Introduction to Machine Learning with Python for Mineral Exploration

Introduction to Machine Learning with Python for Mineral Exploration

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We will be using Python to perform exploratory data analysis and Machine Learning on different geophysical data types.

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Sydney Informatics Hub for Pawsey Data Science Week

Introduction to Machine Learning with Python for Mineral Exploration

Synopsis: We will be using open-source Python libraries (scikit-learn, pandas, seaborn, matplotlib) to perform exploratory data analysis on different geophysical data types. We will then use predictive machine learning approaches for finding mineral deposits based on the different geophysical layers. The framework we will build can be expanded to high performance computing or cloud environments, and is versatile enough to be applied to many datasets from different domains with predictive applications.

Recommended pre-requisites: A fundamental grasp on Python will be beneficial for attendees, but we will step through the workflow in a simplified approach. You should be able to recognise a for-loop and if-else statements, know about importing modules, and understand the commands for how a simple plot is made in matplotlib. Completing The Carpentries Python course will be more than adequate: https://swcarpentry.github.io/python-novice-inflammation/

Domain knowledge of exploration or geoscience will be helpful and most relevant, but these workflows can be applied to any similar datasets.

Target audience: Students, Researchers, and Industry Professionals looking to learn more about Machine Learning in a Geoscience context.

Course notes (to be updated): https://sydney-informatics-hub.github.io/geopython-pawsey

For more information contact: nathaniel.butterworth@sydney.edu.au 

Data Science Week (DSW) aims to bring together a community of data scientists, technologists, educators and more to raise awareness around data science, network, and share ideas with like-minded peers. Check out and register for some of the great Data Science Week events at www.datascienceweek.org; from May 9-13, 2022.

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