This talk will provide a brief introduction into scikit-learn and it's part in the machine learning ecosystem. It will also discuss recent additions to scikit-learn, such as better integration with pandas and better support for missing values and categorical data. We'll end some future directions, including better control over parallelization and better use of multi-core systems, as well as better tools for model inspection and model debugging.
Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O'Reilly book "Introduction to machine learning with Python", describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon.
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