During the company's Worldwide Partner Conference, Microsoft has unveiled its upcoming plans to provide online degrees that cater to the demands of highly competitive technological fields. Officially launched as the Microsoft Professional Degree (MPD) program, the first course offered to interested professionals and fresh graduates alike, mainly focuses on skill development and education through a Data Sciences curriculum. "Recognizing a shortage of qualified individuals to fill the growing need for data scientists, Microsoft consulted with education and industry partners to develop a curriculum concentrated on developing the skills and real world experience these new roles require," says Microsoft. This specific MPD program features courses that educate incoming applicants on how they can visualize and implement data in Microsoft Excel and Power BI, as well as supplemental (and needed) skills in R and Python programming language, statistics and machine learning. "At Microsoft, we believe the approach and tools used for learning need to continually evolve to meet the demands of our device-centric and data-driven world," said Alison Cunard, the general manager at Microsoft Learning Experiences.

You can watch the Azure ML tutorial video at the landing page to have a quick overview about the Azure ML service. This will open the page where we will develop our Azure ML experiments in the next labs.

In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us? A neural network gets an image as an input, and returns a classification result: yes, that's a face. It achieves this by recognising features in an hierarchical fashion.

Teachers are not ends-in-themselves, they are always a means to an end - improvements in the learner. Given this premise, could it be possible to eventually replace teachers with AI technology? This may not happen soon but let's, as a thought experiment, ask whether it could. Obvious points are that AI is 24/7, fast, scalable and cheaper. This gives it a head start.

This course will help you to gain the skills to use one of the fastest growing mobile technologies, Chatbots. Now you too can learn to build sophisticated Chatbots for your customers all with NO Coding. This course is for those in Web design, marketing and graphics, who want to be able to offer their clients something new and exciting. I will cover Chatbots for Websites, for Facebook, for KiK, and for Slack – although the bots created can be used also on many other services. I will show you examples of travel bots, entertainment bots, productivity bots, and retail bots.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

I've worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what's going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. It's on web instead of PDF because all books should be, and eventually it will hopefully include animations/demos etc. My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. Thus, this tutorial will contain very little math (I don't believe it is necessary and it can sometimes even obfuscate simple concepts). Since my background is in Computer Science and Physics, I will instead develop the topic from what I refer to as hackers's perspective. Basically, I will strive to present the algorithms in a way that I wish I had come across when I was starting out. "…everything became much clearer when I started writing code." You might be eager to jump right in and learn about Neural Networks, backpropagation, how they can be applied to datasets in practice, etc. But before we get there, I'd like us to first forget about all that. Let's take a step back and understand what is really going on at the core. Update note: I suspended my work on this guide a while ago and redirected a lot of my energy to teaching CS231n (Convolutional Neural Networks) class at Stanford. The notes are on cs231.github.io These materials are highly related to material here, but more comprehensive and sometimes more polished. In my opinion, the best way to think of Neural Networks is as real-valued circuits, where real values (instead of boolean values {0,1}) "flow" along edges and interact in gates. However, instead of gates such as AND, OR, NOT, etc, we have binary gates such as * (multiply), (add), max or unary gates such as exp, etc.

A big benefit of using the Weka platform is the large number of supported machine learning algorithms. The more algorithms that you can try on your problem the more you will learn about your problem and likely closer you will get to discovering the one or few algorithms that perform best. In this post you will discover the machine learning algorithms supported by Weka. How to Use Machine Learning Algorithms in Weka Photo by Eugeniy Golovko, some rights reserved. Weka has a lot of machine learning algorithms.

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.

To download all course materials, select the compressed .zip To download individual course materials, select the checkbox next to each file that you'd like to download and click download.