For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning. There seems, however, to be an increasing trend of getting on to the practical from the start, and mixing practice and theory along the way as resources progress. This post presents 5 such resources. Covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks, these resources cover quite a bit of ground. They are also all free, so get reading, get watching, and get coding.

A 2017 Tractica report on the natural language processing (NLP) market estimates the total NLP software, hardware, and services market opportunity to be around $22.3 billion by 2025. The report also forecasts that NLP software solutions leveraging AI will see a market growth from $136 million in 2016 to $5.4 billion by 2025. In order to shed more light on the growing applications of NLP solutions, Dan Faggella, the CEO of TechEmergence, converses with Vlad Sejnoha, the CTO of Nuance Communications, an organization offering AI and NLP solutions in voice, natural language understanding, reasoning and systems integration. Vlad Sejnoha has been the Senior Vice President and CTO at Nuance since 2001. He holds a Masters degree in Electrical Engineering from McGill University. Vlad has been working in the field of NLP and speech recognition for over 30 years and holds 22 patents to date.

We are living in interesting times, where digital assistants schedule meetings, chatbots work alongside humans as teaching assistants, and your suitcase can now become self driving luggage as showcased at CES, 2018. The implications are just starting to be felt in the workplace. In 2017, I wrote about how The Employee Experience is the Future of Work. Now, as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience. As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well. Forward looking HR leaders are piloting chatbots now to transform HR, and, in the process, re-imagine, re-invent, and re-tool the employee experience.

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Is it the German operators that created the AI in 2016, or is it the deep learning machine itself? That was one of the questions a panel of intellectual property experts grappled with at an AI conference in Toronto hosted by Osgoode Hall Law School last week. As businesses struggle to keep step with the rapid advancement in AI, policies and laws are also being stretched, said lawyer Carole Piovesan of McCarthy Tetrault LLP. "Canada is really at the precipice, as is much of the world, of trying to define what its legal framework is going to look like in the face of AI," she said. "But with the current pace of AI innovation -- it's happening so quickly and it's of such a transformative nature -- that policy-makers are being forced to anticipate issues that don't necessarily exist here and now."

BISHKEK – I was recently offered the presidency of a university in Kazakhstan that focuses primarily on business, economics, and law, and that teaches these subjects in a narrow, albeit intellectually rigorous, way. I am considering the job, but I have a few conditions. What I have proposed is to transform the university into an institution where students continue to concentrate in these three disciplines, but must also complete a rigorous "core curriculum" in the humanities, social sciences, and natural sciences – including computer science and statistics. Students would also need to choose a minor in one of the humanities or social sciences. There are many reasons for insisting on this transformation, but the most compelling one, from my perspective, is the need to prepare future graduates for a world in which artificial intelligence and AI-assisted technology plays an increasingly dominant role.

Dr. Das further explained how with the help of complex virtual learning techniques, a wide range of physical and cognitive tasks are being managed today with a high level of efficiency and accuracy. And as artificial intelligence or AI systems advance through machine learning these will continue to impact not just business but our lives as well. But, if indeed machines continue to improve their performance beyond human levels, a natural question to ask is whether machines will put humans' jobs at risk and reduce employment. According to Mr. Vijay Sethi, CIO and Head CSR at Hero MotoCorp Ltd., "Such a concern is not new and in fact dates back to the 1940s when AI and automation started developing." Time and again these concerns have been raised by the citizens of the world.

The e-learning or digital content ecosystem is quickly evolving, and mobile learning, multi-device content, mobile responsive content (call it what you will) is here to stay. For me, Microsoft PowerPoint works the best. Example #5: Integrate Scrolling into the Design Process If you are not careful, your instructional designers will simply transfer their fixed screen design ideas into long scrolling pages.

This article covers far more than the title suggests. It is written in simple English and accessible to quantitative professionals from a variety of backgrounds. Deep mathematical and data science research (including a result about the randomness of Pi, which is just a particular case) are presented here, without using arcane terminology or complicated equations. The topic discussed here, under a unified framework, is at the intersection of mathematics, probability theory, chaotic systems, stochastic processes, data and computer science. Many exotic objects are investigated, such as an unusual version of the logistic map, nested square roots, and representation of a number in a fractional or irrational base system. The article is also useful to anyone interested in learning these topics, whether they have any interest in the randomness or Pi or not, because of the numerous potential applications. I hope the style is refreshing, and I believe that you will find plenty of material rarely if ever discussed in textbooks or in the classroom.

Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. Pick up a machine learning paper or the documentation of a library such as PyTorch and calculus comes screeching back into your life like distant relatives around the holidays. And it's not just any old scalar calculus that pops up--you need differential matrix calculus, the shotgun wedding of linear algebra and multivariate calculus. Well... maybe need isn't the right word; Jeremy's courses show how to become a world-class deep learning practitioner with only a minimal level of scalar calculus, thanks to leveraging the automatic differentiation built in to modern deep learning libraries. But if you really want to really understand what's going on under the hood of these libraries, and grok academic papers discussing the latest advances in model training techniques, you'll need to understand certain bits of the field of matrix calculus.