Is the robot revolution arriving sooner -- and with more devastating force -- than you once believed? While much of the automation discussion has surrounded blue-collar careers in manufacturing and transportation, recent studies on the subject see a much wider range of jobs being affected. According to an algorithm developed in 2013 by researchers at Oxford University, 47 percent of U.S. jobs could be automated "in the next decade or two." A more recent multinational study puts 210 million jobs in 32 countries at risk. Now is not the time to panic; now is the time to take action to set your company up for the shifting landscape of work in a way that will protect your business and your most valuable employees.

Teach a sled controlled by artificial intelligence to catch falling Christmas presents! Learn to work in an exciting area of computer science and artificial intelligence. In this course we will train an artificial brain to make the game work. No matter where the present falls, the computer will know exactly how get it. Make an AI Christmas game!

We introduce a method which enables a recurrent dynamics model to be temporally abstract. Our approach, which we call Adaptive Skip Intervals (ASI), is based on the observation that in many sequential prediction tasks, the exact time at which events occur is irrelevant to the underlying objective. Moreover, in many situations, there exist prediction intervals which result in particularly easy-to-predict transitions. We show that there are prediction tasks for which we gain both computational efficiency and prediction accuracy by allowing the model to make predictions at a sampling rate which it can choose itself.

Chatbots are one of the most exciting and in-demand topics in tech. Gartner predicts that by 2020, 85% of businesses will have their own chatbot. If you want to learn this rapidly emerging technology, put a chatbot on your own website or make money by building chatbots for clients, this free chatbot course is for you. This course provides a practical introduction on how to build a chatbot with Watson Assistant (formerly Watson Conversation). Within it, you'll learn how to plan, build, test, analyze, and deploy your first chatbot.

Whether it's business, IT companies, financial services or even education, Artificial Intelligence (AI) is being integrated into various industries. AI's digital, dynamic nature also offers opportunities for student engagement that cannot be found in often outdated documents or in the fixed environment. In a synergistic fashion, AI has the potential to propel and accelerate the discovery of new learning frontiers and the creation of innovative technologies. Though yet to become a standard cult in organizations and schools, Artificial Intelligence in learning or training has been a "big thing" since AI's uptick in the 1940s (when the first seeds of AI were sown with programmable computers). In many ways, the 2 seem made for each other.

Neural Networks are a class of models within the general machine learning literature. So for example, if you took a Coursera course on machine learning, neural networks will likely be covered. Neural networks are a specific set of algorithms that has revolutionized the field of machine learning. They are inspired by biological neural networks and the current so called deep neural networks have proven to work quite very well. Neural Networks are themselves general function approximations, that is why they can be applied to literally almost any machine learning problem where the problem is about learning a complex mapping from the input to the output space. After finishing the famous Andrew Ng's Machine Learning Coursera course, I started developing interest towards neural networks and deep learning.

A basic grounding in the principles and practices around artificial intelligence (AI), automation and cognitive systems is something which is likely to become increasingly valuable, regardless of your field of business, expertise or profession. Fortunately, today you don't have to take years out of your life studying at university to become familiar with this seemingly hugely complex technology. A growing number of online courses have sprung up in recent years covering everything from the basics to advanced implementation. Some are aimed at people who want to dive straight into coding their own artificial neural networks, and understandably assume a certain level of technical ability. Others are useful for those who want to learn how this technology can be applied by anyone, regardless of prior technical expertise, to solving real-word problems.

Here is the course link. Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used for both statistical inference and for prediction. This course will introduce you to Bayesian data analysis: What it is, how it works, and why it is a useful tool to have in your data science toolbox. This chapter will introduce you to Bayesian data analysis and give you a feel for how it works.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligence. This review is not meant to be a tutorial, but when necessary, we provide self-contained derivations for completeness. This review has two features. First, though there are different perspectives to classify DGMs, we choose to organize this review from the perspective of graphical modeling, because the learning methods for directed DGMs and undirected DGMs are fundamentally different. Second, we differentiate model definitions from model learning algorithms, since different learning algorithms can be applied to solve the learning problem on the same model, and an algorithm can be applied to learn different models. We thus separate model definition and model learning, with more emphasis on reviewing, differentiating and connecting different learning algorithms. We also discuss promising future research directions. This review is by no means comprehensive as the field is evolving rapidly. The authors apologize in advance for any missed papers and inaccuracies in descriptions. Corrections and comments are highly welcome.