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

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Michael Beyeler

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