Once you enrolled into this course you'll get an introduction of AI and walkthroughs of concepts such as Machine learning, Robotics, Game Theory, Computer vision and Natural Language Processing. You'll also learn about Machine learning algorithms, Applications of AI in Natural Language Processing, Robotics, Computer vision etc, This course is developed by Meassachusetts Institute of Technology. This course introduces the basic knowledge representation, Problem solving and learning methods of Artificial Intelligence. At the same time you'll learn the AI concepts such as knowledge representation, computer vision, Natural language processing and machine learning.
Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.
Mercedes-Benz, whose engineers have been working on self-driving car technology, is eager to increase the size of its engineering team both in Silicon Valley and in Germany. SAN FRANCISCO - So you say you want join the automotive revolution? Over the past few years, only elite roboticists have been positioned to heed the self-driving car's call to action. Armed with degrees from places such as Carnegie Mellon University and experience at institutions such as NASA, these tech titans have been highly sought after by technology and automotive companies looking to build the future. But now massive open online course pioneer Udacity has a proposition: Give the Web-based education outfit 36 weeks and 2,400, and they'll turn graduates onto jobs at autonomous-car partner companies Mercedes-Benz, Didi Chuxing, Nvidia and Otto.
Sure, the autonomous era will wipe out a lot of jobs. Automakers, tech titans, and startups are racing to essentially put four million truckers, cabbies and other drivers out of work. But like all radical technological shifts, self-driving cars will provide opportunities, too--for those with the right skills. Working in the most compelling part of this field requires an understanding of deep learning, the branch of artificial intelligence that trains computers to do things like discern pedestrians from lamp posts. Universities can't crank out graduates fast enough.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.