data science


Machine learning fundamentals: What cybersecurity professionals need to know - Help Net Security

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In this Help Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know. Hi, this is Chris Morales and I'm Head of Security Analytics at Vectra, and in this Help Net Security podcast I want to talk about machine learning fundamentals that I think we all need to know as cybersecurity professionals. AI has become very used within our industry more and more, and here at Vectra we are an AI company as well. As you start to hear more about AI, you have to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just kind of a quick understanding of the different algorithms or understanding when you need to use certain algorithms for specific jobs. There has always been a very muddled use of the terms artificial intelligence, data science and machine learning.


Data science is a growing field. Here's how to train people to do it

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The world is inundated with data. Take just the global financial markets. They generate vast amounts of data – share prices, commodity prices, indices, option and futures prices, to name just a few. But data is of no use if there aren't people able to collect, collate, analyse and apply it to the benefit of society. All that data generated by global financial markets gets used for asset and wealth management – and it must be properly analysed and understood to inform good decision making.


Getting Started With Google Colab – Towards Data Science

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Just let me code, already! You know it's out there. You know there's free GPU somewhere, hanging like a fat, juicy, ripe blackberry on a branch just slightly out of reach. Wondering how on earth to get it to work? For anyone who doesn't already know, Google has done the coolest thing ever by providing a free cloud service based on Jupyter Notebooks that supports free GPU.


Automated Machine Learning: is it the Holy Grail? - AnalyticsWeek

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Machine learning is in the ascendancy. Particularly when it comes to pattern recognition, machine learning is the method of choice. Tangible examples of its applications include fraud detection, image recognition, predictive maintenance, and train delay prediction systems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not the only ones). Data Quality – Data from multiple sources across multiple time frames can be difficult to collate into clean and coherent data sets that will yield the maximum benefit from machine learning.


Career Comparison: Machine Learning Engineer vs. Data Scientist--Who Does What? - Springboard Blog

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There's some confusion surrounding the roles of machine learning engineer vs. data scientist, primarily because they are both relatively new. However, if you parse things out and examine the semantics, the distinctions become clear. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something. But before we go any further, let's address the difference between machine learning and data science. It starts with having a solid definition of artificial intelligence.


The Best Free Books for Learning Data Science

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The Elements of Statistical Learning - Another valuable statistics text that covers just about everything you might want to know, and then some (it's over 750 pages long). Make sure you get the most updated version of the book from here (as of this writing, that's the 2017 edition). Data Mining and Analysis - This Cambridge University Press text will take you deep into the statistics and algorithms used for various types of data analysis. Do you need books to learn data science?


Should I Open-Source My Model? – Towards Data Science

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I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.


Cloudera's Hilary Mason: To make AI useful, make it more "boring"

ZDNet

When it comes to Artificial Intelligence, the industry is at a crossroads of fascination versus function. We're awed by the technology, but a number of forces are conspiring to minimize the progress we're making, especially in the Enterprise. There are a few people out there who are adamantly trying to address this. One of them is Hilary Mason, Cloudera's GM of Machine Learning. Mason was previously Chief Data Scientist at Bitly, then founder and CEO of Fast Forward Labs, which Cloudera acquired in 2017.


The Data Driven Partier: Movie Mustache – Towards Data Science

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The concept behind'Movie Mustache' is simple, but revolutionary. This game was foreign to me until a few weeks ago when I got to experience it watching the Adam Sandler classic, The Waterboy. As amazing as it was to watch every single character wear the handlebar mustaches that were taped to the TV, that paled in comparison to the problem statement that followed: How can we place the mustaches to maximize our drinking as a group? As always, the code seen here can be viewed in its entirety on my GitHub. Due to the availability of facial recognition packages, this problem can be solved in less than a day with the right approach.


A Tell of Tensorflow Probability – Towards Data Science

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Regardless of whatever we think about the mysterious subject of Probability, we live and breath in a stochastic environment. From the ever elusive Quantum Mechanics to our daily life ("There is 70% chance it will rain today", "The chance of getting the job done in time is less than 30%" …) we use it, knowingly or unknowingly. We live in a "Chancy, Chancy, Chancy world". And thus, knowing how to reason about it, is one of the most important tools in the arsenal of any person. In this article, I will not introduce or explain probability in details (that would make it too long anyway), neither will I try to give some general picture.