Udacity, the Silicon Valley based lifelong learning platform, announced its newest initiative to expand students' artificial intelligence skills: the Intel Edge AI Scholarship Program. This new scholarship program, announced at the Intel AI Summit and the Future of Education and Workforce Summit in San Francisco, will empower professional developers interested in advanced learning, specifically deep learning and computer vision, to accelerate the development and deployment of high-performance computer vision and deep learning solutions. Computer vision and AI at the edge are becoming instrumental in powering everything from factory assembly lines and retail inventory management to hospital urgent care medical imaging equipment like X-ray and CAT scans. This program will teach fluency in some of the most cutting-edge technologies. Upon successful completion of the first phase of the program, students will also have the opportunity to earn their way to a full scholarship to the Intel Edge AI for IoT Developers Nanodegree program, a brand-new Udacity Nanodegree program built in partnership with Intel.
Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. The Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity for you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2019, then you have come to the right place.
Data Science Specialization is one of the best known sets of courses offered by Coursera in conjunction with Johns Hopkins University. This specialization covers the concepts and tools you'll need throughout the entire data science pipeline. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses. Coursera John Hopkins Data Science is a ten course program that covers the data science process from data collection to the production of data science products. It focuses on implementing the data science process in R. Coursera Johns Hopkins data science certification includes 9 courses and a capstone project.
Deep Learning Specialization provides introduction to DL methods for computer vision applications for practitioners who are familiar with the basics of DL. You will discover a breakdown and review of the convolutional neural networks course taught by Andrew Ng on deep learning specialization. It does not focus too much on math and does not include any code. After finishing the specialization you will know how to build models for photo classification, object detection, face recognition, and more. Instructors patiently explain the requisite math and programming concepts in a carefully planned order for learners who could be rusty in math/coding.
The rise of technology within the education sector over the last few decades has been astounding. This is certainly the case if we consider that teaching with technology has become pervasive in almost every classroom environment. Within today's classroom, for example, we find ourselves surrounded by devices such as smart boards, AV, computers, laptops, tablets and phones, to name but a few technologies which are now being integrated into teaching. We have also seen the rise of the virtual learning environment and blended learning, alongside a significant rise in online education. This has allowed distance learning to take new forms and shapes and to reach greater audiences around the world.
Learning new skills to enhance your abilities to do a task effectively can be a hectic schedule especially if you are an employee. It's hard to chase coaching or learning centers after spending 8-10 hours in the office per day. And when it comes to becoming technology-efficient specifically in the field of data science, you need to have the best qualification, handy experiences to get better job opportunities in this high in-demand profession. To ease out people's hectic schedules without compromising with the quality of the education, online platforms like Coursera, Udemy, eDX and many more have a collection of data science certification and courses. Adding a touch of extra bonanza, these courses are free of cost.
Good day, The biggest AI bootcamp in Nigeria is here! Will you be part of the best of the best who will make it to the all-expense paid residential Artificial Intelligence Bootcamp?... This was a mail I received on September 24 from Data Science Nigeria. And below is a snippet of what I got on Data Science Nigeria's website today. I started my programming journey back in September, 2018 with the most highly rated course on Udemy courtesy of my mentor, Fakorede Abiola.
The robots are coming – for jobs. This is the plain, cold, hard fact we now face as we head towards the third decade of the 21st Century. The technology-driven world in which we now live is one filled with promise – cars that drive themselves, algorithms that respond to customer service inquiries, automated business intelligence on tap. Yet, this brave new world is also filled with challenges. For even as AI and automation increase productivity and improve our lives, their widespread adoption means that many work activities humans currently perform will soon be displaced – if they haven't been already. What this doesn't mean, however, is that there will be a shortage of jobs in the future.
With each passing year, parents are getting more worried about how their children will fare once it's time to take that step from school to the workforce. They have good reason to fret. Some 17 million Americans under age 30--about one third of the under-30 population--are saddled with student debt. Many are worried about their career prospects despite having invested--heavily, in some cases--in education. The cost of college is being hotly debated.
About the book: A widely used text on reinforcement learning, which is one of the most active research areas in artificial intelligence, this book provides a clear and simple account of the field's key ideas and algorithms. With a focus on core online learning algorithms, including UCB, Expected Sarsa, and Double Learning, it then extends these ideas to function approximation covering topics on artificial neural networks and the Fourier basis. This second edition includes new chapters on reinforcement learning's relationships to psychology and neuroscience as well as updated case-studies on AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. About the authors: Richard S. Sutton is a distinguished research scientist at DeepMind in Edmonton and a professor in the Department of Computing Science at the University of Alberta. He previously worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts.