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Python Regression Analysis: Statistics & Machine Learning

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This hands-on, regression-analysis bootcamp will help you master practical statistical modeling and machine learning in Python. Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in Python in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions...All of this while exploring the wisdom of an Oxford and Cambridge educated researcher. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis.


AI, Automation Predictions for 2022: More Big Changes Ahead

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Just when you thought it was safe to go back to normal -- are you ready for round two? "There are big changes ahead," says Forrester VP Brandon Purcell. "There are a lot of changes that have been brought about by what happened over the last 2 years. The pace of change is very rapid. There are pretty big things happening." Purcell spoke with InformationWeek about the predictions for AI in 2022 and beyond.


AI Can Visualize Thoughts

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What if your thoughts can be visualized in the screen front of you? The answer is yes.., Tomoyasu et al managed to persuade the global expectations . The evolution in the field of Artificial Intelligence never fails to amaze us, One day the technology becomes so advanced that it connects our brains with projectors and enables us to share real-time thoughts. The possibilities with AI have no boundaries, and more advancement in artificial intelligence opens new communication doors. Tomoyasu Horikawa et al., researched Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) which paves the visualization of perceptual contents.


10 Popular Machine Learning Algorithms In A Nutshell

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Hierarchical clustering means creating a tree of clusters by iteratively grouping or separating data points. There are two types of hierarchical clustering named Agglomerative clustering and Divisive clustering. Agglomerative clustering is the bottom-up approach. It merges the two points that are the most similar until all points have been merged into a single cluster. Divisive clustering is the top-down approach.


Nota raises $14.7M to adapt biometrics, AI models for edge applications

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Nota, which provides technology to optimize AI models, announced that it has closed a $14.7 million Series B funding round. The company's technology is another important piece of the puzzle when it comes to helping resource-constrained edge devices run applications such as biometric identification. Participants in the funding round included Stonebridge Ventures, LB Investment, DS Asset, Intervest, and Company K Partners. The fresh funding comes roughly a year after Nota closed its Series A round with $6.7 million. Nota has raised a total of $23 million to date.


When Amazon came to town: Swindon feels strain as new depot sucks up jobs

The Guardian

Black boxes rattle along miles of conveyor belt, carrying everything from toys to painkillers amid a cacophony of alarms and the faint hum of Christmas songs. "I'm looking around here at anything that might not be right, but it's actually running very smoothly," says David Tindal, the general manager of the Swindon fulfilment centre. "The team has been fantastic. We spend the whole year preparing for this peak time, like a good football club preparing for the cup final." Known internally as BRS2 – using a naming system based on the nearest big airport (in this case, Bristol) – the warehouse is a vision in gleaming concrete, steel and glass landed on the Wiltshire countryside.


Paper explained: DINO -- Emerging Properties in Self-Supervised Vision Transformers

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In this story, I would love to give you a a good idea of how the DINO paper works and what makes it great. I've tried to keep the article simple so that even readers with little prior knowledge can follow along. Traditionally, Vision Transformers (ViT) have not been as attractive as some would expect: They have high computational demands, need more training data, and their features do not exhibit unique properties. With their 2020 paper, "Emerging Properties in Self-Supervised Vision Transformers", Caron et al. aimed to examine why supervised ViT have not yet taken off and if that could be changed by applying self-supervised learning methods to them. This meant that a human would have to create labels for the training data like telling the model that there is a dog in the image.


NEW AUTONOMOUS SHUTTLE BUS COLLABORATION BETWEEN SENSIBLE 4 OY AND MOOVE GMBH - Sensible 4

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MOOVE GmbH was founded in 2018 on the RWTH Aachen Campus. MOOVE develops sustainable, electric and ultimately driverless commercial vehicles. With the industrialized, purpose designed light commercial vehicle platform, MOOVE strives for an emission-free delivery and passenger service ecosystem by reducing emissions and noise in cities. The company's first vehicles, PeopleMover and CargoMover, will be built in disruptive micro factories, which will focus on sustainability, energy efficiency and minimizing environmental impact by eliminating traditional manufacturing processes, such as pressing and painting.


AWS unveils latest innovations in cloud, machine learning, 5G, and IoT

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Amazon Web Services has this week detailed the latest innovation in cloud, machine learning, data analytics, 5G, and Internet of Things technology at its 10th annual re:Invent conference. In his first keynote as the company's new chief executive, Adam Selipksy reinforced how AWS "continues to be the most comprehensive and broadly adopted cloud offering in the world by further democratising the use of cloud technology". Selipsky also revealed six major service announcements to help businesses, and government organisations reduce their IT spend, improve their customer experience, and better harness the power of data to make better decisions. Phil Davis, regional managing director for AWS in Asia Pacific and Japan, says the launches are the next iteration of its continued innovation to help solve problems on behalf of its customers. "We are focused on helping customers power their digital transformation across Asia Pacific and Japan so that they can build innovative solutions that improve lives and protect our planet," he says.


Grading AI: The Hits and Misses

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AZEEM AZHAR: Welcome to The Exponential View podcast where multidisciplinary conversations about the near future happen every week. Now, as an entrepreneur, investor, and analyst I've been inside the technology industry for over 20 years. During that time, I've observed that exponentially developing technologies are changing the face of our economies, business models, and culture in unexpected ways. Now, I return to this question every week in my newsletter Exponential View, in this podcast, as well as in my recent book The Exponential Age. So, in today's edition I wanted to look back and forward on one of the key technologies of the exponential age, artificial intelligence. We're about a decade into the current industrial boom in AI and I thought it was time to take a scorecard, look at what we've achieved, and how and perhaps what we didn't on which milestones have surprised us. To help me I called on a great experts Murray Shanahan, a senior research scientist at London's DeepMind, as well as a professor of cognitive robotics at Imperial College in London. Murray works on machine learning, consciousness, the impacts of artificial intelligence. He and I have known each other for a few years and have indeed done a podcast together previously. We appeared as guests on a show hosted by a technology investor. So, my challenge to Murray today was not simply to access the last 10 years of development, but to look forward to the next 10. It's a bold challenge and we did our best to look forward as well as back. MURRAY SHANAHAN: It's very nice to be here.