Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?If the answer to any of the above questions is "YES", then this course is for you.Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using TensorFlow and OpenCV. As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :"Short, sweet, to the point course"The same techniques can be used in :Skin cancer detection Normal pneumonia detection Brain
Build on your existing knowledge with intermediate tensorflow techniques and real-world applications.
Build on your existing knowledge with intermediate deep learning techniques and real-world applications.
Build on your existing knowledge with intermediate data science techniques and real-world applications.
Build on your existing knowledge with intermediate computer vision techniques and real-world applications.
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