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    Write a note on devising validating and testing of algorithm

    We need to talk about the values of cryptography, of open software and networks, of hackers being a force for measurable good.

    We need to talk about how infrastructure like DNS -- it was there 25 years ago, we can imagine it will be there 25 years from now -- acts as foundation for future development in a way that the API of the hour doesn't.

    If you do not have a bachelor’s degree, the work experience requirement is greater at 7,500 hours of experience.

    You need to have completed 36 months (three years) of unique, non-overlapping project management experience…

    Things do need to be better, and we need to talk about the role of Government in that. Let's talk about how it really works, so we can discuss how we can do it better.

    The things that need to be better are technical in nature, and guide research priorities that are outright not being addressed at present. The winning submissions to Pwn2Own 2016 provided unprecedented insight into the state of the art in software exploitation.

    feature engineering is another topic which doesn’t seem to merit any review papers or books, or even chapters in books, but it is absolutely vital to ML success.

    This includes getting the best results from the algorithms you are using.

    It also involves getting the most out of the data for your algorithms to work with.

    How do you get the most out of your data for predictive modeling?

    You will discover what feature engineering is, what problem it solves, why it matters, how to engineer features, who is doing it well and where you can go to learn more and get good at it.

    If you read one article on feature engineering, I want it to be this one.

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