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Practical Machine Learning and Image Processing For Facial Recognition, Object Detection, and Pattern Recognition Using Python

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Description

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in
Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring
Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the
core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll
see the OpenCV algorithms and how to use them for image processing. 

The next section looks at advanced
machine learning and deep learning methods for image processing and classification. You’ll work with concepts
such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific
applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools.


All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios.
After reading this book you will be able to apply image processing techniques and make machine learning models for
customized application.

What You Will Learn
Discover image-processing algorithms and their applications
using Python
Explore image processing using the OpenCV library
Use TensorFlow, scikit-learn, NumPy, and other
libraries
Work with machine learning and deep learning algorithms for image processing
Apply image-processing
techniques to five real-time projects

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