Description
Computer Vision: Face Recognition Quick Starter in Python is a training course on building a deep learning model based on Python to discover and recognize faces, emotions, gender and age. This is the second course of the Computer Vision training series. Face detection and recognition are the most important and widely used parts of machine vision. Using the techniques taught in this course, computers will be able to extract one or more faces from an image or video and then match it with the available data to recognize the people in the image. Face detection and facial recognition are widely used by governments and organizations for security and care issues, and of course we use these capabilities in everyday use in programs such as unlocking the mobile phone screen with the face. This course is an introductory course for people who want to learn how to use face recognition using Python without working with the complex mathematics involved in deep learning.
What you will learn in the Computer Vision: Face Recognition Quick Starter in Python course:
- Face detection and recognition using images
- Face detection and recognition using live image file
- Recognition of emotion from the face
- Prediction of gender-age
Course details
Publisher: Udemy
Instructors: Abhilash Nelson
English language
Training level: introductory to advanced
Number of courses: 87
Duration: 9 hours and 20 minutes
Course topics Computer Vision: Face Recognition Quick Starter in Python 2022-7
Course prerequisites:
A decent computer configuration and an enthusiasm to dive into the world of computer vision based Face Recognition
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
Subtitle: No (based on the course specifications in the picture, this course does not have subtitles.)
Quality: 720
Changes:
Version 2022/1 has increased the number of 45 lessons and the total duration of lessons by 5 hours and 2 minutes compared to version 2020/5.
download link
Password file(s): www.downloadly.ir
Size
6.02 GB
Be the first to comment