Statistics.com, a provider of online education in statistics and analytics, announces a partnership with CrowdANALYTIX, a predictive modeling "managed crowdsourcing" company, offering a new online course, "Applied Predictive Analytics in partnership with CrowdANALYTIX", which will run from Oct. 11 to Nov 8, 2013. The goal of this course is to teach users (who have basic knowledge of R programming, predictive analytics and statistics) to apply machine learning techniques in real world case studies. This course provides a hands on approach, presenting the opportunity to participate in a private educational competition hosted by CrowdANALYTIX. Business Case Study: We will study data from the "daily deals" industry (consisting of websites like Groupon, Living Social etc. which source local deals to offer each day). The daily deals industry is emerging and highly competitive.

It was end of last year, I decided to research upon Machine learning (ML) and have been taking few little steps. I need to understand what it's all about ML and related hype factor that it has created in the technology industry. Few articles suggested that I should have good understanding of basic Mathematics, Statistics and few suggested that I need to be good in domain knowledge etc. etc. Most of the basic algorithms or ML Techniques has been there for many years but it has gained lot of momentum now. We see the modern systems have good computing power to execute ML at ease and also due to exponential data growth every year (Lot of data are available to us) which encourages us to build systems that could deliver better insights real-time.

In our latest inspection of Github repositories, we focus on "data science" projects. Unlike other searches we have performed over the past several months, nearly all of the repositories which show up (listed by number of stars* in descending order) are resources for learning data science, as opposed to tools for doing. As such, this is much less a software listing than it is a collection of tutorials and educational resources. There are, however, a few software surprises in here as well, such as a data science-oriented IDE and a great notebook-related project. We include, however, the standard informational notification we have placed on our previous Github Top 10 lists: open source tools have been used by 73% of data scientists in the past 12 months, according to a recent KDnuggets survey (and accounting for the 12 months prior to the survey). While the following repositories focus mainly on learning resources, previous offerings have been software-heavy; also, open source learning materials are the new black, and a main source of learning for data scientists these days.

Just by typing the letters "A," "r-o-b," "w-r-o," "t-h-i," and "s-e-n" into the text messenger on a mobile phone, the predictive text function helped write that first sentence. What will do so is software such as StatsMonkey, which can automate sports reporting. The software analyzes statistics from a baseball game and "generates natural language text" to come up with phrases such as "Things looked bleak for the Angels when they trailed by two runs in the ninth inning" and even includes quotes from players. This is just one of the many well-researched examples presented by Martin Ford in his scarily intriguing new book, "Rise of the Robots." Ford is not some Luddite scared of technology, though.

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him. Lenat fed the massive Traveller rulebook into the system and asked it to find the best way of winning.

As R programming language becoming popular more and more among data science group, industries, researchers, companies embracing R, going forward I will be writing posts on learning Data science using R. The tutorial course will include topics on data types of R, handling data using R, probability theory, Machine Learning, Supervised – unSupervised learning, Data Visualization using R, etc. Before going further, let's just see some stats and tidbits on data science and R.

Organizations like Insight Data science founded by Jake Klamka is specifically designed for helping PhD's transition into industry. At the other end of the spectrum, aspiring data scientists, who have enough domain expertise and are keen to pursue this art can take umbrage from the example of Clare Corthell who has embarked on a self crafted journey to embrace the art of data science purely on online learning MOOCs. In Fact she has herself come out with a curriculum for data science with the Open Source Data Science Masters--OSDSM- program. These courses can help you to bridge the gap in your learning and practicing the craft. The OSDSM is a collection of open source resources that will help you to acquire skills necessary to be a competent entry level data scientist. You can access the curriculum here . You have to be adept at learning and upgrading on the job and on the fly. Kunal Punera the Co founder / CTO at Bento labs talks about this aspect when he says.. I spent two years at RelateIQ. I worked on building the data mining system from scratch -- and by the time I left I had built most of the data products deployed in RelateIQ.

Here's what happened yesterday at the first meeting. Rachel started by going through the syllabus. So, what is data science? This is an ongoing discussion, but Michael Driscoll's answer is pretty good: Data science, as it's practiced, is a blend of Red-Bull-fueled hacking and espresso-inspired statistics. But data science is not merely hacking, because when hackers finish debugging their Bash one-liners and Pig scripts, few care about non-Euclidean distance metrics.

Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. You do not need to know any statistics or linear algebra to understand linear regression. This is a gentle high-level introduction to the technique to give you enough background to be able to use it effectively on your own problems. Linear Regression for Machine Learning Photo by Nicolas Raymond, some rights reserved.

Here is the link to coursera course for NLP Pick the software from the The Stanford NLP (Natural Language Processing) Group and input some text to view its parse tree, named entities, part of speech tags, etc.