Machine learning the F# way
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Data science and machine learning are rapidly growing fields that focus on extracting insights from data. But what actually hides behind the buzz words? In this talk I'll use data science tools in F# to analyse live information from social media. You'll see how F# simplifies data processing tasks and seamlessly integrates different steps of the analysis, and you'll also learn about ideas behind interesting machine learning algorithms, including deep neural networks.
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Machine learning the F# way
Evelina Gabasova
Evelina is a Senior Research Data Scientist at The Alan Turing Institute, the UK's national centre for data science and artificial intelligence. She is passionate about making data science understandable and accessible to everyone. She originally started as a programmer but got interested in machine learning early on and did a mathematics PhD at the University of Cambridge. During her PhD, she worked on Bayesian models for unsupervised learning that integrate heterogeneous biomedical datasets. After that, she worked in cancer research at the MRC Cancer unit in Cambridge, where she focused on helping biologists analyse genomic data.
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SkillsCast
Please log in to watch this conference skillscast.
Data science and machine learning are rapidly growing fields that focus on extracting insights from data. But what actually hides behind the buzz words? In this talk I'll use data science tools in F# to analyse live information from social media. You'll see how F# simplifies data processing tasks and seamlessly integrates different steps of the analysis, and you'll also learn about ideas behind interesting machine learning algorithms, including deep neural networks.
YOU MAY ALSO LIKE:
Thanks to our sponsors
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About the Speaker
Machine learning the F# way
Evelina Gabasova
Evelina is a Senior Research Data Scientist at The Alan Turing Institute, the UK's national centre for data science and artificial intelligence. She is passionate about making data science understandable and accessible to everyone. She originally started as a programmer but got interested in machine learning early on and did a mathematics PhD at the University of Cambridge. During her PhD, she worked on Bayesian models for unsupervised learning that integrate heterogeneous biomedical datasets. After that, she worked in cancer research at the MRC Cancer unit in Cambridge, where she focused on helping biologists analyse genomic data.