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Top 5 Courses to Learn Natural Language Processing (NLP) for Beginners in 2021 - Best of Lot

#artificialintelligence

Hello guys, if you want to learn Natural Langauge Processing (NLP) and looking for the best online training courses then you have come to the right place. Earlier, I have shared the best courses to learn Data Science, Machine Learning, Tableau, and Power BI for Data visualization and In this article, I'll share the best online courses you can take online to learn Natural Langauge Processing or NLP. These are the best online courses from Udemy, Coursera, and Pluralsight, three of the most popular online learning platforms. They are created by experts and trusted by thousands of developers around the world and you can join them online to learn this in-demand skill from your home. Natural language processing is a science related to Artificial Intelligence and Computer Science that uses data to learn how to communicate like a human being and answer questions, translate texts, spell check, spam filtering, autocomplete, chatbots that you can interact with such as Siri and Alexa, and more applications.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


15 Best Udacity Machine Learning Courses

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This is an intermediate-level free artificial intelligence course. This course will teach the basics of modern AI as well as some of the representative applications of AI including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. To understand this course, you should have some previous understanding of probability theory and linear algebra.


Personalized Education in the AI Era: What to Expect Next?

arXiv.org Artificial Intelligence

The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.


Classification of Pedagogical content using conventional machine learning and deep learning model

arXiv.org Artificial Intelligence

Billions of users create a large amount of data every day, which in a sense comes from various types of sources. This data is in most cases unorganized and unclassified and is presented in various formats such as text, video, audio, or images. Processing and analyzing this data is a major challenge that we face every day. The problem of unstructured and unorganized text dates back to ancient times, but Text Classification as a discipline first appeared in the early 60s, where 30 years later the interest in various spheres for it increased [1], and began to be applied in various types of domains and applications such as for movie review [2], document classification [3], ecommerce [4], social media [5], online courses [6, 7], etc. As interest has grown more in the upcoming years, the uses start solving the problems with higher accurate results in more flexible ways. Knowledge Engineering (KE) was one of the applications of text classification in the late 80s, where the process took place by manually defining rules based on expert knowledge in terms of categorization of the document for a particular category [1]. After this time, there was a great wave of use of various modern and advanced methods for text classification, which all improved this discipline and made it more interesting for scientists and researchers, more specifically the use of machine learning techniques. These techniques bring a lot of advantages, as they are now in very large numbers, where they provide solutions to almost every problem we may encounter. The need for education and learning dates back to ancient times, where people are constantly improving and trying to gain as much knowledge as possible.


Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

arXiv.org Artificial Intelligence

In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.


NLP - Natural Language Processing with Python

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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


Aspect-Based Sentiment Analysis in Education Domain

arXiv.org Artificial Intelligence

Analysis of a large amount of data has always brought value to institutions and organizations. Lately, people's opinions expressed through text have become a very important aspect of this analysis. In response to this challenge, a natural language processing technique known as Aspect-Based Sentiment Analysis (ABSA) has emerged. Having the ability to extract the polarity for each aspect of opinions separately, ABSA has found itself useful in a wide range of domains. Education is one of the domains in which ABSA can be successfully utilized. Being able to understand and find out what students like and don't like most about a course, professor, or teaching methodology can be of great importance for the respective institutions. While this task represents a unique NLP challenge, many studies have proposed different approaches to tackle the problem. In this work, we present a comprehensive review of the existing work in ABSA with a focus in the education domain. A wide range of methodologies are discussed and conclusions are drawn.


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.


What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

arXiv.org Machine Learning

Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of "what should one learn first," we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.