With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at https://github.com/sahanbull/context-agnostic-engagement

Advances in AI, making use of machine learning (ML) and natural language processing (NLP) have proven adept for voice recognition and identifying …

BagyaTech is not just another online training institute that offers courses on Testing Tools and other software technologies. We are transforming and redefining the online education. We are making learning a fun and interactive activity through which learners gain maximum and succeed in their careers.

Distance learning and remote teaching have increased reliance on tech making it a reality, and able to traverse borders with less regard for physical geo-locations. There are numerous restrictions that prevent online learning from being ubiquitous such as internet accessibility, access to learning platforms, adequate attention for learners individually, and language barriers. Video-based learning could be enough for urban pupils, but for rural areas, connectivity becomes low, less reliable, and interrupted lessons. For international students, pursuing higher education or probably taking vocational courses, a lack in fluency in English or any other intermediary languages can play a significant role in limiting proper online learning. Learning a new language is the objective for work or to further studies, but the bigger question is how technology can bridge the language learning divide.

Online Courses Udemy | Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing BESTSELLER 4.5 (2,250 ratings) Created by Jose Portilla  English [Auto-generated], Italian [Auto-generated] Preview this course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Udemy Online Courses - Create a SHMUP with Unity 3D, Build a Shoot em up game for mobile with C# and Unity 3D 4.7 (50 ratings), Created by Romi Fauzi, English [Auto-generated] Create a complete SHMUP game like Skyforce Upgrade (include timed upgrades) and Save features. Upgrade (include timed upgrades) and Save features. In this course we will create a full Shoot Em Up game (Skyforce, Raiden) from scratch in Unity. You will learn about object oriented programming and have an overall better understanding of C#. We will provide you with all the assets needed to create the game (including 3d models, audio), feel free to use these assets in your own games.

Online Courses Udemy - Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn Created by Lazy Programmer Inc English [Auto-generated], Spanish [Auto-generated] Students also bought Bayesian Machine Learning in Python: A/B Testing The Complete Python Course Learn Python by Doing Complete Python Developer in 2020: Zero to Mastery Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning in Python Preview this course GET COUPON CODE Description In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

MOOCs have been around since 2008, when 25 students attended a course on connectivism at the University of Manitoba - with 2,300 joining online worldwide. They really hit the public consciousness around 2012, when Coursera was created. It partnered with universities to offer courses online, typically with a mix of active participation and self-paced study using filmed lectures and reading lists. Its closest competitor, edX, is a joint venture of Harvard and MIT. As the name suggests, MOOCs are designed for unlimited participation and are free, subsidized, or much cheaper than traditional higher education courses. Although they have been criticized for their low completion rate - an MIT study found an average quit rate of 96% over five years - that has done nothing to stop the demand. In 2019, MOOCs had attracted 110 million students and more than 900 universities around the world had submitted 13,500 courses.

Offered by Duke University. In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.

We study the problem of online learning with primary and secondary losses. For example, a recruiter making decisions of which job applicants to hire might weigh false positives and false negatives equally (the primary loss) but the applicants might weigh false negatives much higher (the secondary loss). We consider the following question: Can we combine "expert advice" to achieve low regret with respect to the primary loss, while at the same time performing {\em not much worse than the worst expert} with respect to the secondary loss? Unfortunately, we show that this goal is unachievable without any bounded variance assumption on the secondary loss. More generally, we consider the goal of minimizing the regret with respect to the primary loss and bounding the secondary loss by a linear threshold. On the positive side, we show that running any switching-limited algorithm can achieve this goal if all experts satisfy the assumption that the secondary loss does not exceed the linear threshold by $o(T)$ for any time interval. If not all experts satisfy this assumption, our algorithms can achieve this goal given access to some external oracles which determine when to deactivate and reactivate experts.