A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures.

We have put together a list of highly rated and most enrolled online courses on Artificial intelligence, machine learning, deep learning. The list will keep on increasing as and when we find more resources. Consider bookmarking this page and come back often to see newly added courses. The course is created by Lazy Programmer Inc. and has currently 2513 students enrolled with a feedback score of 4.6 out of 5. It is listed as the best selling Udemy course on Artificial Intelligence.

In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular). Based on the Stanford Computer Science course CS246 and CS35A, this book is aimed for Computer Science undergraduates, demanding no pre-requisites. This book has been published by Cambridge University Press. This book holds the prologue to statistical learning methods along with a number of R labs included. This Deep Learning textbook is designed for those in the early stages of Machine Learning and Deep learning in particular.

When an Icelander arrives at an office building and sees "Solarfri" posted, they need no further explanation for the empty premises: The word means "when staff get an unexpected afternoon off to enjoy good weather." The people of this rugged North Atlantic island settled by Norsemen some 1,100 years ago have a unique dialect of Old Norse that has adapted to life at the edge of the Artic. Hundslappadrifa, for example, means "heavy snowfall with large flakes occurring in calm wind." But the revered Icelandic language, seen by many as a source of identity and pride, is being undermined by the widespread use of English, both for mass tourism and in the voice-controlled artificial intelligence devices coming into vogue. Linguistics experts, studying the future of a language spoken by fewer than 400,000 people in an increasingly globalized world, wonder if this is the beginning of the end for the Icelandic tongue.

A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this!

Few days back I went to a bar with couple of friends and found that one of my friends is working with the bartender to create a perfect cocktail. The scene was such that it got me thinking. The bartender's action could very well be used to explain how an analytics lead could get his machine learning deployed and what best practices are needed. A good bartender keeps his vocabulary updated with what all liquor and additives at his disposal, so that he could create a variety that specifically targets your experience. Similarly, having an open mindset will help in picking the tool that could best serve the problem and not the bias?

The pressure was on for Abdul Gouda and his classmates at George Mason University: Not only did their graduation depend on the success of their project, but so did the hopes of an impossibly cute 10-year-old girl. Fifth-grader Isabella Nicola wanted to play the violin, but she was born with no left hand and a severely abbreviated forearm. Her music teacher at Island Creek Elementary in Fairfax County had built her a prosthetic allowing her to move the bow with her left arm and finger the strings with her right -- the opposite of how violin is usually taught. But the prosthetic was heavy and he thought there might be a better option. He reached out to Mason, his alma mater.

From the printing press to the digital age, new technology has long had a tendency to be viewed as disruptive, and is often met with resistance in the workplace. This is also the case when new advancements shift or evolve how we work. Artificial Intelligence (AI), what many have deemed the cause of the "fourth industrial revolution," is no different. While humans aren't always inventing new things, one thing we're really good at is figuring out how to do things better, faster, and more efficiently. Thirty years ago, robots might have seemed limited to science fiction novels, but even today there are many industries that have seen the shift towards automation take hold.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. After seeing this video, I'm convinced that RoboCup 2017 in Nagoya will be the best robot competition in the history of the universe. NASA's humanoid robots are too big and expensive to spend their time doing human-robot interaction studies, so students at Rice University built NotBot to take their place: It's not at all surprising that a human in a robot suit is much more capable than an actual robot for many (if not most) applications.

Deep learning, a subfield of machine learning, has become one of the most known areas in the ongoing AI hype. Having led to many important publications and impressive results, it is applied to dozens of different scenarios and has already yielded interesting results like human-like speech generation, high accuracy object detection, advanced machine translation, super resolution and many more. There is a steady flow of papers and publications that describe the latest advances in network design, compare existing architectures or describe unseen approaches leading to even better results than the current state-of-the-art. At the same time more and more companies and developers jump on the deep learning bandwagon and deploy the ideas and architectures to real world production systems. This article describes our approach to applying deep learning to our image editing product, the struggle we had with finding the right architecture and the experience we made while developing a system that can be deployed to mobile devices.