Hoy traemos a este espacio el último número, recién salido de la revista International Journal of Emerging Technologies in Learning (iJET) This interdisciplinary journal aims to focus on the exchange of relevant trends and research results as well as the presentation of practical experiences gained while developing and testing elements of technology enhanced learning. So it aims to bridge the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Readers don't have to pay any fee. Vol 14, No 02 (2019) Table of Contents Papers Multi-Dimensional Analysis to Predict Students' Grades in Higher Education Eslam Abou Gamie, Samir Abou El-Seoud, Mostafa Salama, Walid Hussein Implemented and Tested Conception Proposal of Adaptation Model for Adaptive Hypermedia Mehdi Tmimi, Mohamed Benslimane, Mohammed Berrada, Kamar Ouzzani Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models Mohamed El Fouki, Noura Aknin, Kamal Eddine El Kadiri Visualization Teaching of Deformation Monitoring and Data Processing based on MATLAB 3D Course Teaching Based on Educational Game Development Theory – Case Study of Game Design Course The Development and Performance Evaluation of Digital Museums Toward Second Classroom of Primary and Secondary School – Taking Zhejiang Education Technology Digital Museum as An Example Ying Zheng, Yuhui Yang, Huifang Chai, Mo Chen, Jianping Zhang Students' Beliefs Regarding the Use of E-portfolio to Enhance Cognitive Skills in a Blended Learning Environment Prakob Koraneekij, Jintavee Khlaisang Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System AlBara Khalifa, Tsuneo Kato, Seiichi Yamamoto The Different Roles of Help-Seeking Personalities in Social Support Group Activity on E-Portfolio for Career Development Suthanit Wetcho, Jaitip Na-Songkhla Short Papers A Review of Digital Skills of Malaysian English Language Teachers Mohd Zulhilmi Che Had, Radzuwan Ab Rashid International Journal of Emerging Technologies in Learning.

The level of threat to a given profession depends on two factors: the type of value provided and how it's delivered. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. There is no question that automation is changing the nature of work. But are the robots really coming for your job? One of the most popular narratives is that low-paying jobs are doomed, while college-educated professions will remain largely untouched. Analysts often focus on wages and education as the primary predictors of job evolution, along with organizations' potential to increase efficiency and reduce costs by changing or cutting jobs.

Natural Language Processing (NLP) is a hot topic into the Machine Learning field. This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, Text Data Visualization, Model Building and finally developing NLP applications. In this course you will find a concise review of the theory with graphical explanations and for coding it uses Python language and NLTK library.

Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typically, the cooperation is achieved by letting the data providers exchange their models between neighbors, e.g., recommendation model. However, the best regret bound for a decentralized online learning algorithm is $\Ocal{n\sqrt{T}}$, where $n$ is the number of nodes (or users) and $T$ is the number of iterations. This is clearly insignificant since this bound can be achieved \emph{without} any communication in the networks. This reminds us to ask a fundamental question: \emph{Can people really get benefit from the decentralized online learning by exchanging information?} In this paper, we studied when and why the communication can help the decentralized online learning to reduce the regret. Specifically, each loss function is characterized by two components: the adversarial component and the stochastic component. Under this characterization, we show that decentralized online gradient (DOG) enjoys a regret bound $\Ocal{n\sqrt{T}G + \sqrt{nT}\sigma}$, where $G$ measures the magnitude of the adversarial component in the private data (or equivalently the local loss function) and $\sigma$ measures the randomness within the private data. This regret suggests that people can get benefits from the randomness in the private data by exchanging private information. Another important contribution of this paper is to consider the dynamic regret -- a more practical regret to track users' interest dynamics. Empirical studies are also conducted to validate our analysis.

The demand for data science and machine learning jobs is rapidly rising and the gap between this demand and the number of data scientists available is still very wide. Now, people from an engineering background and pure sciences are shifting their careers in the field of ML. Physics is one such background which falls into this category because of the high level of logic and mathematics required in an ML job. Physics research requires dealing with a lot of data, just like ML. Physicists are also proficient in at least one programming language -- most likely Python, as it is popular in the Physics community as well. There are many physicists today who are data scientists.

Before they got down to business for the day, students in Devin Tatro's social studies class were offered a quiet moment of self-reflection: On this golden fall afternoon at Nome-Beltz Junior/Senior High School, were they feeling chipper, distressed, or somewhere in between? One by one, they selected the picture of the facial expression that best matched their mood, and with a swift click sent an answer to the teacher. She scanned the responses and made a few mental notes. Then, without missing a beat, she switched the smartboard display and launched into a multiple-choice quiz using a game-based online learning platform called Kahoot! "Tell me one thing you remember about yesterday's lesson on expansions and tax on Native Americans," Tatro said, pacing the front of the classroom. She rattled off students' responses as they popped up on the smartboard in a colorful word cloud: "Forced relocation, reduced population, disease, warfare, cultural destruction ... wow, that's a powerful term."

No doubt, they are quite extensive -- and in high demand. "We've seen a tremendous rise in interest and enrollment in AI and machine learning, not just year over year but month over month as well. From 2017 to 2018, we saw over 30% growth in demand for courses on AI and machine learning. In 2018, we saw an even more significant rise with a 70% increase in demand for AI and machine learning courses. We anticipate interest to continue to grow month over month in 2019."

Get your team access to Udemy's top 3,000 courses anytime, anywhere. This course is designed to be accessible to brand new Python programmers but also worthwhile for more experienced Pythonistas who want to get started with AI and Natural Language processing. You do not any previous experience with Python or programming to be successful in this course. You can use a Windows or Mac computer to complete the course (or Linux for that matter).

Get Udemy Discount Coupon The Beginner's Guide to Artificial Intelligence in Unity. The course begins with a detailed examination of vector mathematics that sits at the very heart of programming the movement of NPCs. Following this systems of waypoints will be used to move characters around in an environment before examining the Unity waypoint system for car racing with AI controlled cars. This leads into an investigation of graph theory and the A* algorithm before we apply these principles to developing navmeshes and developing NPCs who can find their way around a game environment. Before an aquarium is programmed complete with autonomous schooling fish, crowds of people will be examined from the recreation of sidewalk traffic to groups of people fleeing from danger.

After working as Electronic Engineer, I decided to change my career path to Data Scientist . To reach my Data Science career goal I have started to review Moocs about this field. All these courses are explain core machine learning algorithms. Also, in Coursera's Machine Learning course Andrew NG explained the mathematical background of these algorithms. If you want to learn what Machine Learning is and the way that you can use it, i strongly suggest you to take these entire three courses.