Face Recognition using Python Language Simple step by step approach for developing applications for Face Recognition Highest Rated Rating: 4.6 out of 54.6 (97 ratings) 14,802 students Description Face Recognition is one of the popular domains in Artificial Intelligence having applications in Employee attendance management, Student attendance management, Immigration, Travel Documentation, Surveillance of campuses, and many more. In this course, we teach an ordinary beginner to develop Python applications for very useful AI applications using Face Recognition. This course begins with a historical perspective of Face recognition and its importance in the AI-enabled world. We also give the algorithms used in research for some high-level understanding. We first write a Python program to read an image and identify all faces in the image.

Part 1- The first part is about setting up the docker container for detectron2. The architecture of the detection model is a Faster region proposal convolutional neural network (FRCNN) with a Feature pyramid network(FPN) and the backbone is resnet101. We will learn the steps to train a multiclass model. Detectron2 is created by the Facebook research team. This is the official GitHub repository of Detectron2.

The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture…

The recent developments in Artificial Intelligence (AI) technologies challenge educators and educational institutions to respond with curriculum and resources that prepare students of all ages with the foundational knowledge and skills for success in the AI workplace. Research on AI Literacy could lead to an effective and practical platform for developing these skills. We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education. Such a discipline requires moving beyond a conceptual framework to a multi-level competency model with associated competency assessments. This approach to an AI Literacy could guide future development of instructional content as we prepare a range of groups (i.e., consumers, co-workers, collaborators, and creators). We propose here a research matrix as an initial step in the development of a roadmap for AI Literacy research, which requires a systematic and coordinated effort with the support of publication outlets and research funding, to expand the areas of competency and assessments.

Statistical Decision Making in Data Science with Case Study Understand how Statistics is Applied to Data Science Problem like ANOVA, t-test, F-test in Python Rating: 4.8 out of 54.8 (34 ratings) 16,792 students Description Welcome to the course "Statistical Decision Making in Data Science with a Case Study in Python" You will learn the approaches towards regression with case study. First we start with understanding linear equation and the optimization function value sum of squared errors. With that we find the values of the coefficient and makes least square regression. Then we starts building our linear regression in python. For the model we build we necessary test like hypothesis testing.

Children who learn computer science skills such as coding gain a multitude of benefits in other areas, including problem solving, creative thinking and mathematics, according to a new study by OKdo. For a new report titled Broader Benefits of Learning to Code, the global tech company gathered survey responses from almost 7,000 UK teachers and parents (with children aged 5-16), in which 96% of teachers claimed to have seen first-hand evidence of how computer science lessons can help to improve both hard and soft skills, as well as IT abilities, in children. Overall, eight in 10 (82%) of teachers said computer science education boosts pupils' problem solving capabilities. On top of this, two thirds (68%) agreed that it helps them develop expertise in mathematics, while six in 10 (60%) claimed that lessons in the subject also positively impacts creative thinking in young people. Over a third (35%) felt that teaching coding can boost children's organisational and time management skills, with 34% also feeling that participating in the subject can improve young people's ability to work as part of team.

The Snakes AI Competition was held by the Innopolis University and was part of the IEEE Conference on Games2020 and 2021 editions. It aimed to create a sandbox for learning and implementing artificial intelligence algorithms in agents in a ludic manner. Competitors of several countries participated in both editions of the competition, which was streamed to create asynergy between organizers and the community. The high-quality submissions and the enthusiasm around the developed framework create an exciting scenario for future extensions.

In this tutorial, you will learn how to train your first neural network using the PyTorch deep learning library. To learn how to train your first neural network with PyTorch, just keep reading. We'll start by reviewing our project directory structure and then configuring our development environment. From there, we'll implement two Python scripts: With our two Python scripts implemented, we'll move on to training our network. To follow this guide, you need to have the PyTorch deep learning library and the scikit-machine learning package installed on your system.

Data Science applications are everywhere in our regular life. Every sector is revolutionizing Data Science applications, including Healthcare, IT, Media, Entertainment, and many others. Today, healthcare industries are utilizing the power of Data Science successfully, and today we are going to disclose the use of Data Science in Healthcare. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning. This course is designed for both beginners & experienced with some python & machine learning skills.

Gift This Online Course What you'll learn Description Comprehensive Course Description: Convolutional Neural Networks (CNNs) are considered as game-changers in the field of computer vision, particularly after AlexNet in 2012. And the good news is CNNs are not restricted to images only. They are everywhere now, ranging from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes almost inevitable in all the fields of Data Science. Even most of the Recurrent Neural Networks rely on CNNs these days.