Finland knows it doesn't have the resources to compete with China or the United States for artificial intelligence supremacy, so it's trying to outsmart them. "People are comparing this to electricity – it touches every single sector of human life," says Nokia chairman Risto Siilasmaa. From its foundations as a pulp mill 153 years ago, Nokia is now one of the companies helping to drive a very quiet, very Finnish AI revolution. Last May, the small Scandinavian country announced the launch of Elements of AI, a first-of-its-kind online course that forms part of an ambitious plan to turn Finland into an AI powerhouse. To date, more than 130,000 people have completed the course.

Online education in India is unfortunately underrated. Especially when it is touted as the future of education in our country. India's online education market is set to grow to $1.96 billion and almost 9.6 million users by 2021 from $247 million and around 1.6 million users in 2016. This year, it is believed that big data, machine learning, and data science will drive some of the top job opportunities in the country. There has already been a rapid advancement in the digital space that has led to a surge in demand for professionals skilled in the above-mentioned fields.

Hoy traemos a este espacio esta TEd talk titulada "The coming transhuman era: Jason Sosa at TEDxGrandRapids" Sosa is the founder and CEO of IMRSV, a computer vision and artificial intelligence company and was named one of "10 Startups to Watch in NYC" by Time Inc., and one of "25 Hot and New Startups to Watch in NYC" by Business Insider. He has been featured by Forbes, CNN, New York Times, Fast Company, Bloomberg and Business Insider, among others.

This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.

Do you want to learn Python Programming Language? Learn it from the Best Python Tutorial for Beginners, Certification, Course, and Training that you will find online. Python is a high level, general-purpose programming language. It is widely used by programmers all over the world. This object-oriented programming language has a large and comprehensive standard library. Python was first built in the 1980s and since then it has been developing. The latest version of this programming language, Python 3.0, was released in 2008. Ever since it was built, Python has been used by data scientists and programmers in every country. The best thing about Python is that it is easy to understand and adaptable with any of the operating systems. Anyone can learn Python programming language and use it to analyze data, create applications, develop web, and for many other things. It is the most in-demand programming language of this time. Python programmers get highly paid jobs for their skills. We have found the best courses you can find online to learn Python and listed those in here. These online courses will help you to shape your knowledge of Python. So, get through the list and details about those courses and chose one for yourself. Pierian Data International by Jose Portilla is presenting this online course on Python. You can go from the basics to creating your own applications and games with this course. It has a rating of 4.5 out of 5 on Udemy and over 457,000 enrolled students. This python tutorial for beginners provides 24 hours on-demand video, 19 articles and 19 coding exercises with lifetime access. This course will teach you both Python 2 and Python 3. You will learn to use Jupyter Notebook system and Object-Oriented Programming with online classes. This online course on Python programming language has over 100 lectures. It also includes quizzes, tests and homework assignments. They have 3 major projects to complete a Python portfolio.

Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content. Users interact with content and with each other in a complex and dynamic social environment while simultaneously evolving over time. In order to effectively characterize users and predict their future behavior in such a setting, it is necessary to overcome several challenges. Content heterogeneity and temporal inconsistency of behavior data result in severe sparsity at the user level. In this paper, we propose a novel mutual-enhancement framework to simultaneously partition and learn latent activity profiles of users. We propose a flexible user partitioning approach to effectively discover rare behaviors and tackle user-level sparsity. We extensively evaluate the proposed framework on massive datasets from real-world platforms including Q&A networks and interactive online courses (MOOCs). Our results indicate significant gains over state-of-the-art behavior models ( 15% avg ) in a varied range of tasks and our gains are further magnified for users with limited interaction data. The proposed algorithms are amenable to parallelization, scale linearly in the size of datasets, and provide flexibility to model diverse facets of user behavior.

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.