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Machine Learning for OpenCV 4

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

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789536300
Length 420 pages
Edition 2nd Edition
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Authors (4):
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Aditya Sharma Aditya Sharma
Author Profile Icon Aditya Sharma
Aditya Sharma
Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
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Table of Contents (18) Chapters Close

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

Preface

As the world changes and humans build smarter and better machines, the demand for machine learning and computer vision experts increases. Machine learning, as the name suggests, is the process of a machine learning to make predictions given a certain set of parameters as input. Computer vision, on the other hand, gives a machine vision; that is, it makes the machine aware of visual information. When you combine these technologies, you get a machine that can use visual data to make predictions, which brings machines one step closer to having human capabilities. When you add deep learning to it, the machine can even surpass human capabilities in terms of making predictions. This might seem far-fetched, but with AI systems taking over decision-based systems, this has actually become a reality. You have AI cameras, AI monitors, AI sound systems, AI-powered processors, and more. We cannot promise you that you will be able to build an AI camera after reading this book, but we do intend to provide you with the tools necessary for you to do so. The most powerful tool that we are going to introduce is the OpenCV library, which is the world's largest computer vision library. Even though its use in machine learning is not very common, we have provided some examples and concepts on how it can be used for machine learning. We have gone with a hands-on approach in this book and we recommend that you try out every single piece of code present in this book to build an application that showcases your knowledge. The world is changing and this book is our way of helping young minds change it for the better.

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