Machine learning libraries you’d wish you’d known about
Diagnosing, explaining and scaling machine learning is hard. I’ll talk
about a set of libraries that have helped me to understand when and
how a model is failing, helped me communicate why it is working to
non-technical users, automated the search for better models and helped
me to scale my modeling.
I’ll discuss YellowBrick, LIME, ELI5, TPOT and Dask. These libraries
will make it more likely that you deliver trustworthy and reliable
systems that will actually make it past R&D and into Production. The
talk will be rooted in my experience delivering client projects and
participating in Kaggle competitions.
Principal Data Scientist, Modelinsight.io
PyDataLondon co-founder, co-chair for 2014-2016, international keynote speaker on data science, O’Reilly author, citizen of the world and London based. Python, ML, High Performance.