R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also started developing packages on R and offering enterprise version of R. What is R? R is a free language and environment for statistical computing and graphics. You can perform a variety of tasks using R language.

Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content," Zuri Kemp, Programme Manager for Google's machine learning education, said in a statement. Google on Thursday introduced "Learn with Google AI" -- a set of educational resources developed by Machine Learning (ML) experts at the company, for people to learn about concepts, develop skills and apply Artificial Intelligence (AI) to real-world problems. "Learn with Google AI" comes with existing content as well as the new Machine Learning Crash Course (MLCC). "We believe it's important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content," Zuri Kemp, Programme Manager for Google's machine learning education, said in a statement.

What is machine learning / ai? How to lean machine learning in practice? There are a lot of interested people out there but many do not know where to start. The difficult question basically is how to start actually learning it? Especially beginners might get discouraged because of statistics and math which is an integral part of machine learning.

All countries will feel the impact of automation, but at different speeds and in different ways. In this podcast, McKinsey Global Institute looks at its likely impact in China, Europe, and India. New technologies such as artificial intelligence and automation are reshaping the workplace globally. All countries will feel the impact in some way, shape, or form. In this episode for the McKinsey Global Institute's New World of Work podcast, MGI directors Jonathan Woetzel and Jacques Bughin and MGI partner Anu Madgavkar examine automation's likely impact in China, Europe, and India. I'm Peter Gumbel from the McKinsey Global Institute, and today we'll be taking a look at the quite different ways that new technologies like automation and artificial intelligence will affect work in different parts of the world. Specifically, we'll be looking at China, Europe, and India. These differences come about for a number of reasons that we explain in our new MGI report on the future of work, which is called Jobs lost, jobs gained: Workforce transitions in a time of automation. Among the reasons for these differences are different levels of economic development, different wage rates, and different potential for automation adoption in different economies. First, let's talk about China. Here to do so is Jonathan Woetzel, director of the McKinsey Global Institute, based in Shanghai. Jonathan, perhaps you can start by telling us where the Chinese workforce is at the moment.

Robot Is The Boss book is not about how Artificial Intelligence (AI) will destroy humanity or how machines will rebel against us. Instead, it explains the best way to get benefits from using machine learning in your business today. This book will help you understand the basic terminology behind the buzzwords and will provide a framework for how you can use machine intelligence in your business today.

In what way will AI be incorporated into e-learning in the near future? E-learning has the potential to revolutionize education. For one thing, the internet and burgeoning AI technology have made e-learning more accessible than ever before. But e-learning also offers solutions to some of education's most pressing challenges, and in the future, it could serve to more adequately provide all students access to quality teaching. Everyone processes content in different ways and at different speeds.

January 25 & 26 in San Francisco will see the sixteenth global Deep Learning Summit and the fifth global AI Assistant Summit joined by the first ever Deep Learning for Enterprise Summit. Use code KDNUGGETS to save 20% on Early Bird passes!

NVIDIA HAS been teaching MPs how to create neural networks as part of an AI Workshop organised in conjunction with the Big Innovation Centre. A cross-party group including Stephen Metcalf MP and Lord Clement Jones CBE, co-chairs of the All Party Parliamentary Group on Artificial Intelligence (APPG AI), was tasked with learning about AI, what it really means, and then taking the information back to inform the discussion with constituents. Of course, that doesn't just mean being able to respond if an MP's surgery bears the question "So, do I need to live in fear of Skynet?" but also more practical pursuits like a local business looking at bringing AI to their business. Stuart Wilson, AI and Supercomputing director at Nvidia, said: "Artificial intelligence represents the biggest technological and economic shift in our lifetime. It is of national importance that policymakers understand the core components, capabilities and limitations surrounding the modern AI boom."

An important machine learning method for dimensionality reduction is called Principal Component Analysis. It is a method that uses simple matrix operations from linear algebra and statistics to calculate a projection of the original data into the same number or fewer dimensions. In this tutorial, you will discover the Principal Component Analysis machine learning method for dimensionality reduction and how to implement it from scratch in Python. How to Calculate the Principal Component Analysis from Scratch in Python Photo by mickey, some rights reserved. Take my free 7-day email crash course now (with sample code).

Nonnegative matrix factorization (NMF) has become a workhorse for signal and data analytics, triggered by its model parsimony and interpretability. Perhaps a bit surprisingly, the understanding to its model identifiability---the major reason behind the interpretability in many applications such as topic mining and hyperspectral imaging---had been rather limited until recent years. Beginning from the 2010s, the identifiability research of NMF has progressed considerably: Many interesting and important results have been discovered by the signal processing (SP) and machine learning (ML) communities. NMF identifiability has a great impact on many aspects in practice, such as ill-posed formulation avoidance and performance-guaranteed algorithm design. On the other hand, there is no tutorial paper that introduces NMF from an identifiability viewpoint. In this paper, we aim at filling this gap by offering a comprehensive and deep tutorial on model identifiability of NMF as well as the connections to algorithms and applications. This tutorial will help researchers and graduate students grasp the essence and insights of NMF, thereby avoiding typical `pitfalls' that are often times due to unidentifiable NMF formulations. This paper will also help practitioners pick/design suitable factorization tools for their own problems.