This article is on preparing data for modeling in R using vtreat. Suppose we wish to work with some data. Our example task is to train a classification model for credit approval using the ranger implementation of the random forests method. We will take our data from John Ross Quinlan's re-processed "credit approval" dataset hosted at Lichman, M. (2013). For convenience we have copied the data to our working directory here.

Bloq, a provider of blockchain technology solutions for global enterprises, announced that it has acquired Skry (formerly Coinalytics), a pioneer in blockchain analytics, to accelerate the development of its analytics capabilities and open the door for Artificial Intelligence (AI) on its platform. With the acquisition, Bloq wants to enhance its suite of analysis tools and position itself to maximize the value of blockchain data sets through AI and machine learning. The Chicago-based company focuses on solving key business issues surrounding security, provenance, authentication and reconciliation. The new acquisition, whose detailed terms haven't been disclosed, includes Skry's intellectual property and team, which seems a perfect fit for Bloq's focus on empowering better visibility and decision-making in a multi-blockchain, multi-network world. "Financial institutions will need a full suite of tools to take blockchain [technology]'s role from high-tech database to business-driver," Bloq's Co-Founder and Chairman Matthew Roszak explained to Bitcoin Magazine.

These days, marketers can't read about their profession without getting bombarded with wild claims about how AI is going to disrupt everything they do. And with the sales and marketing functions evolving so rapidly in recent years, marketers in particular must embrace an entrepreneurial spirit and constantly explore new technologies in order to give their team a competitive edge. That mindset shift, along with new consumer trends -- such as self-driving cars and intelligent voice-first products like Amazon's Alexa and Apple's Siri -- are bringing the possibilities of AI to the forefront of business-to-business marketing technology discussions. But all of this begs the question, "Which AI claims are hype and which are reality?" In order to know what a new technology like AI can bring to the table, it's important to fully understand the problems you're trying to solve.

ML differs from traditional programming in that the program discovers or learns rules from the data. When we think of how we learn ourselves, we tend to learn general concepts or broad skillsets. However ML is best applied to solve a specific task. In this sense ML is similar to a normal program where a specific goal is quite well defined. The difference is that a typical ML problem is more complex and therefore manually hardcoding rules would be unviable.

If this job is available in multiple locations, by applying to this position you may be considered for the additional locations in your area The Principal Data Scientist will be responsible for designing and implementing processes and layouts for complex, large-scale data sets used for modeling, data mining, and research purposes. This role supports the Advertising Analytics and Audience Measurement team, and interfaces with various marketing, media and advertising functions within AT&T's Entertainment Group. Specific responsibilities and requirements of this opportunity include: · Use data mining techniques to reveal patterns in the data that have implications on the business decisions. Actively promotes good working relationships and collaboration across multiple teams within Big Data and across the enterprise. Ability to interface with and convey technical concepts and approaches to a non-technical/senior management audience Qualifications: · Requires extensive specialized technical expertise · Preferred Masters of Science in Computer Science, Math or Scientific Computing; Data Analytics, Machine Learning or Business Analyst nanodegree; or equivalent experience.

Some people think I am a machine learning expert. And in some ways, I am, which is great, because it is suddenly the flavor of the week--and I have lots of companies asking me for help solving their marketing problems with this new secret sauce. What I know is less how machine learning works, and more how to know that machine learning will help you with your marketing problem. What I don't know is all how all of the lovely algorithms in the picture above actually work. And you don't need to, either.

It's not enough for marketers to collect petabytes of data; it takes a sharp mind to make sense of it all. Actually it takes a nonhuman one. That's why artificial intelligence has invaded the marketing world, with Facebook, Google, Salesforce, IBM, Amazon, and others building machine learning into their platforms. Now marketers must understand the lingo if they're going to survive the machines. To help, here's a guide to the terminology around A.I. When a machine teaches itself with minimal programming needed.

Machines are finally getting the best of humans at poker. Two artificial intelligence (AI) programs have finally proven they "know when to hold'em, and when to fold'em," recently beating human professional card players for the first time at the popular poker game of Texas Hold'em. And this week the team behind one of those AIs, known as DeepStack, has divulged some of the secrets to its success--a triumph that could one day lead to AIs that perform tasks ranging from from beefing up airline security to simplifying business negotiations. AIs have long dominated games such as chess, and last year one conquered Go, but they have made relatively lousy poker players. In DeepStack researchers have broken their poker losing streak by combining new algorithms and deep machine learning, a form of computer science that in some ways mimics the human brain, allowing machines to teach themselves.

Researchers from Argonne National Laboratory, using supercomputers at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC), are employing machine learning algorithms to accurately predict the physical, chemical and mechanical properties of nanomaterials, reducing the time it takes to yield such predictions from years to months--in some cases even weeks. This approach could help accelerate the discovery and development of new materials. Using a modeling framework built around a molecular dynamics code (LAMMPS), the research team ran a series of simulations to study the structure and temperature-dependent thermal conductivity of stanene, a 2D material made up of a one-atom-thick sheet of tin. This work, which involved a set of parameters known as the "many-body interatomic potential" or "force field," yielded the first atomic-level computer model that accurately predicts stanene's structural, elastic and thermal properties. The findings were published in The Journal of Physical Chemistry Letters.