Machine Learning as a Service – Anand Chitipothu

This talk is about creating a simple end interface for running your machine learning code. Anand is the co-founder of https://rorodata.com/ a Platform-as-a-Service, designed for data scientists, for running machine learning code. Machine learning libraries that were mentioned that I had not come across before: PyTorch – https://pytorch.org/ – a deep learning framework for fast, flexible experimentation. joblib – https://pypi.org/project/joblib/ – is a set […]

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Navigating the magical data visualisation forest

Speaker: Dr Margriet Groenendijk from IDM Margriet is a tech enthusiast at IBM and gave a lightning talk at the Django Bristol Bath meetup as well. Slides are online here: https://www.slideshare.net/PoleSystematicParisRegion/a-beginners-guide-to-weather-climate-data-margriet-groenendijk This talk is about using Jupyter notebooks, https://jupyter.org/, for data analysis and visualisations. NB: These can be run on the desktop and is available in the cloud as well. Libraries Margriet has […]

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Categorizing Tweets Using Machine Learning – Halide Bey

Code in this talk can be found here https://github.com/halidebey/PyCon2018 Speaker made use of this tool, Kaggle – https://www.kaggle.com/ – the place to do data science projects. Speaker refers to this source of data Data For Everyone Discussed the approaches to machine learning supervised/non supervised Ignoring parts of your dataset interesting Python libraries – pandas – https://pypi.org/project/pandas/ and nltk – https://pypi.org/project/nltk/ sklearn (superseded […]

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Is it Shakespeare?

Using Python for authorship attribution in Renaissance drama A lecturer, Paul Brown, and a student, Katie Jones, present their exploration of analysing old plays in an automated fashion. The period of interest there are at least a 1/3 of which the authorship is unknown. A source of early plays online is the Early Book Online tool, https://eebo.chadwyck.com/home Not common to use […]

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