Education technology has the power to revolutionize education but with the integration of artificial intelligence, experts believed that it can be more beneficial, particularly in higher education and online learning. In an era where modern technology has become a valuable influence in the lives of humans, it's safe to assume that technology will be able to enhance the learning experience of educators and students, especially in higher education and online learning. As experts combined education technology (EdTech) and artificial intelligence (AI), a powerful tool to potentially transform education has been born. Due to the pervasiveness of technology today, the way students communicate and entertain themselves have changed. But some experts believed that the implementation of education technology alone in schools, colleges and universities across the nation is not enough to revolutionize education.

Learn how to design a spam classifier using machine learning APIs with this webinar. Syed is an IT Engineering Manager with 14 years of experience in software development, technology management, IT strategy, technology consulting, and product development.

The combination of big data and machine learning is a revolutionary technology that can make a great impact on any industry if used in a proper way. In the field of healthcare it has great usage in cases like early disease detection, finding signs of early breakouts of epidemics, using clustering to figure out regions of epidemics (e.g. In this article, I have tried to explore the prediction of the existence of heart disease by using standard machine learning algorithms, and the big data toolset like Apache Spark, parquet, Spark mllib, and Spark SQL. The source code of this article is available on GitHub here. Also, you can check out the entire eclipse project from here.

Designed to help understand the fundamentals of DS & Algorithms really well. A must have for programming interviews.

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.

The ultimate goals of serious education games (SEG) are to facilitate learning and maximizing enjoyment during playing SEGs. In SEG development, there are normally two spaces to be taken into account: knowledge space regarding learning materials and content space regarding games to be used to convey learning materials. How to deploy the learning materials seamlessly and effectively into game content becomes one of the most challenging problems in SEG development. Unlike previous work where experts in education have to be used heavily, we proposed a novel approach that works toward minimizing the efforts of education experts in mapping learning materials to content space. For a proof-of-concept, we apply the proposed approach in developing an SEG game, named \emph{Chem Dungeon}, as a case study in order to demonstrate the effectiveness of our proposed approach. This SEG game has been tested with a number of users, and the user survey suggests our method works reasonably well.

In the last several decades, thousands of machine learning algorithms have been developed. Very often, the selection of an algorithm to solve a particular problem is driven more by the data scientist's familiarity with a small subset of available algorithms, than optimizing for predictive power or operational constraints. This is unsurprising: Newcomers to machine learning and veteran data scientists alike, may be overwhelmed by the multitude of machine learning algorithms and where and how it is most appropriate to use them. In this webinar, Daniel Emaasit will introduce Model-Based Machine Learning (MBML), an approach to machine learning which addresses these challenges. Daniel will discuss the various uses of MBML, from tasks such as classification, to regression and clustering, and how it allows data scientists to address the uncretainty inherent to real-world machine learning applications.

The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services. This is a step-by-step guide which my colleague Peter Daukintis has developed this tutorial which walks you through the development of a Microsoft Bot in C# using the Bot Framework Connector SDK .NET template. You will need to have a Microsoft Band and have collected some sleep data using a Microsoft Band and have had that synchronised up to the Microsoft cloud as this tutorial uses the Bot Framework to provide access to that data. Heart Rate monitoring depends on the current Band mode, It only monitors hr continuously when in exercise mode (along with all other sensors), all of the other scenarios have pre-defined cadences that allow the data to have analytical relevance whilst only using the sensors as much as needed. This is the information that is sent to MS Health, and as such is available through the Cloud API, along with the curated information that is derived by MS Health, e.g sleep efficiency, recovery time, etc.

Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data. It is popular in machine learning and artificial intelligence text books to first consider the learning styles that an algorithm can adopt. There are only a few main learning styles or learning models that an algorithm can have and we'll go through them here with a few examples of algorithms and problem types that they suit. This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result. When crunching data to model business decisions, you are most typically using supervised and unsupervised learning methods.