As a data scientist, you'll have to extract useful information from extremely imperfect data. You can't completely eliminate uncertainty but you can reduce it with a strong grasp of statistics and probability fundamentals.

Way2AI is a group of enthusiasts and specialists in AI & Machine Learning, created by Long Nguyen, PhD in AI (France), aiming at teaching people learning about this emerging technology. AI is really changing the world! Almost every domain can benefit from the power of AI, from business, healthcare, to transport, entertainment, and military etc. There are more and more investments in AI but the domain still lacks of qualified employees. Therefore we really hope that our contribution can help many people find a fast and easy way in approaching AI.

How one gets educated for AI continues to be an area worth exploring with many options available. Charting one's career as a member of a newly-formed team working to leverage AI to help the business is best met with creativity and patience. It's as much a mission to find out how organizations are setting up for AI development as it is about finding out what you really want to do. The experience of one now-veteran machine modeler could be timely guidance for many in this context. Daniel Bourke is an entrepreneur running a YouTube site and writing about technology.

As per the Data Science Skills Study 2020, more than 10% of the machine learning and data science practitioners learn from various online sources, such as massive online open courses or MOOCs, online certifications and courses, online videos hosted on such platforms as well as LinkedIn and YouTube, among others. On the other hand, traditional formats like university certifications and courses are at the lower end of the spectrum of preference, which is 5.7%. The one main reason behind this is the dearth of AI teachers among institutions and academia. To get an industry perspective on this, Analytics India Magazine caught up with a few experts in this field who explained the reasons behind the void and helped in understanding how these issues can be addressed. The use of artificial intelligence-based solutions has been proliferating in everyday life, starting from the shopping experience to financial transactions.

Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oftentimes, this decentralized graph support changes with time due to link failures or topology variations. These changes create a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be used to retrain GNNs at testing time, overcoming this issue. However, most online algorithms are centralized and work on convex problems (which GNNs rarely lead to). This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms. The WD-GNN comprises two components: the wide part is a bank of linear graph filters and the deep part is a GNN. At training time, the joint architecture learns a nonlinear representation from data. At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem. We derive convergence guarantees for this online retraining procedure and further propose a decentralized alternative. Experiments on the robot swarm control for flocking corroborate theory and show potential of the proposed architecture for distributed online learning.

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Facebook says that it will expand an online course in deep learning to more students to help improve the diversity of its AI division. After a successful pilot program at Georgia Tech, the company will roll out this graduate-level course in deep learning to more colleges across 2021. The focus will be on offering the system to universities that serve large numbers of Black and Latinx students. It's hoped that, by improving the diversity of the people building these systems, some of the more odious biases will be weeded out. This is part of a broader program to encourage people to enter the computer science field even if their undergraduate training is in another area.

Nearly a decade ago, back in 2011, when I had just completed the 4th semester of my Computer Science & Engineering Degree course, I found that even though I had fared well in all my exams, my practical knowledge in this field was (in the words of Lord Kelvin -- the famous mathematical physicist)"of a meager and unsatisfactory kind". This was due to the fact that, aside from a handful of really great courses, the majority of my coursework relied on rote-learning and the competitive pursuit of grades instead of practical knowledge. I had joined the field of Computer Science to satisfy my childhood dream of working with computers, but I found I was still far from my dream of understanding and creating software with my computer. In this dismal state, I spent the beginning of my semester-break searching for a motivation in the online universe. After Googling for a short while, I stumbled upon Mehran Sahami's CS106A video lectures on Stanford's Youtube channel, and thus began my tryst with online education.

Covid-19 has severely impacted the whole world socially, economically and health-wise. And as uncertainty grips the post coronavirus phase and the "new normal" phrase lingers on, one thing is for sure from many debates and discussions--neither the business models nor workplace dynamics will remain the same as before. Already, innovative tech giant companies like Google have developed interactive maps to help track both local and global cases of the novel coronavirus. These maps feature statistics of verified cases, recoveries, deaths as well as predictions of how the crisis is likely to unfold in the near future. Chances are that you're relying on one of these kinds of maps to stay updated now and then with the latest on the pandemic.

Teaching yourself Python machine learning can be a daunting task if you don't know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics. It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters. A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category. While the title sounds like another introductory book on machine learning algorithms, the content is anything but.