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Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

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Computers learn differently: their hardware remains always constant, and never reconfigures itself. What changes is the software; the data, or the program, or both. A human mind is a work-in-progress, as the brain evolves and constantly changes. A computer mind does not evolve, or change its structure, or hardware; it becomes better by accumulating and storing more data, by creating more connections between the data, and by running logical programs of better quality faster. This subtle difference between computers and biological brains is what makes humans have subjective experiences, as opposed to computers that cannot.

I think I read about activation functions "squashing inputs into outputs" five or six times before I finally started getting the gist of them. This was no fault of the material I was reading, they were just this abstract concept that I just accepted and shoved my inputs into when I was building a neural network. However, I finally feel that I have a much clearer picture of why they are used and how well each one performs for certain tasks. I hope somehow I can squash this concept into your head by the end of this article so something useful can come out in the future! I'm sure you will catch on much faster than I did.

Last week at Microsoft's Build conference, CEO Satya Nadella said that the future of the company was "conversation as platform." In other words, less Windows and Office, and more Cortana and Tay--conversational interfaces that can understand the natural language of human users. If Nadella thought he was expressing some unique vision of the future, though, he was fooling himself. The idea of conversational UI has quickly colonized nearly every corner of Silicon Valley over the past year. Now seems like a good time to ask: What is a conversational interface?

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Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. In the pixelated cube world of "Minecraft," players can create almost anything their hearts desire. Now, Microsoft is using the popular world-building game to build and test artificial intelligence in the fictional environment. Microsoft has made a platform for (AI) research using a modified version of "Minecraft" that will become available to the public following a limited release to select researchers.

DARPA are starting a competition to help automate defence and see how artificial intelligence can combat cyber-threats. The latest DARPA Grand Challenge is looking to artificial intelligence to seek out and destroy vulnerabilities in software. The US Defense Advanced Research Projects Agency (DARPA) Cyber Grand Challenge will see seven teams battle it out to see if machine learning can do better in finding and fixing exploits better than humans. The agency said on its competition website that identifying threats and remediating them can take over a year from first detection to the deployment of a solution, by which time critical systems may have already been breached. "This slow reaction cycle has created a permanent offensive advantage," reads the blurb.