From the largest and most successful tech corporations to the smallest start-ups just finding their footing, most will agree that increasing diversity is in the best interests of customers, employees, and the general public. However, we in the tech world often fail to recognize the impact of our own biases. We sometimes think that, because our products and services are based on 0's and 1's, everything we put out into the world is fair and logical. This International Women's Day (Thursday), let's take a closer look at the biases that inhabit so much of our work, as well as some of the ways we can work toward a culture of inclusive AI. The theme of this year's International Women's Day is #PressforProgress, a call for gender parity across countries, industries, and all kinds of organizations. As we celebrate the achievements of women and push for equality, we should recognize that harmful biases affect a range of communities.

The site provides ways to learn core Machine Learning (ML) concepts, develop and hone one's ML skills, and apply ML to real-world problems. Technology is shaping the way we live and function. Pacemakers in the heart can fix your heart rate to normal, while cars can throw in recommendations and use computer vision to detect traffic patterns and predict accidents. Artificial intelligence (AI) is the future of computer science, thanks to digital data pattern recognition, which is enabling software to tell humans possible outcomes and scenarios. Taking its increased usage into concern, Google has announced the launch of an AI learning platform, which will be available to anyone willing to learn about the world of software and data.

Technology and search engine giant Google has now introduced an easy-to-learn platform called'Learn with Google AI'. This is available at At google.ai for free.This comprises a set of educational resources developed by Machine Learning experts at the company to help people learn about concepts, develop skills and apply artificial intelligence to problems in real life. When you go to the page on google.ai, it indicates the programme's utility by saying, "Whether you're just learning to code or you're a seasoned machine learning practitioner, you'll find information and exercises in this resource center to help you develop your skills and advance your projects." Google in its blog said, "To help everyone understand how AI can solve challenging problems, we've created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to "curious cats" who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here."

THE training and development of your workforce is vital to the achievement of digital transformation success for businesses. And today, more and more businesses are leveraging e-learning to educate their employees. The advantages for businesses using online learning platforms as opposed to traditional training methods are bountiful. First, it lowers business costs since one training session can be delivered to multiple people. Second, topics can be broken down into bite-sized chunks, meaning that employees do not need to spend lengthy periods of time away from their desks.

For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students' homework, and provide feedback to the students (and the students' parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a "one-size-fits-all" gauntlet of learning, not personalized to their abilities, needs, or learning context. I'm always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson: "Why is there this assumption that we should educate children simply according to how old they are? It's almost as if the most important thing that children have in common is their date of manufacture."

A new study by Paul Rad, assistant director of the UTSA Open Cloud Institute, and Nicole Beebe, Melvin Lachman Distinguished Professor in Entrepreneurship and director of the UTSA Cyber Center for Security and Analytics, describes a new cloud-based learning platform for artificial intelligence (A.I.) that teaches machines to learn like humans.

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in maths - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science. This specialisation aims to bridge that gap, getting you up to speed in the underlying maths, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimise fitting functions to get good fits to data.

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.

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify existing kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency and spectral filtering properties. Our theoretical results provide valuable insights in assessing the advantages and limitations of existing pairwise learning methods.