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How to Mitigate IoT Security Threats in 2022

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The Internet of Things (IoT) plays a key role in digital transformation. However, in many cases, organizations realize that they already have a large fleet of legacy IoT devices that have been gradually deployed over the years. Many of these devices may not have been designed with security in mind. One of the biggest concerns of IoT is managing the risks associated with a growing number of IoT devices. Information security and privacy issues related to IoT devices have attracted global attention, because of the ability of these devices to interact with the physical world.


datamining_2022-09-18_23-45-00.xlsx

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The graph represents a network of 3,785 Twitter users whose tweets in the requested range contained "datamining", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 19 September 2022 at 06:55 UTC. The requested start date was Monday, 19 September 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 2-hour, 31-minute period from Monday, 05 September 2022 at 00:29 UTC to Sunday, 18 September 2022 at 03:00 UTC.


Putting artificial intelligence and machine learning workloads in the cloud

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Artificial intelligence (AI) and machine learning (ML) are some of the most hyped enterprise technologies and have caught the imagination of boards, with the promise of efficiencies and lower costs, and the public, with developments such as self-driving cars and autonomous quadcopter air taxis. Of course, the reality is rather more prosaic, with firms looking to AI to automate areas such as online product recommendations or spotting defects on production lines. Organisations are using AI in vertical industries, such as financial services, retail and energy, where applications include fraud prevention and analysing business performance for loans, demand prediction for seasonal products and crunching through vast amounts of data to optimise energy grids. All this falls short of the idea of AI as an intelligent machine along the lines of 2001: A Space Odyssey's HAL. But it is still a fast-growing market, driven by businesses trying to drive more value from their data, and automate business intelligence and analytics to improve decision-making. Industry analyst firm Gartner, for example, predicts that the global market for AI software will reach US$62bn this year, with the fastest growth coming from knowledge management.


The 18 best data science podcasts on SoundCloud, Apple podcast, and Spotify

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My commute to work every day is roughly one hour ( /- 15 minutes depending on the day). It's safe to say I cruise through A LOT of podcasts. The subjects I listen to range from True Crime, NFL Fantasy Football, Major League Baseball, and Data Science. This is my personal ranking/list of the best data science podcasts on SoundCloud, Apple Podcast, and Spotify. I found the descriptions of each podcast to be pretty true to what I would have written myself, which is why you won't see a whole lot of my own writing in the descriptions (why reinvent the wheel?).


Remote NLP Engineer openings near you -Updated September 17, 2022 - Remote Tech Jobs

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Role requiring'No experience data provided' months of experience in Chicago Within 3's virtual engagement platform helps the top life science and pharmaceutical companies in the world to engage more efficiently with physicians and patients. Drugs get to market faster. Busy physicians can spend more time with patients. We work hard to create a dynamic and collaborative culture where innovation is encouraged. Even as a globally distributed organization, we strive to create and maintain connections that not only make work fun but make our work better!


iiot ai_2021-05-28_03-17-11.xlsx

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The graph represents a network of 1,148 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 28 May 2021 at 10:22 UTC. The requested start date was Friday, 28 May 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 1-hour, 36-minute period from Tuesday, 25 May 2021 at 22:22 UTC to Thursday, 27 May 2021 at 23:58 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


A Guide On How To Become A Data Scientist - (Step By Step Approach)

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In this article, we will see the skills that one must acquire to become a data scientist. You cannot learn data science within one year or six months -- instead, it's a lifetime process that you have to follow with proper dedication and hard work. There are tons of resources and links out there, but often we get confused on which resources to follow. I have got you covered. I have attached the links to several Youtube channels, blogs, courses, and other websites, which I found appropriate for a beginner.


No, You're Not Alone. Google Is Also Making This Big Mistake On AI

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Just this past month, an article was shared that showed that over 30% of the data used by Google for one of their shared machine learning models was mislabeled with the wrong data. Not only was the model itself full of errors, but the actual training data used by that model itself was full of mistakes. How could anyone using Google's model ever hope to trust the results if it's full of human-induced errors that computers can't fix. And Google isn't alone with major data mislabeling, an MIT study in 2021 found that almost 6% of the images in the industry-standard ImageNet database are mislabeled, and furthermore, found "label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets". How can we hope to trust or use these models if the data used to train those models is so bad?


Opportunities for Data Science Innovation in the Policing Sector

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According to Peter K. Manning, in Anglo-American societies, the purpose of the police is to "sustain politically defined order and ordering via tracking, surveillance, coercion and arrest" (2014: p.6). Consisting of several authoritatively coordinated and legitimate organizations (ibid.), the policing sector serves governments in protecting their communities, preventing crime and disorder, and ensuring justice (The Policy Circle, 2022). The police's position as acting in the communities' interest suggests that their functions are heavily dependent on public trust and societal consensus concerning social justice and fairness (Manning, 2014). While there are large numbers of police officers employed in Australia (67,200 in 2021), a number which is expected to increase in the future (Australian Industry and Skills Committee, 2022), Ransley & Mazerolle (2009) have argued that trends in public governance and regulation have caused the increased pluralization and privatisation of policing efforts. Nowadays, the policing sector thus constitutes a large network of private, public and welfare organizations geared at controlling and preventing crimes (ibid.). In this essay, I will thus focus on data science opportunities for a variety of stakeholders involved in ensuring public security and order.


Data Transformation and ML Models with Python - Views Coupon

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Buff your skills to keep your job and get a raise in ANY economic climate. This course BUNDLE keeps your skills sharp and your paycheque up! This masterclass is without a doubt the most comprehensive course available anywhere online. Even if you have zero experience, this course will take you from beginner to professional. Each certificate in this bundle is only awarded after you have completed every lecture of the course.