An AI that can produce photorealistic videos of sign language interpreters from speech could improve accessibility by removing the need for humans. Ben Saunders at the University of Surrey, UK, and his colleagues used a neural network that converts spoken language into sign language. The system, called SignGAN, then maps these signs on to a 3D model of the human skeleton.

Education is the building block of society. In the early days, the education setup was about a tree, chalk, and slate with a change of time, restored as a luxury space that had everything to do with human effort in terms of learning. Today, most of all, the whole focus is shifting towards innovation, creativity, and technological advancement. Education is not just about restricting one's education, it is becoming a tool of innovation and official education policy is taking a new level. These innovations have brought many changes in the field of robotics.

This is a complete software engineering course with Python 3. In this course, you will learn programming from A-Z. You will learn all the concepts. We will build many real-world and useful applications in this course. You will learn object-oriented programming (OOP), data visualization, file handling, APIs, RESTful APIs, graphical user interfaces GUI, crud operation, software development, and much more.

After a lot of turmoil that the year 2020 brought in terms of business disruption, challenges to ensure business continuity, remote working becoming the new norm, among others, many companies and institutes introduced and developed free courses for the data science and AI enthusiasts to make the most of the lockdown. From various ivy league institutions to major organisations across the globe, several courses were made free this year. As the year 2020 comes to an end, we list a few such courses in AI and data science that were made free and are still available for tech enthusiasts to avail. The courses are listed in no particular order. In March this year, as lockdown ensued, to uplift the learners and help the community in the critical time, Coursera decided to launch new, free resources, as well as sharing impressive course collections, community discussions, and expert interviews.

Machine learning is an intimidating topic to tackle for the first time. The term encompasses so many fields, research topics and business use cases, that it can be difficult to even know where to start. To combat this, it's often a good idea to turn to textbooks that will introduce you to the basic principles of your new field of research. This holds true for AI and machine learning, especially if you have a background in statistics or programming. When used alongside more focused online articles like our introduction to training data, they can be an essential part of a powerful toolkit with which to learn and grow.

Machine Learning with Core ML 2 and Swift 5 Learn how to integrate machine learning into your apps. Hands-on Swift 5 coding using CoreML 2, Vision, NLP and CreateML What you'll learn Description ** A practical and concise Core ML 2 course you can complete in less than three hours ** Extra Bonus: Free e-book version included (sells for $28.80 on Amazon)! Wouldn't it be great to integrate features like synthetic vision, natural language processing, or sentiment analysis into your apps? In this course, I teach you how to unleash the power of machine learning using Apple Core ML 2. I'll show you how to train and deploy models for natural language and visual recognition using Create ML. I'm going to familiarize you with common machine learning tasks.

Things such as extended learning beyond classrooms, real-time assessment and feedback, automated creation of personalized learning plans, and supporting teachers on time consuming tasks, are now possible courtesy of AI-based systems and applications. AI technologies are being utilized more and more these days to analyze the growing number of data points for evaluating the performance and the changing needs of students. AI is playing an increasingly valuable role in accelerating effectiveness and efficiencies in many areas of the educational administrative processes. Individualized learning is priority one for educators. AI-based solutions for education are rapidly accelerating the individualized learning mission.

Researchers at UC Berkeley have developed an AI model that detects'silent speech.' The model is based on digital voicing to predict words and generate synthetic speech. Electromyography (EMG), with electrodes located at the face and throat, is used to detect the silent speech. Researchers assert that the model can enable many applications for people who cannot produce audible speech and assist speech detection for AI tools and additional devices that respond to voice commands. The team states that digitally voicing silent speech has broad applications.

We recommend these YouTube channels regardless of your machine learning experience, whether you have a computer science degree or just a passing interest in AI. You'll soon be on the way toward mastering the basics of AI, machine learning, and computer science in no time, through easy-to-follow demos and tutorial videos. The official Deep Learning AI YouTube channel has video tutorials from the deep learning specialization on Coursera. Artificial Intelligence -- All in One: This YouTube channel has tutorial videos related to science, technology, and artificial intelligence. Andrew Ng: Andrew Ng is a computer scientist and entrepreneur, co-founder of Google Brain, former VP & Chief Scientist at Baidu, adjunct professor at Stanford University.

Machine learning can be deployed virtually anywhere there's data. You've likely worked with tools like Google Analytics, Typeform, Hubspot, and others. In fact, machine learning can be used to make all of these tools more powerful, whether it's predicting traffic and conversions, predicting any data in a Typeform, or even driving growth hacking. Nowadays, AutoML tools seem to be taking over the world. If you Google "AI without code," you'll find tools like RunwayML for creative applications and Obviously.AI for tabular data, besides the more well-known Google AutoML tools (though, to be fair, Google's version is fairly hard to use).