In this special guest feature, Karthik Krishnan, Vice President of Product Management at Niara, discusses how machine learning is an option that's gaining popularity to detect cyber attacks given its effectiveness in classifying and clustering attack activity, even within large event data streams. Karthik is responsible for driving product strategy and direction as well as customer engagements. He helped to drive the initial framework for the Niara Security Analytics Platform. Before joining Niara, he served as vice president of product management at Embrane, acquired by Cisco in 2015, and senior director of product management at PGP, acquired by Symantec in 2010. Karthik also spent five years at Juniper as the director of product management, driving product strategy, sales and customer engagements for Juniper's Access Control products.

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

This session took place in February 2016. Part 4 of 7 - Speaker: John Overington, Director Bioinformatics, Stratified Medical Data mining, machine learning and artificial intelligence are becoming the most talk-about topics in digital health. With vast volumes of medical data available, exploiting these techniques to derive valuable insights may both challenge and reshape certain elements of our healthcare system. These new approaches are leading to redefining drug discovery, assisting and automating diagnoses and helping predict and prevent diseases using health record data – or even our digital footprint. But there remains much hype, confusion and misunderstanding in the field.

PRINCETON, NEW JERSEY – Last month, AlphaGo, a computer program specially designed to play the game go, caused shock waves among aficionados when it defeated Lee Sidol, one of the world's top-ranked professional players, winning a five-game tournament by a score of 4-1. Why, you may ask, is that news? Twenty years have passed since the IBM computer Deep Blue defeated world chess champion Garry Kasparov, and we all know computers have improved since then. But Deep Blue won through sheer computing power, using its ability to calculate the outcomes of more moves to a deeper level than even a world champion can. Go is played on a far larger board (19 by 19 squares, compared to eight by eight for chess) and has more possible moves than there are atoms in the universe, so raw computing power was unlikely to beat a human with a strong intuitive sense of the best moves.

They heyday of the app is not coming to an end any time soon. But the next evolution of the apps economy is starting to take shape. And it looks nothing like what we think about as "apps." But that does not mean that the structure of software development and distribution will fundamentally be altered. The app store model of creating software, uploading it to a platform and wirelessly transmitting it to people on demand--a system which dramatically changed the nature of software distribution--will remain intact.

It has been a nightmare of a PR week for Microsoft. It started with the head of Microsoft's Xbox division, Phil Spencer, having to apologise for having scantily clad female dancers dressed as school girls at a party thrown by Microsoft at the Game Developers Conference (GDC). He said that having the dancers at this event "was absolutely not consistent or aligned to our values. That was unequivocally wrong and will not be tolerated". The matter was being dealt with internally and so we don't know who would have been responsible and why they might have thought this was going to be a good idea.

With driverless cars already on California roads, it is not too soon to ask whether we can program a machine to act ethically.

Today, at Build 2016, Microsoft revealed a new AI tool for developers called the Bot Framework. The Bot Framework allows developers to write software bots that interact with people (and other bots). Essentially this makes it easy to create a Conversational User Interface (CUI), ushering in a new era that makes us think more about what a user interface should be. Beyond a visual interface, User Experience (UX) will take on new meaning. The bot will need to lead the user through a series of steps that make sense, yet be able to adjust so as not to be too rigid and lead them down a path that might not make sense for the situation.

We examine top Python Machine learning open source projects on Github, both in terms of contributors and commits, and identify most popular and most active ones.

I have an image database containing raw medical images (lung x-ray image) and their corresponding binary masks indicating blood vessels. I would like to apply machine learning techniques suck as GLM on these training data to build a model. So I can use this model to generate binary blood vessle mask for other lung x-ray images. I need some help for a machine learning workflow/pseudo code for this project.