Python comes with many freely available powerful modules for handling images, mathematical computing, and data mining which makes it an ideal language for rapidly prototyping and developing production-grade codes for image processing and computer vision. If you wish to build computer vision applications that are smarter, faster, more complex, and more practical with Python 3, then you should surely go for this Learning Path.
This comprehensive 2-in-1 course aims to equip you to build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that you will learn in this course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet. You will also learn state-of-the-art techniques to classify images, and to find and identify humans within videos. This learning path will give you a versatile range of computer vision techniques with Python 3, which you will put to work in building your own computer vision applications.
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Python 3.x for Computer Vision, starts off with an introduction to image processing. You will then learn features and filters in computer vision. You will also implement feature detection algorithms such as LBP and ORB. Finally, you will understand convolutional neural networks to learn patterns in images. Throughout this course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms.
The second course, Computer Vision Projects with Python 3, starts off by showing you how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision. You will then learn state-of-the-art techniques to classify images and find and identify humans within videos. Next, you will learn to augment Python with the powerful vision and machine learning tools such as OpenCV and TensorFlow. Finally, you will learn to detect facial features and develop a general image classifier.
By the end of this Learning Path, you will be able to build computer vision applications that are capable of working in real-world scenarios effectively.
About the Authors :
Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi. His interests are in computer vision, numerical analysis, and algorithm design. He often spends time-solving competitive programming questions. Saurabh also enjoys working on IoT applications and tinkering with hardware. He likes to spend his free time playing or watching cricket.
Matthew Rever is an image processing and computer vision engineer at a major national laboratory. He has years of experience automating the analysis of complex scientific data, as well as the control of sophisticated instruments. He has applied computer vision technology to save a great many hours of valuable human labor. He is also enthusiastic about making the latest developments in computer vision accessible to developers of all backgrounds.
