Calling sklearn or PyTorch APIs and using pretrained models seems easy, but digging in to develop a deeper understanding of how methods work, when to apply them, and how to push their limits can be daunting and confusing.
Having gone from research to industry, I have successfully guided interns, bachelor/master graduates, and Ph.D. students on their path through the field of machine learning from concepts to applications and back.
Currently, I work as co-founder and principal engineer at an early-stage machine learning start-up coming out of my colleagues and my academic research. I am also very happy to talk about product-market-fit, validation, business models, start-up fundraising and bootstrapping, and all the things related to the pre-seed/seed stage.
Prior to starting my company, I worked as a machine-learning research scientist bringing deep learning methods on tabular data to know-your-customer and anti-money-laundering products in financial institutions.
Before that, I completed my PhD where I developed machine-learning algorithms for reverse engineering software and applied them on software engineering and network analysis tasks.
Throughout my career, I've done commercial data science consulting projects on the side.
$240/month2 spots left!
Includes unlimited Q&A via chat
Tasks & exercises
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Expect responses in 2 days
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We will send you a quick email if Chris has new open spots for mentorship, and only in that case!