Zipfian Academy has graduated more than 50 alumni, placing graduates into data science roles at Facebook, Twitter, Airbnb, Tesla, Uber, Square, Coursera, and many more Silicon Valley companies. Participants in our program come from backgrounds in engineering, data analysis, statistics, and occasionally professional poker. Here, we share an interview with Alex Mentch, a graduate from our Winter 2014 Cohort. Alex hails originally from Idaho, and studied electrical engineering at Washington University in St. Louis. Looking for a career transition into data science, Alex attended our Winter 2014 cohort where he built a search engine for state legislation.

New lip-reading technology developed at the University of East Anglia could help in solving crimes and provide communication assistance for people with hearing and speech impairments. The visual speech recognition technology, created by Dr Helen L. Bear and Prof Richard Harvey of UEA's School of Computing Sciences, can be applied "any place where the audio isn't good enough to determine what people are saying," Dr Bear said. Dr Bear, whose findings will be presented at the International Conference on Acoustics, Speech and Signal Processing (ICASSP) in Shanghai on March 25, said unique problems with determining speech arise when sound isn't available – such as on CCTV footage – or if the audio is inadequate and there aren't clues to give the context of a conversation. The sounds '/p/,' '/b/,' and '/m/' all look similar on the lips, but now the machine lip-reading classification technology can differentiate between the sounds for a more accurate translation. Dr Bear said: "We are still learning the science of visual speech and what it is people need to know to create a fool-proof recognition model for lip-reading, but this classification system improves upon previous lip-reading methods by using a novel training method for the classifiers. "Potentially, a robust lip-reading system could be applied in a number of situations, from criminal investigations to entertainment.

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.

Machine learning, the act of computers learning without being explicitly programmed,has typically been thought of as magic that only mathematicians and programmers could perform. That has been the case for a while and that is due to several reasons. Not only do you need to be able to write code, but you need to have strong math skills. There is no way around it, but you can still do a lot of meaningful work if you don't have the full math background. I believe we are on a path where everyone who programs now will be building some form of machine learning models in the future.

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.

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises -- yet -- but we are getting ever closer to what people tend to call "artificial intelligence." Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects.

Since 2013, MetisMetisVisit their siteView company profile Create Job Alert has been offering a full-time data science bootcamp in New York. Tailored specifically for people with some experience coding and an aptitude for math, the program expanded to San Francisco last year, where it's about to commence its second class. Now, the company has its eyes set on Chicago. This week, Metis announced it will be offering a part-time course in machine learning, taught by machine learning consultant Jeremy Watt, who holds a PhD from Northwestern and has authored an upcoming textbook on the subject. The 36 hour course will start in July and take place over six weeks.

If you have ever tried your hand at lip-reading in a noisy environment, you'll know it isn't easy. Now, researchers have invented a machine that can tell the difference between sounds that look the same on the lips to give anyone the ability to decipher what's being said. It is hoped the new technology could help people with hearing and speech impairments communicate more easily and even help solve crimes. Researchers have invented a machine that can tell the difference between sounds that look the same on the lips to give anyone the ability to decipher what's being said. The visual speech recognition technology, can be applied'any place where the audio isn't good enough to determine what people are saying,' according to Helen Bear, who created the machine alongside Richard Harvey at the University of East Anglia (UEA).

A computer algebra system (CAS) is a software program that allows computation over mathematical expressions in a way which is similar to the traditional manual computations of mathematicians and scientists. The development of the computer algebra systems in the second half of the 20th century is part of the discipline of "computer algebra" or "symbolic computation", which has spurred work in algorithms over mathematical objects such as polynomials. Computer algebra systems may be divided in two classes: the specialized ones and the general purpose ones. The specialized ones are devoted to a specific part of mathematics, such as number theory, group theory, or teaching of elementary mathematics. General purpose computer algebra systems aim to be useful to a user working in any scientific field that requires manipulation of mathematical expressions. The library must cover not only the needs of the users, but also the needs of the simplifier.

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.