Coursera Python for Everybody Specialization from University of Michigan is for those who are the complete beginners to programming language and also for who have no prior programming experience. This online coursera python for everybody course helps you to learn the basics of programming using Python Programming Language. This specialization will cover the fundamental topics of how you construct a program from a simple instruction in Python. After a general introduction to programming, coursera python for everybody teaches you how to use python to extract data from the web and work with databases. It's a good demonstration of how Python can be useful for managing large datasets.

Coursera Machine Learning by Andrew Ng is an online non-credit course authorized by Stanford University, to deeply understand the inner algorithms in Machine Learning. Machine learning is a core sub-area of artificial intelligence, it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. The instructor of Coursera Machine Learning is Andrew Ng.

Researchers from The University of Western Australia and the University of California have developed a new machine-learning tool that will improve the management, restoration and irrigation of rangeland areas used for grazing. Associate professor Sally Thompson from the UWA School of Engineering and UWA Institute of Agriculture says the tool was suited to environments where the amount of rainfall exceeded the absorption capacity of the soil, resulting in the excess water flowing over the land. The new machine learning tool models surface water flows in dry environments with patchy vegetation cover. The findings have significant implications for agricultural and natural systems in Australia and worldwide. "The research could help environmental designers limit soil erosion in rangeland environments and agricultural systems, minimising degradation risks in drylands," says Sally. "It also has applications in urban settings, where waterproof surfaces, like pavement, generate runoff and flood risks."

Former Infosys CEO Vishal Sikka, who has announced a new AI startup with USD 50 million fund, believes India has the potential to become a world leader in artificial intelligence but the key to this is integrating AI into the country's education system in a massive way. India is at "an inflection point" when it comes to AI or artificial intelligence, Sikka said. Over the next 20-25 years, AI is going to be "a very, very big disruptor" for the Indian society because what one is seeing now in terms of automation and job losses because of automation is just the beginning, said Sikka, who announced his startup Vianai Systems last week. "But on the other hand, if we are able to bring AI education, the ability to build AI systems to India at a very large scale, and I'm talking about like billion plus people, then India can really leap frog and become the world's leader in artificial intelligence, in AI skill and AI talent," Sikka told PTI in an exclusive interview. Doing that requires working on multiple dimensions in parallel, he said.

What exactly is artificial intelligence (AI) and what business does it have in higher education? Simply put, AI is an attempt to emulate human knowledge by programming extensive rules into computers. Through machine learning and expert systems, machines can produce patterns within mass flows of data and pinpoint correlations that couldn't possibly be immediately intuitive to humans. The developmental capabilities and precision of AI ultimately depend on the gathering of data – Big Data. Where better to find a continuous stream of information than within the highly active and engaging community of students.

Artificial intelligence--the ability of a computer program to perform human tasks such as thinking and learning, sometimes referred to as machine learning--is changing classrooms in both K12 and higher ed. But robotics has some questioning whether AI is just a fad that will eventually fade into obscurity or alter teaching and learning processes as we know it. Experts discussed the topic at a recent conference for future K12 educators held by the Teachers College at Columbia University, "Where Does Artificial Intelligence Fit in the Classroom?" Borhene Chakroun, director of the division for policies and lifelong learning systems at UNESCO, kicked off the event extolling the future of AI technology and its potential to "profoundly alter every aspect of the teaching and learning process." He also acknowledged the implications of AI and how it is altering how machines and humans work together.

We are proud to share some words of the letter received from Borhene Chakroun, Director of Education Sector Division for policies and lifelong learning Systems (UNESCO). "In light of the very good results achieved by the above-mentioned Chair, confirmed by the...

This post originally published on Medium. It is republished here with permission. I've been around computing since my older brother got a Commodore 64 for Christmas in 1983. I took my first "business machines" class in high school in 1991, attended my first computer science class in 1994 (learning Pascal), and moved to Silicon Valley in 1997 after Cisco converted my internship into a permanent position. I worked in Cisco's IT department for several years before moving to their engineering group, where I designed networking protocols. I went to grad school at MIT in 2004, where I met the founders of several companies in Y Combinator's first couple of batches and worked on Hubspot before it was Hubspot. After writing several books for O'Reilly and attending the first O'Reilly Web 2.0 and MIT Sloan Sports Analytics conferences, I started a "Web 2.0 for Sports" company called StatSheet.com in 2007, which, in 2010, pivoted into the first Natural Language Generation (NLG) company called Automated Insights. I recently stepped back at Automated Insights to become a Ph.D. student at UNC studying artificial intelligence.

Recent advances in pre-training huge models on large amounts of text through self supervision have obtained state-of-the-art results in various natural language processing tasks. However, these huge and expensive models are difficult to use in practise for downstream tasks. Some recent efforts use knowledge distillation to compress these models. However, we see a gap between the performance of the smaller student models as compared to that of the large teacher. In this work, we leverage large amounts of in-domain unlabeled transfer data in addition to a limited amount of labeled training instances to bridge this gap. We show that simple RNN based student models even with hard distillation can perform at par with the huge teachers given the transfer set. The student performance can be further improved with soft distillation and leveraging teacher intermediate representations. We show that our student models can compress the huge teacher by up to 26x while still matching or even marginally exceeding the teacher performance in low-resource settings with small amount of labeled data.

This article is an overview of supervised machine learning problems for regression and classification. Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds. Implicit regularization in deep learning examples are presented, including data augmentation, adversarial training, and additive noise. These methods are re-framed as explicit gradient regularization.