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The Dragonfly Machine Learning Engine (MLE) provides the machine learning and data science capabilities included within OPNids. Data science and machine learning promise to counteract the dynamic threat environment created by growing network traffic and increasing threat actor sophistication. This post will provide an overview of the MLE engine itself, reasoning for why data science and cybersecurity go together, and some insight into using the MLE as part of the OPNids system. The Dragonfly MLE is available as part of OPNids. The Dragonfly MLE provides a powerful framework for deploying anomaly detection algorithms, threat intelligence lookups, and machine learning predictions within a network security infrastructure.


Taking the pulse of machine learning adoption ZDNet

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A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Taking the pulse of machine learning adoption

ZDNet

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Machine Learning and Data Science Redefining the African Continent

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With the continuous evolution of technology and new developments arising from the need to integrate technology to efficiently deliver an excellent digital consumer experience, more women are taking charge by being part of this evolution through their involvement in local communities tailored to effectively share resources and current trends in data science and Machine Learning. The WiMLDS community which comprises of data scientist and machine learners aims at increasing representation of women data scientist into the tech space, the luck of therefore presented an opportunity to build up this local community where the majority are self taught and hence are able to keep up with the ever highly advancing technology. "We are all largely self taught so we found each other while looking for data science and machine learning communities to aid our learning journeys. There was no such community in existence and the opportunity presented itself to start a local chapter of Women in Machine Learning and Data Science. So we jumped at it and now it has been almost 2 years," says Kathleen Siminyu Head of data science at Africa's Talking.


Different methods of feature selection

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In our previous post, we discussed what is feature selection and why we need feature selection. In this post, we're going to look at the different methods used in feature selection. There are three main classification of feature selection methods – Filter Methods, Wrapper Methods, and Embedded Methods. Filter methods are learning-algorithm-agnostic, which means they can be employed no matter which learning algorithm you're using. They're generally used as data pre-processors.


Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework

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One of the most difficult and most critical parts of implementing data science in business is quantifying the return-on-investment or ROI. In this article, we highlight three reasons you need to learn the Expected Value Framework, a framework that connects the machine learning classification model to ROI. Further, we'll point you to a new video we released on the Expected Value Framework: Modeling Employee Churn With H2O that was recently taught as part of our flagship course: Data Science For Business (DS4B 201). The video serves as an overview of the steps involved in calculating ROI from reducing employee churn with H2O, tying the key H2O functions to the process. Last, we'll go over some Expected Value Framework FAQ's that are commonly asked in relation to applying Expected Value to machine learning classification problems in business.


Notes from Microsoft Machine Learning and Data Science Summit – Day 1 – R&D

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Microsoft Machine Learning & Data Science Summit is taking place in conjunction with Microsoft Ignite at Georgia World Congress Center. Today, day 1 started with keynote by Dr. Joseph Sirosh who identified three axes of innovation along with various customer case studies. Thought leaders and Microsoft engineers discuss the latest Big Data, Machine Learning, Artificial Intelligence, and Open Source techniques and technologies along with important case studies. There were various great take aways from sessions.


Impact of Artificial Intelligence in Pharma: An overview – Saty Mohanty

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Today, AI is being implemented in every industry that we can think of. Some use it as a buzzword to generate hype while others truly understand the full potential of the value it brings. To keep up with the 21st century, the pharmaceutical industry is constantly on research mode and AI is drawing plenty of attention in the industry. The widespread success of AI in several industries has led many big pharma companies to invest in it. AI leaves a deep impact on pharma and will positively affect drug invention and development--especially cost--in the long-run.


Attacks Against Machine Learning -- An Overview - Predictive Analytics Times - machine learning & data science news

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Adversarial inputs, which are specially crafted inputs that have been developed with the aim of being reliably misclassified in order to evade detection. Adversarial inputs include malicious documents designed to evade antivirus, and emails attempting to evade spam filters. Data poisoning attacks, which involve feeding training adversarial data to the classifier. The most common attack type we observe is model skewing, where the attacker attempts to pollute training data in such a way that the boundary between what the classifier categorizes as good data, and what the classifier categorizes as bad, shifts in his favor. The second type of attack we observe in the wild is feedback weaponization, which attempts to abuse feedback mechanisms in an effort to manipulate the system toward misclassifying good content as abusive (e.g., competitor content or as part of revenge attacks).


Big Data Tech 2018: Scalable Automatic Machine Learning with...

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In this presentation, Erin LeDell (Chief Machine Learning Scientist, H2O.ai), will provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard. Erin will also provide simple code examples to get you started using AutoML.