I recently joined the Enterprise Insight Studio team at Accenture's global center for innovation in Dublin as an Artificial Intelligence (AI) Software Engineering.
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Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform a technical analysis of data. A basic visualization such as a bar chart might give you some high-level information, but with statistics, we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
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With the release of technologies such as TensorFlow and CloudVision, it's becoming easier to use machine learning (ML) in your mobile apps, but training machine learning models still require a significant amount of time and effort.
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Deirdre Dempsey has decades of experience dealing with data analytics, data science stemming from a strong foundation in mathematics.
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Computers are good with numbers, but not that much with textual data. One of the most widely used techniques to process textual data is TF-IDF in Python. In this article, we will learn how it works and what are its features.
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A team of researchers at The University of Texas at Austin has created CRN++, a new language for programming deterministic (mass-action) chemical kinetics in performing computations.
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This list contains free learning resources for data science, machine learning and big data related concepts, techniques, and applications. Inspired by Free Programming Books.
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Top 100 Data Science Resources & Tools For 2018 Whether you're just getting started in data science, or are gearing up to get a Masters in Data Science or Business Analytics, there's always more to learn. This guide gathers 2018's top data science resources on the web for learners of all stages.
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An illustrated guide to becoming a DevOps Engineer or Programming Language expert with links to relevant courses.
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Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary.
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Generators have been an important part of Python ever since they were introduced with PEP 255.
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Many teams try to start an applied Artificial Intelligence project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that's like raising a puppy in a New York City apartment for a few years, then being surprised that it can't herd sheep for you.
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In this post, I want to provide easy-to-understand definitions of deep learning and reinforcement learning so that you can understand the difference.
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Go is an open source programming language developed by the tech giant "Google". This language led to the creation of software in a very easy and efficient manner. Why?
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Machine learning is undoubtedly on the rise, slowly climbing into 'buzzword' territory. This is in large part due to misuse and a simple misunderstanding of the topics that come with the term.
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Nowadays, the real life of every business cannot be handled easily without computers. All organizations require computer installation, support, and maintenance.
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A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master's program using online resources.
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For the past year, we have compared nearly 8,800 open source Machine Learning projects to pick Top 30 (0.3% chance).
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