Jesper Dramsch - How to guarantee your machine learning model| PyData Global 2020

Slides:

Talk
Recently I had my PhD thesis rejected. As a failure, I am uniquely positioned to recognize failure. While I am unwaveringly enthusiastic about machine learning, I aim to share my insights into failed machine learning modelling from real-world examples in science and industry. This talk is for you if you have an introductory understanding of machine learning and would like to avoid common pitfalls.

Speaker
Jesper is a recovering geophysicist with a knack for coding, machine learning, and data science. During their PhD, Jesper focused on deep learning applied to geophysical data. During this time they finished 10 peer-reviewed papers in workshops, conferences, and journals. Being ever so distractable they also reaching top 81 worldwide in the Kaggle notebooks division and worked as a consultant teaching Python and machine learning in a few places. Recently they finished a book chapter, which is under review, titled “70 Years of Machine Learning in Geoscience in Review”. They were invited to rewrite the O’Reilly teaching material for Tensorflow 2 and have just published a data science with Python video course.


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[aashishmalhotra]

Hello rode your free e learning book . It's quite good . Can u extend that specifically in a way where u take a ML algo explain all of info about that plus with code snippets.

If can make videos it would be awesome. Can we expect more applied content relevant to which are being used in industry rather than more research oriented.