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£600k Machine Learning collaboration to supercharge data-driven science

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A collaboration between the Norwich Bioscience Institutes and The Alan Turing Institute will enhance the ways machine learning and artificial intelligence are applied to life science research. With biological research becoming increasingly data rich, the collaboration will help identify new ways to exploit this wealth of information and accelerate advances in understanding. The Norwich Bioscience Institutes – including the Earlham Institute, the John Innes Centre, Quadram Institute and The Sainsbury Laboratory – have teamed up with The Alan Turing Institute in a £600,000 project to kickstart collaborations that will employ machine learning and artificial intelligence. Half of the funding comes from The Alan Turing Institute's'AI for Science and Government' Strategic Priorities Fund award, with the other half coming from a strategic award from the Biotechnology and Biological Sciences Research Council (UKRI-BBSRC).The funding will support up to six year-long research posts who will work together in a cross-institute cohort to expand the application of machine learning and artificial intelligence to several key areas, which may include: As technologies for capturing information – from DNA sequences through to high resolution images – become ever cheaper and more widely available, so do the reams of data associated with that. Making sense of huge datasets can create bottlenecks in research projects and, importantly, discoveries.


Ain't Not Such a Thing as a 'Citizen Data Scientist'

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Before you start using'low code' or'drag & drop' data science tools, please learn the fundamentals. Why aspire to be'Citizen Data Scientist' when you can truly become a'Data Scientist.' Don't get swayed by the fancy titles like'Citizen Data Scientist.' It is funny that so much hard selling is happening in data science. I mean, just because we know how to use a thermometer or operate BP machine, should we start calling ourselves'Citizen Doctor'?


Our Recommendations on Data Science Books -- Free and Paid

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Over the last decade, data science has become one of the most paid and highly reputed domains for professionals in the information technology field. Nowadays, data science applications have become inevitable for most (if not all) businesses. Hence, there is a surge of proficient data science professionals. Therefore, if you plan to move into this domain, you may find a wide variety of data-science-related books available online, which in turn, it can be an arduous task to pick out the most notable books to get into data science. This article aims to solve this conundrum by providing you with our editorial recommendations on the best and high-quality books for data science.


Why it is right time to pursue a career in AI, ML and Data Science

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Disruptive technologies– an umbrella term for technical disciplines that are currently said to transform the digital landscape. The spearheads of this transformation are artificial intelligence (AI), data science, and machine learning (ML). The best part is these technologies are also interrelated. In technical parlance, machine learning is a dynamic application of AI that empowers the machines to learn from data provided and improve the model accuracy levels. And data scientists mine data to extract insights and forecast future trends based on the data collected from machine learning or AI models.


TPOT for Automated Machine Learning in Python

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Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Genetic Programming stochastic global search procedure to efficiently discover a top-performing model pipeline for a given dataset. In this tutorial, you will discover how to use TPOT for AutoML with Scikit-Learn machine learning algorithms in Python. TPOT for Automated Machine Learning in Python Photo by Gwen, some rights reserved.


Top Data Science Influencers to Follow in 2020

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Social Media and information sharing is something every internet user will know about. The presence and popularity of Twitter, LinkedIn, and many other platforms have made it convenient to spread knowledge all around the globe in a couple of clicks. It is because of the extensive usage of these networking sites by various Thought leaders, achievers, and change-makers that Data Science and AI knowledge has spread across the globe. IPFC online has recently come up with a list of Top 50 Digital influencers to follow out of which we are going to talk about the ones concerned with Machine Learning and AI. Additionally, we have provided some more influencers worth following.


£600k Machine Learning collaboration to supercharge data-driven science

#artificialintelligence

As technologies for capturing information – from DNA sequences through to high resolution images – become ever cheaper and more widely available, so do the reams of data associated with that. Making sense of huge datasets can create bottlenecks in research projects and, importantly, discoveries.Machine learning offers a promising route into not only exploring that data, but also helping us to find hidden patterns and new hypotheses that we had never previously considered.The Alan Turing Institute believes that data science and artificial intelligence will change the world, and part of that vision includes their Data Science At Scale research programme. This aligns strongly with the mission of the Earlham Institute, who are contributing to solving global challenges by applying data driven methods, and host the UK National Capability in computational infrastructure."Data "Over the last ten years our ability to generate vast datasets has increased rapidly, and this collaboration will allow us to better use that information for public good. To work alongside The AlanTuring Institute, with all their expertise in machine learning, is fantastic for the future of UK life science research."Professor Jonathan Rowe, Programme Director of Data Science For Science at The Alan Turing Institute, said that "this is a significant new collaboration for the Turing which offers new opportunities for advancing data-driven science.


Our Recommendations on Data Science Books -- Free and Paid

#artificialintelligence

Over the last decade, data science has become one of the most paid and highly reputed domains for professionals in the information technology field. Nowadays, data science applications have become inevitable for most (if not all) businesses. Because of that, there is a surge of proficient data science professionals. Therefore, if you plan to move into this domain, you may find a wide variety of data-science-related books available online. And considering that, it can be an arduous task to pick out the most notable books to get into data science.


3 Free Certified Data Science Courses

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Picture this – you are given the opportunity to take a high-quality course on a data science or machine learning topic(s) free of cost. And as the icing on an already delicious offering, you will even get a certificate upon completing the course! So not only do you get to embellish your blossoming data science skillset, you get a certificate proof of your accomplishments. Sounds too good to be true? Well, Analytics Vidhya is making this a reality in 2020!


How artificial intelligence helps firms stay relevant amid pandemic

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COVID-19 has put technology at the heart of many companies. Consumer behavior has shifted dramatically over the past few months, and ever more transactions are taking place online. While the pandemic has brought financial and operational challenges to all markets, technology, especially artificial intelligence (AI), proves that growth is still possible during times of crisis. Unfortunately, despite being around for quite some time, there are often misconceptions about what AI can and cannot do. Indonesia, with its large population and a deep smartphone penetration, presents a huge opportunity for data intensive technology.