
## Hands-on ML Projects with OpenCV

This is the repository for [Hands-on ML Projects with OpenCV](https://orangeava.com/products/hands-on-ml-projects-with-opencv), published by Orange AVA™


## About the Book
This book is an in-depth guide that merges machine learning techniques with OpenCV, the most popular computer vision library, using Python. The book introduces fundamental concepts in machine learning and computer vision, progressing to practical implementation with OpenCV. Concepts related to image preprocessing, contour and thresholding techniques, motion detection and tracking are explained in a step-by-step manner using code and output snippets. 

Hands-on projects with real-world datasets will offer you an invaluable experience in solving OpenCV challenges with machine learning. It’s an ultimate guide to explore areas like deep learning, transfer learning, and model optimization, empowering readers to tackle complex tasks. Every chapter offers practical tips and tricks to build effective ML models. 

By the end, you would have mastered and applied ML concepts confidently to real-world computer vision problems and will be able to develop robust and accurate machine-learning models for diverse applications. 

Whether you are new to machine learning or seeking to enhance your computer vision skills, This book is an invaluable resource for mastering the integration of machine learning and computer vision using OpenCV and Python.



## What you will learn

● Learn how to work with images and perform basic image processing tasks using OpenCV. 

● Implement machine learning techniques to computer vision tasks such as image classification, object detection, and image segmentation. 

● Work on real-world projects and datasets to gain hands-on experience in applying machine learning techniques with OpenCV. 

● Explore the concepts of deep learning using Tensorflow and Keras and how it can be used for computer vision tasks. 

● Understand the concept of transfer learning and how pre-trained models can be leveraged for new tasks. 

● Utilize techniques for model optimization and deployment in resource-constrained environments. 

● Implement end-to-end solutions and address challenges encountered in practical scenarios.
