Experts concur that new technologies such as Data Science, Cybersecurity, AI & ML, as well as relevant skill-based tools are the future of building a strong technology infrastructure to entirely transform the higher education sector by providing skill-based education and learning. Ed-tech players provide a perfect platform to upskill India's graduates and working professionals, promoting a'skills-based' approach to higher education in the face of new industry-specific skill demands and technological advancements. With industry requirements changing in tandem with economic and social progress, as well as the impact of rapidly progressing technology, a vast number of funds, and other macroeconomic factors have set the stage for India's next chapter of growth. Despite the fact that economic circumstances are changing with continuous new technology, there is a need for graduates and working professionals to have access to comprehensive upskilling programs and tools. It's critical for India's flourishing workforce to acquire new skills to keep up with these tectonic shifts – the Indian economy's engine is its employable workforce.

Begin your professional career by learning data science skills with Data science Dojo, a globally recognized e-learning platform where we teach students how to learn data science, data analytics, machine learning and more. We offer programs in most-demanded formats: virtual instructor-led and self-paced training.

Welcome to the World's best specialized SEO course ever. This is the only course in the world where you woll also learn about the technicalities of SEO and how to handle them. The content of this course is based on real world practices and checklists used by professionals in the SEO world. The content of the course focuses on giving the idea of how any SEO agency or freelancer approaches to any website and start the SEO to rank any particular keyword. You will understand how the SEO activities affect the website in terms of visibility by Search Engine.

Although most cultures, particularly in the west, provide a great many commemorations of distant ancestors – statues, portraits, buildings – we are much less willing to consider our far-off descendants. We might invoke grandchildren, at a push great-grandchildren, but after that, it all becomes a bit vague and, well, unimaginable. And while we look with awe and fascination at the Egyptian pyramids, built 5,000 years ago, we seem incapable of thinking, or even contemplating, 5,000 years in the future. That lies in the realm of science fiction, which is tantamount to fantasy. But the chances are, barring a global catastrophe, humanity will still be very much around in 5,000 years, and going by the average existence of mammal species, should still be thriving in 500,000 years. If we play our cards right, we could even be here in 5m or 500m years, which means that there may be thousands or even millions times more human beings to come than have already existed.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Although artificial intelligence (AI) has immense potential to shape the future of medicine, its place in undergraduate medical education currently is unclear. Numerous arguments exist both for and against including AI in the medical school curriculum.

Welcome to this project on predict whether a customer is eligible for Home loan or not in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing. That's why I haven't included any purely theoretical lectures in this tutorial: you will learn everything on the way and be able to put it into practice straight away. Seeing the way each feature works will help you learn Apache Spark machine learning thoroughly by heart.

The problem of online learning with graph feedback has been extensively studied in the literature due to its generality and potential to model various learning tasks. Existing works mainly study the adversarial and stochastic feedback separately. If the prior knowledge of the feedback mechanism is unavailable or wrong, such specially designed algorithms could suffer great loss. To avoid this problem, \citet{erez2021towards} try to optimize for both environments. However, they assume the feedback graphs are undirected and each vertex has a self-loop, which compromises the generality of the framework and may not be satisfied in applications. With a general feedback graph, the observation of an arm may not be available when this arm is pulled, which makes the exploration more expensive and the algorithms more challenging to perform optimally in both environments. In this work, we overcome this difficulty by a new trade-off mechanism with a carefully-designed proportion for exploration and exploitation. We prove the proposed algorithm simultaneously achieves $\mathrm{poly} \log T$ regret in the stochastic setting and minimax-optimal regret of $\tilde{O}(T^{2/3})$ in the adversarial setting where $T$ is the horizon and $\tilde{O}$ hides parameters independent of $T$ as well as logarithmic terms. To our knowledge, this is the first best-of-both-worlds result for general feedback graphs.

Automatic tagging of knowledge points for practice problems is the basis for managing question bases and improving the automation and intelligence of education. Therefore, it is of great practical significance to study the automatic tagging technology for practice problems. However, there are few studies on the automatic tagging of knowledge points for math problems. Math texts have more complex structures and semantics compared with general texts because they contain unique elements such as symbols and formulas. Therefore, it is difficult to meet the accuracy requirement of knowledge point prediction by directly applying the text classification techniques in general domains. In this paper, K12 math problems taken as the research object, the LABS model based on label-semantic attention and multi-label smoothing combining textual features is proposed to improve the automatic tagging of knowledge points for math problems. The model combines the text classification techniques in general domains and the unique features of math texts. The results show that the models using label-semantic attention or multi-label smoothing perform better on precision, recall, and F1-score metrics than the traditional BiLSTM model, while the LABS model using both performs best. It can be seen that label information can guide the neural networks to extract meaningful information from the problem text, which improves the text classification performance of the model. Moreover, multi-label smoothing combining textual features can fully explore the relationship between text and labels, improve the model's prediction ability for new data and improve the model's classification accuracy.

A well formed query is defined as a query which is formulated in the manner of an inquiry, and with correct interrogatives, spelling and grammar. While identifying well formed queries is an important task, few works have attempted to address it. In this paper we propose transformer based language model - Bidirectional Encoder Representations from Transformers (BERT) to this task. We further imbibe BERT with parts-of-speech information inspired from earlier works. Furthermore, we also train the model in multiple curriculum settings for improvement in performance. Curriculum Learning over the task is experimented with Baby Steps and One Pass techniques. Proposed architecture performs exceedingly well on the task. The best approach achieves accuracy of 83.93%, outperforming previous state-of-the-art at 75.0% and reaching close to the approximate human upper bound of 88.4%.

Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e.g., easy to hard) sequence. Previous work takes incorrect samples as generic hard ones without discriminating between hard samples (i.e., hard samples in correct data) and incorrect samples. Indeed, a model should learn from hard samples to promote generalization rather than overfit to incorrect ones. In this paper, we address this problem by appending a novel loss function DiscrimLoss, on top of the existing task loss. Its main effect is to automatically and stably estimate the importance of easy samples and difficult samples (including hard and incorrect samples) at the early stages of training to improve the model performance. Then, during the following stages, DiscrimLoss is dedicated to discriminating between hard and incorrect samples to improve the model generalization. Such a training strategy can be formulated dynamically in a self-supervised manner, effectively mimicking the main principle of curriculum learning. Experiments on image classification, image regression, text sequence regression, and event relation reasoning demonstrate the versatility and effectiveness of our method, particularly in the presence of diversified noise levels.