Data Skeptic takes a different take on how we review data--thanks to some healthy skepticism, listeners come out with unusual information and knowledge. The show alternates between interviews with industry experts, and mini episodes wherein the host explains data science tidbits to his non data scientist wife. The tone of this show is simultaneously intellectual and a bit off beat. It's fun, and easier to follow than highly technical podcasts. If you need a series with nice production quality and clear, friendly radio voices, this may be the one.

A small step for Google may very soon become a giant step for mankind. An artificially intelligent computer system built by Google has just beaten the world's best human, Lee Sedol of South Korea, at an ancient strategy game called Go. Go originated in Asia about 2,500 years ago and is considered many, many times more complex than chess, which fell to AI back in 1997. Google's programmers didn't explicitly teach AlphaGo – that's what the system is called - to play the game. Instead, they built a sort of model brain called a neural network that learned how to play Go by itself. As it studied a database of about 100,000 human matches, and then continued by playing against itself millions of times, it constantly reprogrammed itself and improved.

Machine learning is already a very big deal. It's here, and it's in use in far more businesses than you might suspect. A few months back, I decided to take a deep dive into this topic to learn more about it. In today's post, I'll dive into a certain amount of technical detail about how it works, but I also plan to discuss its practical impact on SEO and digital marketing. For reference, check out Rand Fishkin's presentation about how we've entered into a two-algorithm world.

I will start by giving some background information. Currently I am a final year (graduation year) CS student who got interested in machine learning about 6 months ago. I started with the Andrew NG course from Coursera which I recently finished (about 3 weeks ago). When I finished the Coursera course I saw a suggestion that if you'd like to continue to learn more about machine learning you could follow the online Coursera specialization from the Washington university. In this AMA he suggested that if you'd like to learn more about machine learning one of the things you could do was to follow and complete the Coursera course from Andrew NG and their specialization course.

The Unit for Natural Language Processing [1] of the Insight Centre for Data Analytics [2] at the National University of Ireland, Galway [3], invites applications for two PhD positions in Natural Language Processing and Linked Data. The positions are associated with the SFI funded research program on Text Mining with Linked Data. Candidates should preferably have a Masters degree in a relevant field of study with an emphasis on areas such as text mining, natural language processing, computational linguistics, machine learning etc. Please send your application, including CV and a research proposal of up to two pages (both in PDF only) before the closing date of April 25th, 2016 to Dr. Paul Buitelaar at paul.buitelaar@insight-centre.org

These questions originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights. Q: How should one start a career in machine learning? A: There is not just one way. You can start at any age. Some math background (in linear algebra, statistics, and calculus) is recommended, so take classes on these topics, if possible.

There has been much discussion in the last few days about the announcement by the World Economic Forum that it predicts it will take 117 more years until we achieve gender parity in the workplace. It seems crazy that in today's workforce, which is driving developments like self-driving cars, gaming-genius AI, and making hoverboards a reality, we still don't have gender equality. Research published recently by EY makes a compelling case for businesses to do more in terms of tackling existing inequalities: data shows that more diverse company boards command higher share prices and improved financial performance; balanced leadership increases a company's productivity and nationally a country's GDP can be lifted by reducing the gender gap. Another piece of research that looked at start-ups receiving Series A funding in the Bay Area in 2015, showed that only 8% of firms were led by women - that's 16 out of 204 start-ups. And this figure was down by 30% from the previous year.

Our world as we know it is running on artificial intelligence. We have cars that park themselves, and air traffic control is almost fully automated. Virtually every field has benefited from advances in artificial intelligence, from the military to medicine to manufacturing. However, almost none of the recent advancements in artificial intelligence have advanced the education industry. Why is education lagging behind?

A UF club is teaching students about creating systems with artificial intelligence. Nicholas Kroeger, a UF computer science sophomore, and John Henning, a UF computer science and mathematics junior, recently established an artificial intelligence club after realizing UF doesn't offer courses on AI for undergraduate students. The club, which will meet Wednesdays at 6:30 p.m., teaches students how to build computers and software capable of intelligent behavior. "There's no undergraduate classes that I saw that I could take for AI and there's no AI undergraduate major, which I think is really important," Kroeger, 19, said. "I'd like to create those later on in life if I choose to pursue a Ph.D." Bernard Marger, a UF computer engineering senior, said UF used to have an undergraduate artificial intelligence curriculum, but it discontinued when the professor retired.

Editor's Note: Don't miss our new free on-demand training course about how to create data pipeline applications using Apache Spark – learn more here. Decision trees are widely used for the machine learning tasks of classification and regression. In this blog post, I'll help you get started using Apache Spark's MLlib machine learning decision trees for classification. In general, machine learning may be broken down into two classes of algorithms: supervised and unsupervised. Supervised algorithms use labeled data in which both the input and output are provided to the algorithm.