This is the hard skills development open hack session. Experts will be available to assist with a variety of things from Tensor Flow to Docker to Microsoft Cognitive Services and Azure. There are several projects in motion as well as folks taking several online classes. Come to learn about Python, Machine Learning, Robotics, how they work together and start getting some hands on experience. There will be pointers to guided tutorials as well as other experts.

Decision trees can suffer from high variance which makes their results fragile to the specific training data used. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance. In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.

Azure Machine Learning is a fully managed predictive analytics service that you can use to create predictive models against your data in SQL Data Warehouse, and then publish as ready-to-consume web services. You can learn the basics of predictive analytics and machine learning by reading Introduction to Machine Learning on Azure. You can then learn how to create, train, score and test a machine learning model using the Create experiment tutorial. We will read data from Product table in the AdventureWorksDW database. Start a new experiment by clicking NEW at the bottom of the Machine Learning Studio window, select EXPERIMENT, and then select Blank Experiment.

Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise. The course is launched for the first time and has limited spaces. Created in partnership with H2O.ai, the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai. To sign up or learn more, email info@futuretext.com The course targets developers and Architects who want to transition their career to Enterprise AI.

If you're keeping up with the rapid changes in the technology industry, you're seeing a bunch of terms thrown around as if they're interchangeable--but really, there are some pretty important distinctions. In this post, we're going to demystify the differences, and clarify the relationships, among these terms, especially artificial intelligence, machine learning, and self-driving cars. Let's begin with a simple model for how we'll approach this topic: Artificial intelligence is the broad field that covers all sorts of different initiatives and efforts to create machines that behave intelligently. What exactly it means to'behave intelligently' is a question best left for the philosophers and cognitive scientists, but for us, it refers to creating machines that do the highly complex things that only humans have previously been able to do. That means that AI is about creating machines that do more than just follow the commands that we give them. They can process input, make decisions, and take action.

SAP SE (NYSE: SAP) today introduced three initiatives to make its business applications more intelligent and empower its ecosystem to build machine learning (ML) applications for customers. Spanning its own solutions, partner programs and educational offerings, these programs will help accelerate ML adoption across SAP's global customer base. This announcement was made at the SAP TechEd conference, being held November 8-10, 2016, in Barcelona. First, SAP has unveiled new intelligent business applications. A new solution, "brand intelligence," is supposed to analyze brand exposure in video and images by leveraging deep learning.

Artificial Intelligence is not about statistics, is about experience. In this webinar Stuart Waplington, Co-Founder and CEO, will walk you through what deep learning really means and the possibilities that have been unlocked to the marketing world. The global market for AI is set to be worth $5.05 Billion by 2020 (Markets & Markets). Happy Finish is already ahead of the curve; we've just presented Shoegazer, a unique Proof of Concept that uses AI and Transfer Learning to identify the exact brand and style of trainers in real-time – with 95% accuracy and Buzzteam, our on-demand workforce resource platform which allows individual companies building teams by employing global network of resources that can be discovered by skill-set, experience, cost or rating. Join this webinar to understand AI and how its rapid adoption is set to transform a range of markets, from advertising and media to finance and retail, offering benefits such as improved productivity and increased customer satisfaction.

Data Analytics is one of the most sought-after skill sets today, with students and professionals alike aspiring to be enabled with the necessary skills to derive data-driven business insights in their careers. It also helps organisations attain a competitive advantage over others. Data Analytics is not limited to mathematicians, statisticians or IT professionals with programming skills. The need to analyse data has become so elementary today that a professional in any business is expected to know the necessary skills. While professionals today are aware of the need to be trained, some are unaware of how to embark on a career in analytics.

Using that information, water authorities or companies can target areas or homes where people are wasting water, and send specialized educational materials to let people know how they can cut down on water waste. Some of OmniEarth's customers have already started seeing results, with some reporting a 15 percent reduction in water use, just by using the conservation messages. Many people, Fentzke said, may not even realize that they're wasting water. There could be bad meters, leaks, or bad settings on automatic sprinkling systems that are contributing to the problem. "We've found it to be very effective in targeting people who may not even know that they're inefficient," Fentzke said.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.