Machine learning (ML) is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed.
In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.
See how InterSystems IRIS Adaptive Analytics can be used to aggregate and query billions of records from a virtual cube, applying machine learning and analytics to that data.
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In it, Anton provides a great, high-level introduction to machine learning and shows why you don't need to be a "unicorn" data scientist to start using machine learning to your advantage!
When we have to predict the value of a categorical (or discrete) outcome we use logistic regression. I believe we use linear regression to also predict the value of an outcome given the input values.
Then, what is the difference between the two methodologies?
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DNA Similarity and Classification was developed as a REST API utilizing InterSystems Vector Search technology to investigate genetic similarities and efficiently classify DNA sequences. This is an application that utilizes artificial intelligence techniques, such as machine learning, enhanced by vector search capabilities, to classify genetic families and identify known similar DNAs from an unknown input DNA.
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Watch this video to learn about the PainChek artificial intelligence technology, which assesses patient pain at the hospital bedside, leverages InterSystems IRIS interoperability to connect to third-party electronic medical record systems:
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Interested in predictive modeling? This exercise shows how IntegratedML® works, and you can get practice creating a model with sample patient data. Try it out and share what you think!
In our latest episode of Data Points, I had a conversation with @Thomas Dyarabout AI Link, which helps bridge the gap between data scientists and business analysts. Our conversation talks about how AI Link fits with IntegratedML and Adaptive Analytics, as well, as what new features are on the horizon for IntegratedML. Take a listen!
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Looking to get started with generative AI? Try two brand-new learning paths. In Getting Started with Generative AI (2h 45m), learn the basics of interacting with GenAI. Then, try Developing Generative AI Applications (2h) to start developing your own GenAI application. Plus, earn badges for completing each path!
Some days ago, I've seen a youtuber talking about how to create a neural network (sorry, is in spanish)
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